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Financial Markets

Financial markets refers to the broad activity of buying and selling financial securities and derivatives, including bonds, equities, currencies, commodities, and other financial instruments. Changes in security valuations can affect the flow of capital through the economy, which can impact economic activity and have implications for monetary policy.

As part of its core responsibilities to inform U.S. monetary policy decisions, the SF Fed conducts extensive research on financial markets, financial conditions, and other related topics. This page features a collection of content on financial markets, including topics such as financial conditions, U.S. Treasury markets, securities and derivatives, and currencies and commodities.

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What Are Financial Markets?

• Understanding Financial Markets

Types of Financial Markets

Examples of financial markets, the bottom line, financial markets: role in the economy, importance, types, and examples.

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and behavioral finance. Adam received his master's in economics from The New School for Social Research and his Ph.D. from the University of Wisconsin-Madison in sociology. He is a CFA charterholder as well as holding FINRA Series 7, 55 & 63 licenses. He currently researches and teaches economic sociology and the social studies of finance at the Hebrew University in Jerusalem.

Financial markets refer broadly to any marketplace where securities trading occurs, including the stock market, bond market, forex market, and derivatives market. Financial markets are vital to the smooth operation of capitalist economies.

Key Takeaways

• Financial markets refer broadly to any marketplace where the trading of securities occurs.
• There are many kinds of financial markets, including (but not limited to) forex, money, stock, and bond markets.
• These markets may include assets or securities that are either listed on regulated exchanges or trade over-the-counter (OTC).
• Financial markets trade in all types of securities and are critical to the smooth operation of a capitalist society.
• When financial markets fail, economic disruption, including recession and rising unemployment, can result.

Investopedia / Theresa Chiechi

Understanding the Financial Markets

Financial markets play a vital role in facilitating the smooth operation of capitalist economies by allocating resources and creating liquidity for businesses and entrepreneurs. The markets make it easy for buyers and sellers to trade their financial holdings. Financial markets create securities products that provide a return for those with excess funds (investors/lenders) and make these funds available to those needing additional money (borrowers). 

The stock market is just one type of financial market. Financial markets are created when people buy and sell financial instruments, including equities, bonds, currencies, and derivatives. Financial markets rely heavily on informational transparency to ensure that the markets set prices that are efficient and appropriate.

Some financial markets are small with little activity, and others, like the  New York Stock Exchange (NYSE) , trade trillions of dollars in securities daily. The equities (stock) market is a financial market that enables investors to buy and sell shares of publicly traded companies. The primary stock market is where new issues of stocks are sold. Any subsequent trading of stocks occurs in the secondary market, where investors buy and sell securities they already own.

Prices of securities traded in the financial markets may not necessarily reflect their intrinsic value.

There are several different types of markets. Each one focuses on the types and classes of instruments available on it.

Stock Markets

Perhaps the most ubiquitous of financial markets are stock markets. These are venues where companies list their shares, which are bought and sold by traders and investors. Stock markets, or equities markets, are used by companies to raise capital and by investors to search for returns.

Stocks may be traded on listed exchanges, such as the New York Stock Exchange (NYSE), Nasdaq , or the over-the-counter (OTC) market. Most stock trading is done via regulated exchanges, which plays an important economic role because it is another way for money to flow through the economy.

Typical participants in a stock market include (both retail and institutional) investors, traders, market makers (MMs) , and specialists who maintain liquidity and provide two-sided markets. Brokers are third parties that facilitate trades between buyers and sellers but who do not take an actual position in a stock.

Over-the-Counter Markets

An over-the-counter (OTC) market is a decentralized market—meaning it does not have physical locations, and trading is conducted electronically—in which market participants trade securities directly (meaning without a broker). While OTC markets may handle trading in certain stocks (e.g., smaller or riskier companies that do not meet the listing criteria of exchanges), most stock trading is done via exchanges. Certain derivatives markets, however, are exclusively OTC, making up an essential segment of the financial markets. Broadly speaking, OTC markets and the transactions that occur in them are far less regulated, less liquid, and more opaque.

Bond Markets

A bond is a security in which an investor loans money for a defined period at a pre-established interest rate. You may think of a bond as an agreement between the lender and borrower containing the loan's details and its payments. Bonds are issued by corporations as well as by municipalities, states, and sovereign governments to finance projects and operations. For example, the bond market sells securities such as notes and bills issued by the United States Treasury. The bond market is also called the debt, credit, or fixed-income market.

Money Markets

Typically, the money markets trade in products with highly liquid short-term maturities (less than one year) and are characterized by a high degree of safety and a relatively lower interest return than other markets.

At the wholesale level, the money markets involve large-volume trades between institutions and traders. At the retail level, they include money market mutual funds bought by individual investors and money market accounts opened by bank customers. Individuals may also invest in the money markets by purchasing short-term certificates of deposit (CDs) ,  municipal notes , or U.S. Treasury bills, among other examples.

Derivatives Markets

A derivative is a contract between two or more parties whose value is based on an agreed-upon underlying financial asset (like a security) or set of assets (like an index). Rather than trading stocks directly, a derivatives market trades in futures and options contracts and other advanced financial products that derive their value from underlying instruments like bonds, commodities, currencies, interest rates, market indexes, and stocks.

Futures markets are where futures contracts are listed and traded. Unlike forwards, which trade OTC, futures markets utilize standardized contract specifications, are well-regulated, and use clearinghouses to settle and confirm trades. Options markets, such as the Chicago Board Options Exchange (Cboe) , similarly list and regulate options contracts. Both futures and options exchanges may list contracts on various asset classes, such as equities, fixed-income securities, commodities, and so on.

Forex Market

The forex (foreign exchange) market is where participants can buy, sell, hedge, and speculate on the exchange rates between currency pairs . The forex market is the most liquid market in the world, as cash is the most liquid of assets. The currency market handles more than $7.5 trillion in daily transactions, more than the futures and equity markets combined.

As with the OTC markets, the forex market is also decentralized and consists of a global network of computers and brokers worldwide. The forex market is made up of banks, commercial companies, central banks, investment management firms, hedge funds, and retail forex brokers and investors.

Commodities Markets

Commodities markets are venues where producers and consumers meet to exchange physical commodities such as agricultural products (e.g., corn, livestock, soybeans), energy products (oil, gas, carbon credits), precious metals (gold, silver, platinum), or "soft" commodities (such as cotton, coffee, and sugar). These are known as spot commodity markets, where physical goods are exchanged for money.

However, the bulk of trading in these commodities takes place on derivatives markets that utilize spot commodities as the underlying assets. Forwards, futures, and options on commodities are exchanged both OTC and on listed exchanges around the world, such as the Chicago Mercantile Exchange (CME) and the  Intercontinental Exchange (ICE) .

Cryptocurrency Markets

Thousands of cryptocurrency tokens are available and traded globally across a patchwork of independent online crypto exchanges . These exchanges host digital wallets for traders to swap one cryptocurrency for another or for fiat monies such as dollars or euros.

Because most crypto exchanges are centralized platforms, users are susceptible to hacks or fraudulent activity. Decentralized exchanges are also available that operate without any central authority. These exchanges allow direct peer-to-peer (P2P) trading without an actual exchange authority to facilitate the transactions. Futures and options trading are also available on major cryptocurrencies.

The above sections make clear that the "financial markets" are broad in scope and scale. To give two more concrete examples, we will consider the role of stock markets in bringing a company to IPO and the role of the OTC derivatives market in the 2008-09 financial crisis.

Stock Markets and IPOs

As a company establishes itself over time and grows, it needs access to additional capital. It will often find itself in need of much larger amounts of capital than it can get from ongoing operations, traditional bank loans, or venture and angel funding. Firms can raise the amount of capital they need by selling shares of itself to the public through an initial public offering (IPO). This changes the company's status from a "private" firm whose shares are held by a few shareholders to a publicly traded company whose shares will be subsequently held by public investors.

The IPO also offers early investors in the company an opportunity to cash out part of their stake, often reaping very handsome rewards in the process. Initially, the underwriters usually set the IPO price through their pre-marketing process.

Once the company's shares are listed on a  stock exchange , and trading commences, the price of these shares will fluctuate as investors and traders assess and reassess their intrinsic value and the supply and demand for those shares at any given moment.

OTC Derivatives and the 2008 Financial Crisis: MBS and CDOs

While the 2008-09 financial crisis was caused and made worse by several factors, one factor that has been widely identified is the market for mortgage-backed securities (MBS) . These are OTC derivatives where cash flows from individual mortgages are bundled, sliced up, and sold to investors. The crisis resulted from a sequence of events, each with its own trigger—these events culminated in the banking system's near-collapse. It has been argued that the seeds of the crisis were sown as far back as the 1970s with the Community Development Act, which required banks to loosen their credit requirements for lower-income consumers, creating a market for  subprime mortgages .

The amount of subprime mortgage debt guaranteed by  Freddie Mac  and  Fannie Mae continued to expand into the early 2000s when the Federal Reserve Board began to cut interest rates drastically to avoid a recession. The combination of loose credit requirements and cheap money spurred a housing boom, which drove speculation, pushing up housing prices and creating a real estate bubble. In the meantime, the investment banks, looking for easy profits in the wake of the  dotcom bust  and the 2001 recession, created a type of MBS called  collateralized debt obligations (CDOs) from the mortgages purchased on the secondary market.

Because subprime mortgages were bundled with prime mortgages, there was no way for investors to understand the risks associated with the product. When the market for CDOs began to heat up, the housing bubble that had been building for several years finally burst. As housing prices fell, subprime borrowers began to default on loans that were worth more than their homes, accelerating the decline in prices.

When investors realized the MBS and CDOs were worthless due to the toxic debt they represented, they attempted to unload the obligations. However, there was no market for the CDOs. The subsequent cascade of subprime lender failures created liquidity  contagion  that reached the upper tiers of the banking system. Two major investment banks, Lehman Brothers and Bear Stearns, collapsed under the weight of their exposure to subprime debt, and more than 450 banks failed over the next five years. Several major banks were on the brink of failure and were rescued by a taxpayer-funded bailout.

What Are the Different Types of Financial Markets?

Some examples of financial markets and their roles include the stock market, the bond market, forex, commodities, and the real estate market, among others. Financial markets can also be broken down into capital markets, money markets, primary vs. secondary markets, and listed vs. OTC markets.

How Do Financial Markets Work?

Despite covering many different asset classes and having various structures and regulations, all financial markets work essentially by bringing together buyers and sellers in some asset or contract and allowing them to trade with one another. This is often done through an auction or price-discovery mechanism.

What Are the Main Functions of Financial Markets?

Financial markets exist for several reasons, but the most fundamental function is to allow for the efficient allocation of capital and assets in a financial economy. By allowing a free market for the flow of capital, financial obligations, and money, the financial markets make the global economy run more smoothly while allowing investors to participate in capital gains over time.

Financial markets provide liquidity, capital, and participation that are essential for economic growth and stability. Without financial markets, capital could not be allocated efficiently, and economic activity such as commerce and trade, investments, and growth opportunities would be greatly diminished.

Many players make markets an essential part of the economy—firms use stock and bond markets to raise capital from investors. Speculators look to various asset classes to make directional bets on future prices. At the same time, hedgers use derivatives markets to mitigate various risks, and arbitrageurs seek to take advantage of mispricings or anomalies observed across various markets. Brokers often act as mediators that bring buyers and sellers together, earning a commission or fee for their services.

Compare Forex Brokers. " Forex Trading Statistics ."

Federal Deposit Insurance Corporation. " Origins of the Crisis ," Page 1-6.

Federal Reserve Bank of St. Louis. " Federal Funds Effective Rate (FEDFUNDS) ."

Federal Deposit Insurance Corporation. " Bank Failures in Brief – Summary 2001 Through 2022 ."

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• How To Conduct Financial Market Research Like a Pro

How To Conduct Financial Market Research Like A Pro

Financial services providers need to integrate financial market research into their business strategy. This research supplies providers in this vertical with meaningful insights on their target market, market products, and services.

Moreover, this data grants and gives financial services providers a framework for decision-making. As these institutions have to deal with money, there’s a lot at stake for banks and money lending companies. 

In 2019 alone, the global financial services industry spent an estimated $50 billion extracting raw data to support their trading activities and transactions. 

This is because financial market research data enables them to forecast and analyze any trends or aspects of the organization that needs changes. Not only this, market research offers opportunities for investments that increase the profits for an organization.

Even in 2020, when the pandemic brought the entire world to a standstill and disrupted the global economy, financial market research helped finance leaders and the financial services industry to support the economy’s financial recovery. 

The Rising Importance Of Financial Services Industry

As aforementioned, the financial services industry is the main driver of a country’s economy, mainly dominated by large organizations. With a strong financial services sector, the nation’s economy grows, and the consumers earn more, thereby increasing their purchasing power.

The uncertainty brought about by the global pandemic means that potentially every company and organization is likely to make smart and bold financial decisions. Unlike the financial crisis of 2008, the 2020 pandemic has disrupted all aspects of the economy. This means that finance leaders and the financial services industry are strategically positioned to support the economy’s financial recovery. 

With the recent changes in technologies, the rise of new investment trends, and the significant shift in consumer behavior, financial services companies are forced to tap into valuable information to approach these changes. 

Moreover, companies need to leverage relevant data and insights to make the most of these opportunities to keep up with the recent digitization and innovation in the financial services industry. 

This means that market research is as crucial for the finance sector as any other industry. 

The Importance of Conducting Financial Market Research 

Financial markets play a crucial role in driving a country’s economy and offering countless opportunities for investors to tap into specific markets and services. But with changing regulatory demands and consumer requirements, financial services companies and organizations need unique insights to keep up with this changing industry.

Financial market research is essential because it can help break down market data and trends into a broader context that offers finance companies a clear perspective of the risks and benefits of a particular service.

Therefore, several financial services providers invest in data analytics and market research to gain valuable information about their customers. 

However, conducting accurate research is not easy. Lenders, bankers, real estate brokers, and all other kinds of financial services providers need to know precisely what kind of information will help them in decision making. 

For commercial banks and investment companies, market research is essential as it determines which business proposition can benefit them in the long run. Moreover, it helps brokerage firms assess which products are in demand by their customer base so that they can suggest them based on the individual requirements of their customers.

The following expounds on  why a financial market analysis is important for banks and other businesses for their decision-making process:

Reduced Business Risks

When it comes to investing money, businesses need to know the right time to invest. With financial market research, they can predict the value of their investment to reduce business risks. Developing a financial plan that outlines all the risks and benefits that a firm may incur can give financial services providers an idea of which opportunities to invest in. 

Understanding these trends is also important so firms know exactly how to respond to market changes. Some frequent research areas for financial services providers include: 

• Business Banking 
• Personal Loans  
• Property Management

Effective Forecasting And Analysis

Accurate financial forecasting is crucial for financial service providers to strategize in the face of any uncertainties that may affect their business. Thus, financial market research equips businesses and institutions in their strategic planning process . 

This kind of research also provides deep insights into customer behavior and market shifts due to external factors and variations in market trends. With thorough finance market research, supported with effective survey campaigns , businesses can tap into what their customers are doing to offer something unique that sets them apart in the industry.

 It enables financial service institutions to set realistic and feasible goals and helps them predict their annual budgets. 

Accurate Demographic Targeting

To know their ideal demographic, businesses need effective financial market research to assess their target market and what they require from their business. This kind of research is also useful for understanding the distribution of consumer spending over a particular period and how customers feel about their financial situations. 

Financial service providers should inquire of their target market as such : are its members willing to make financial deals with your business at this time of crisis? How have their spending and saving plans changed?

Adequate financial research will delve into all these behaviors and trends at length to help institutions make better decisions. It also provides insights into demographic spending trends, where customers look for financial guidance and receptivity to media. 

Internal Audit Assistance

Businesses can evaluate the trading activities, existing credits, and regulatory reports for a successful internal audit by assessing financial market conduct.

The interconnectedness within the financial system has made it necessary for institutions to analyze their vulnerability to systemic risk s by assessing and analyzing macro-economic factors, industry trends, changes in regulations, risks materializing at other entities, and innovation by peers.

Carrying out an internal audit for a company offers a detailed report on the market’s existing and predicted financial risks . The interconnectedness of different industries and systematic risks in the economy brought about by the pandemic have made it imperative for financial services providers to put forward a dynamic audit plan. And the only way an audit plan will be successful in pointing out a company’s vulnerabilities is with adequate financial market research . 

How To Conduct  Financial Market Research

The following are two foremost methods for conducting financial market research:

Primary market research methods for the finance industry

Primary market research for the finance industry involves the direct participation of financial services providers in the research. It offers valuable data on different market areas that providers may require, as obtaining firsthand data provides them with unique insights , particularly their study. This is of utmost importance as secondary research providers do not provide data into the particular inquiries and topic s of study that a financial services provider seeks. 

Some of the methods to conduct primary market research for the finance industry include:

• Interviews – Financial services providers can hold interviews with industry participants to ask questions that require deep understanding. This is important to assess the changing preferences of clients for different financial services
• Observations – This is a qualitative, no-interaction technique used by financial advisors that can help them gain useful insights regarding the practices followed by finance companies, their competitors, and customer trends
• Email surveys – Financial advisors can contact industry participants and get feedback through a survey that includes short questions. These responses can then be analyzed and compiled as a report to understand market trends
• Online Surveys – They prove to be an essential tool within market research techniques as they collect data and insights on the exact amount of respondents to use in a survey pool. Online surveys can also support a company’s decision-making processes and offer them a competitive advantage through fast quality data. By accessing the right platform for generating online surveys, the financial services providers access qualitative and quantitative surveys.

Secondary market research methods for the finance industry

Secondary market research for the finance industry relies on data and information that researchers extract, meaning that the data has already been conducted by a third party and made available. 

Some of the methods to conduct secondary market research include:

• Industry Reports – Industry reports identify any opportunities or risks that the industry might face and present scenarios from the past that can help financial advisors deal with threats in a better way 
• Case Studies – Case studies illustrate how industries dealt with financial crises in the past and can provide a detailed, in-depth investigation into a complex situation. They are perfect for providing you with real-life examples of industries and actionable insights
• Statistics sites – Statistical research can help you decide which data collection methods would garner maximum results, what decisions to make, and how to predict behavioral response based on past statistical reports
• Research papers – Research papers provide in-depth knowledge on a particular topic that helps financial services providers understand and make better decisions
• Research agencies – Research agencies know all the proper tactics for conducting market research. They can help financial advisors with information such as client preferences, the right target market, predicting future financial conditions, etc

However, the type of research method financial advisors choose depends highly on time and available data. Qualitative market research like interviews and participant observation offers detailed, rich information and takes some time to collect. On the other hand, surveys and online feedback are easy to collect and lack necessary details. 

Secondary Market Research For The Finance Industry

The following is the list of the best secondary market resources on important data and insights on the finance industry.

• Business Insider – Financial Services Industry Overview  

It offers a general overview of the financial services industry, from money management to digital banking technologies. Also, it outlines all the major trends, topics, and behaviors needed for companies to climb up the ladder in the financial sector. 

• MarketResearch. Com – Financial Market Research Reports and Industry Analysis

Includes reports on the latest financial market research and provides analytical data on different financial subjects such as insurance management and consumer spending/saving patterns.

• IBIS World – Finance and Insurance Industry in the US

Compiled by IBIS World, this website is a massive database of thousands of industries. It presents useful information on economic changes, demographic data and helps organizations make better financial decisions. 

• MarketsAndMarkets – Banking and Financial Services Market Research Reports and Consulting

This website provides press releases, publications, and reports on banking and financial services. Given the devastating impact of the pandemic on the financial sector, the reports on MarketsAndMarkets also offer strategic, tactical advice on using financial services to help companies recover from their losses. 

• The Business Research Company – Financial Services Global Market report

The Business Research Company is the most authentic report on the internet that provides detailed reports on different industries in the market. It presents past trends and how markets have changed in the past decade and predicts statistical information on financial services providers. 

Financial market research enables the finance industry to gain meaningful insights into different products and services. This increases sales opportunities and offers businesses all the information they need to design effective financial strategies. 

Carrying Out Effective Research in the Financial Sector  

Online surveys are the most convenient method for collecting data owing to their accessibility and accuracy. This is why financial services providers need to leverage the power of an online survey platform or market research experts so that they can target a vast audience base and generate reliable survey responses.

To improve data quality, advanced online surveys leverage machine-learning to highlight and eliminate poor quality responses. AI in market research reduces the chances of error and eliminates duplicate entries of data that might result in outdated information and poor data quality. 

These online surveys are then used in random device engagement sampling (RDE)to engage a huge customer base on devices they are already using. Whether these surveys are posted on mobile apps or gaming interfaces, they are placed where the business’s audience can easily respond to them. This way, they can tap into unique and high-quality market research data that drives their decision-making process. 

Frequently asked questions

What is financial market research.

Financial market research offers useful insights into financial trends and strategies and gives statistical information on the leading finance companies. It also provides actionable insights regarding various financial instruments like portfolio pricing, risk management, etc.

Why do businesses need to conduct financial market research?

Financial market research minimizes the risk for financial services providers and identifies new business opportunities for them. It also gives them insights into market trends, identifies problem areas in the market and untapped resources.

How can an online survey platform help businesses with financial market research?

Online surveys are a reliable way of getting insights directly from consumers. They are also affordable and require minimal to no investment.

What are some of the resources to find secondary research on the finance market?

Secondary market research sources include the web, media sources, industry reports, case studies, press releases, publications, and company compiled data.

Do businesses need a financial advisor?

Even though many business owners choose to conduct their research, financial advisors can offer a solid, strategic plan that takes all their previous, present, and future investments into account and provide them with the most feasible plan. Moreover, financial advisors are experienced in their job and know a lot more about financial market trends than business owners that can help in effective decision making.

Do you want to distribute your survey? Pollfish offers you access to millions of targeted consumers to get survey responses from $0.95 per complete. Launch your survey today.

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The Financial Markets Group analyzes public policy issues in financial markets from multiple perspectives. The group applies legal, market practitioner, technological and other expertise to study challenges and risks in financial markets and infrastructures.

By communicating its findings to policymakers, regulators, industry leaders and the public, primarily through published work and conferences, the group contributes to the Federal Reserve’s mission of fostering the stability, integrity, and efficiency of the nation’s monetary, financial, and payment systems. The group has particular expertise in the derivatives markets and clearinghouses in which Chicago is a global leader.

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Our staff frequently publishes articles, working papers, conference agendas, and more. You can view them all below, or use the dropdown to select a specific category.

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UK Pension Market Stress in 2022—Why It Happened and Implications for the U.S.

June 2023 by Ketan B. Patel and Santiago I. Sordo Palacios

A steep increase in British sovereign yields and swap rates and an equally steep drop in the value of the British pound (GBP) in September 2022 put substantial liquidity pressures on United Kingdom pension funds. This repricing in risk assets was triggered by the UK chancellor’s mini-budget announcement on September 23, 2022, which led to reactions from market participants. The structure and investment strategies of pension funds made them particularly ill-prepared to deal with market turmoil.

What Does the CDS Market Imply for a U.S. Default?

April 2023 by Luca Benzoni, Christian Cabanilla, Alessandro Cocco, and Cullen Kavoussi

As the debt ceiling episode unfolds, we highlight a sharp increase in trading activity and liquidity in the U.S. credit default swaps (CDS) market, as well as a spike in U.S. CDS premiums. Compared with the periods leading up to the 2011 and 2013 debt ceiling episodes, we show that elevated CDS spreads in the current environment are partially explained by the cheapening of deliverable Treasury collateral to CDS contracts. We infer the likelihood of a U.S. default from these CDS premiums, and estimate an increase in the market-implied default probability from about 0.3–0.4% in 2022, to around 4% in April 2023, which is lower than it was in July 2011 and about where it was in October 2013. Finally, we document changes in Treasury bills trading activity as market participant update their expectations for a U.S. default.

2022 Financial Markets Group Fall Conference–Recap

February 6, 2023 by Nahimoy Alvarez, Nomaan Chandiwalla and Alessandro Cocco

This blog post summarizes the annual Fall Conference of the Financial Markets Group at the Federal Reserve Bank of Chicago, held on November 16, 2022. The conference featured Commissioners Kristin N. Johnson and Christy Goldsmith Romero from the U.S. Commodity Futures Trading Commission, Lamont Black, professor of finance at DePaul University and former Federal Reserve Board economist, and Klaus Löber from the European Securities and Markets Authority. This year’s event took place a week after the collapse of the cryptocurrency exchange FTX, providing a timely opportunity for discussion of the potential spillover risks into traditional finance, existing opportunities and risks in digital assets, and key regulatory tools in place or under consideration to address systemic risks and strengthen consumer protections going forward.

Distributed Ledger Technology, Carbon Accounting, and Emissions Trading

November 2022 by Alessandro Cocco, Jesse Leigh Maniff, David Rodziewicz, Michael Warner

The reduction of carbon emissions is a critical part of the transition to a more sustainable economy. Reducing carbon emissions is expected to lead to fewer natural disasters, lower energy transitions risk, and a lower impact on financial risks resulting from physical damage caused by climate change.

The Impact of Covid-19 Related Policy Responses on Municipal Debt Markets

September 2021, Revised August 2022 by Robert Bernhardt, Stefania D’Amico, Santiago I. Sordo Palacios

Municipal (muni) bonds are an important source of funding for state and local governments. During the Covid-19 pandemic, muni debt markets became severely distressed. In response, the Federal Reserve established the Municipal Liquidity Facility (MLF). Meanwhile, Congress enacted extensive fiscal measures that included direct aid to cities and states. To understand whether and how these policies worked, we employ a state-level regression model to estimate the relative efficacy of monetary and fiscal policy interventions for the term structure of muni-Treasury yield spreads. We find that fiscal and monetary policy together reduced those spreads by as much as 225 basis points. Fiscal policy contributed at least twice as much as monetary policy to the notable decline in shorter-term muni-Treasury spreads. At longer maturities, the contribution of fiscal policy was at least three times as large as that of monetary policy, suggesting that it addressed fundamental credit concerns.

The Misleading Notion of Notionals—Why Market Value Might Be a More Meaningful Measure in the Treasury Futures Market

June 2022 by Ketan B. Patel

The world’s communities and economies are already feeling significant effects from global warming and related climate and extreme weather events, as the latest United Nations Intergovernmental Panel on Climate Change (IPCC) world climate report published in August 2021 makes clear. In some industry sectors, such as insurance and energy, financial market tools have been developed specifically to mitigate the risk of financial loss related to climate. Such tools have yet to be developed for the U.S. mortgage market—one of the world’s largest at roughly $11 trillion as of the end of 2020.

The Customer Settlement Risk Externality at US Securities Central Counterparties

May 27, 2022 by Sam Schulhofer-Wohl

The architecture of securities clearing and settlement in the United States creates an externality: investors do not always bear the full cost of settlement risk for their trades and can impose some of this cost on the brokerages where they are customers. When markets are volatile and settlement risk is high, this externality can result in too much or too little trading relative to the efficient level, because investors ignore trading costs, while brokerages may refuse to allow investors to trade. Both effects were evident during the volatility in GameStop stock in 2021. Alternative approaches for clearing customer trades that are used in derivatives markets would eliminate the externality. We examine the potential benefits and costs of different approaches for clearing customer securities trades.

CFTC Roundtable Discussion on Non-intermediation

May 25, 2022

Robert Steigerwald, Senior Policy Advisor, Federal Reserve Bank of Chicago, moderated a roundtable discussion hosted by the Commodity Futures Trading Commission (CFTC) that debated the impact a non-intermediation model could have on derivatives clearing organizations (DCOs), and on derivatives trading and clearing overall. Following are some of the participants who participated in this roundtable discussion: DCOs, future commission merchants (FCMs), FCM customers, end-users academics, proprietary traders, and others.

Market Risk in UST Securities and Futures: How Much Did Volatility Increase in March of 2020 Through the Lens of Filtered Historical Simulation Value-at-Risk Models?

May 2022 by Ketan B. Patel

Market volatility increased substantially in March of 2020 as financial market participants reacted to the risk of Covid-19. Even the market for U.S. Treasury securities, long considered a safe haven and one of the most liquid debt instruments in the world, experienced large swings in volatility. In this policy discussion paper, I demonstrate that as volatility increased, model estimates of the market risk increased as well.

What Will It Take to Remove All Lead Service Lines? Common Barriers to Getting the Lead Out of Drinking Water

May 2022 by Cindy Hull, Nathan Anderson

Despite evidence that there is no safe amount of lead exposure in drinking water, there’s still work to be done to replace lead service lines (LSLs) in many American communities. What holds back progress, and what will it take to remove all LSLs? Staff at the Chicago Fed found two common barriers: the split ownership structure of LSLs and the resulting coordination challenges, and a lack of clear financial benefits for those who have the power to replace LSLs. Cities with full LSL replacement programs (Flint, MI, Benton Harbor, MI, Madison, WI, Galesburg, IL, Newark, NJ) offer lessons on how to overcome these barriers and enable a faster pace of replacement.

Fourth Annual Joint Conference of the Deutsche Bundesbank, European Central Bank and Federal Reserve Bank of Chicago on CCP Risk Management

March 22, 2022

On 22 March 2022 the European Central Bank (ECB) hosted the fourth annual joint conference of the Deutsche Bundesbank, the ECB and the Federal Reserve Bank of Chicago on CCP Risk Management. In light of the ongoing Covid-19 pandemic, this year’s conference was held virtually. The event, by invitation only and held under Chatham House Rules, brought together participants from industry, regulatory bodies and academia to discuss expanding access to central clearing and climate change risk in financial markets. The program and relevant recordings are linked in this summary.

Getting the Lead Out: New Opportunities and Challenges to Scale Up Lead Service Line Replacement

February 2022 by Nathan Anderson, Cindy Hull, Steven Kuehl, Suchi Saxena

An estimated to 12 million lead service lines (LSLs) supply water to homes in the United States, potentially exposing people to lead and endangering their health. For children, the consequences are even worse. Although the only way to eliminate lead exposure from LSLs is to replace them with new pipes, replacement has been slow, sporadic, and typically occurred in response to a lead-contamination crisis. New funding and recent changes to laws may spur proactive rapid replacement, presenting new economic and financial challenges. Given the critical nature of this issue, especially in the Upper Midwest, staff at the Federal Reserve Bank of Chicago are working to advance understanding, as well as analyze potential solutions.

Eighth Annual Central Counterparties Risk Management Conference—A Summary

January 31, 2022 by Nomaan Chandiwalla

On October 19, 2021, the Financial Markets Group of the Federal Reserve Bank of Chicago hosted its eighth annual Central Counterparties (CCP) Risk Management conference. The event featured a panel of experts discussing the U.S. Treasury market structure and a fireside chat with Commissioner Allison Herren Lee of the U.S. Securities and Exchange Commission (SEC). This blog post summarizes the discussions.

Bank Exposure to Commercial Real Estate and the Covid-19 Pandemic

November 2021 by Kyle Binder, Emily Greenwald, Sam Schulhofer-Wohl, Alejandro H. Drexler

The Covid-19 pandemic had an immediate and substantial impact on the commercial real estate (CRE) market—emptying workplaces, shopping centers, and hotels, thus affecting the cash flows of businesses occupying commercial space and in turn the ability of commercial space owners to meet their debt obligations.

Managing Climate Risk in Mortgage Markets: A Role for Derivatives

October 2021 by Ketan B. Patel

Fireside Chat with SEC Commissioner Allison Herren Lee and Maggie Sklar

October 2021

Securities and Exchange Commissioner Allison Herren Lee shares her views on a wide-ranging set of market structure issues with Maggie Sklar, Senior Policy Advisor and Director of International Engagement, Federal Reserve Bank of Chicago.

What’s the Potential Impact of Force Majeure Claims on Financial Stability?

September 2021 by Alessandro Cocco

This article examines the potential aggregate impact on financial stability of several bilateral force majeure claims filed at approximately the same time in one or more markets. One and a half years after the pandemic started, I take stock of the developments involving force majeure claims thus far, and conclude that the likelihood of these claims creating a systemic threat to financial stability is low.

Is a Treasury Clearing Mandate the Path to Increased Central Clearing?

June 2021 by Marta Chaffee and Sam Schulhofer-Wohl

Following stresses in the market for U.S. Treasury securities in March 2020, several observers suggested that increased central clearing of Treasury transactions could help the market function better and called for investigating the costs and benefits of a clearing mandate. This post argues that the effectiveness of a clearing mandate is unclear absent broad changes in the design of the Treasury market. Additionally, the changes in market design necessary to facilitate a clearing mandate could increase clearing even without a mandate. Analysis of the costs and benefits of expanded clearing therefore needs to consider the specific costs and benefits of these broader changes.

Clearing: Perspectives on Industry Developments and Learnings From the Covid Crisis

June 2021 by Ketan B. Patel

LaSalle Street hosts a discussion with executives representing exchanges, swap dealers, clearinghouses and asset managers on the impact of the Covid crisis and what to watch for as the pandemic eases.

Third Annual Joint Conference of the Deutsche Bundesbank, European Central Bank and Federal Reserve Bank of Chicago on CCP Risk Management

Financial leaders from around the world gathered for the Third Annual Joint Conference on CCP Risk Management organized by the Deutsche Bundesbank, the European Central Bank, and the Federal Reserve Bank of Chicago. Over the course of the day, speeches, fireside chats, and panel debates grappled with the pressing issues facing the financial sector. They addressed the industry’s response to COVID-19, climate change risk in financial markets, and ongoing structural changes in clearing.

Externalities in Securities Clearing and Settlement: Should Securities CCPs Clear Trades for Everyone?

March 2021 by Sam Schulhofer-Wohl

The architecture of securities clearing and settlement in the United States creates an externality: Investors do not always bear the full cost of settlement risk for their trades and can impose some of these costs on the brokerages where they are customers. When markets are volatile and settlement risk is high, this externality can result in too much or too little trading relative to the efficient level, because investors ignore trading costs but brokerages may refuse to allow investors to trade. Both effects were evident during the recent volatility in GameStop stock. Alternative approaches for clearing customer trades that are used in derivatives markets would eliminate the externality. I examine the potential benefits and costs of different approaches for clearing customer securities trades as well as implications for the U.S. Treasury market, where there have been calls to investigate the costs and benefits of expanded clearing of customer trades, and the relationship to faster equities settlement.

“YOLOing the Market”: Market Manipulation? Implications for Markets and Financial Stability

March 2021 by Maggie Sklar

Since the start of the Covid-19 pandemic in 2020, retail investors have increasingly participated at higher rates in the U.S. equities markets, particularly in day trading and short-term trading. In January 2021, amid a surge of online postings and interest by retail investors who use free trading apps, GameStop stock began moving up and down by billions of dollars a day—resulting in big gains for some investors and billions in losses for others. To the extent the proliferation of free trading democratizes the market, increases the diversity of participants able to participate in the market, and contributes to vibrant and healthy markets, this activity has positive benefits. These recent developments pose new questions for policymakers, such as whether the ability for users to gather together on social media and move the price of a financial product—for reasons unrelated to market news or market fundamentals—is a larger vulnerability, whether this activity fits into tools to prevent or stop market manipulation or not, and if there is a gap in regulatory ways to address such activity.

Climate Change Risk in Financial Markets: Advancing the Conversation in 2021

January 2021

The Federal Reserve Bank of Chicago’s Financial Markets Group opened 2021 with a conversation on climate change risk in financial markets. Panelists representing diverse perspectives discussed the following: How has climate change risk impacted global financial markets to date, and what can we expect moving forward? What are some of the key developments to watch out for—in terms of technology, investing practices, new guidance, etc.? What role can financial markets and the public sector play in better managing this risk moving forward? What are some of the strengths of and opportunities for existing efforts?

Treasury Market Structure Podcast

January 2021 By Alessandro Cocco and Sam Schulhofer-Wohl

In this episode, experts from industry, academia, and government discuss the operations of and recent stresses in the U.S. Treasury market. This market has a huge impact across the financial system—from determining the borrowing costs for governments to serving as a key benchmark within the financial system to helping to keep credit flowing to people who need it.

Can Central Counterparties (CCPs) Use Improved Buffers to Reduce Cyclical Funding Demands on the Market?

November 2020 By Ketan B. Patel

In this blog post, Ketan B. Patel investigates changes in margin at central counterparties (CCPs) during the market stress of 2020:Q1 to show how reactive CCP funding demands were to the increase in market volatility. He examines whether rainy day funding in the form of buffers might be useful in supporting the broader resilience of the clearing ecosystem.

A New Framework for Assessing Climate Change Risk in Financial Markets

November 2020 By Nahiomy Alvarez, Alessandro Cocco, and Ketan B. Patel

While there is growing recognition that climate change poses a new risk for the economy, more research is needed to understand how climate change risk affects global financial markets. We establish a new framework for this research by merging the climate change risk categories of physical risk, transition risk, and liability risk with the risk categories commonly assessed in the financial markets: market risk, credit risk, liquidity risk, and operational risk. We then factor in market structure and market regulation as we seek to assess the overall impact of these variables on systemic risk. Our framework shows that climate change affects each of the risk-management categories commonly assessed in the financial markets as well as the ways that they interact to generate broader systemic risk.

7th Annual Conference on CCP Risk Management

November 2020

Ketan B. Patel will moderate a panel discussion on Resilience and Lessons Learned in Central Clearing in 2020. Senior policymakers, central bankers, market regulators, industry leaders and others are invited to participate in the conference. The CCP Risk Management Conference is by invitation only. To encourage candid discussion among invited participants, the conference is conducted under the Chatham House rule and is not open to the press. This conference has been very well attended in the past.

Diversity in Financial Markets

October 2020

The Chicago Fed and FIA teamed up with a group of expert panelists to explore past and present experiences, as well as the outlook for diversity in financial markets. The panelists explored why diversity is important, how to achieve it, and what metrics could be used to determine progress. During this interactive webinar, panelists also shared their experiences and answer audience questions on impactful changes and how to move your organization and the broader industry forward.

Watch the recording here .

What Are the Financial Systemic Implications of Access and Non-Access to Federal Reserve Deposit Accounts for Central Counterparties?

October 2020 By Maggie Sklar

In this working paper, I examine the interconnections between designated derivatives central counterparties (CCPs) with Federal Reserve deposit accounts and non-designated CCPs and the potential financial stability implications. This working paper notes the interconnections between the non-designated and designated derivatives CCPs through their clearing members and the commercial custodial banks they utilize to hold and transfer collateral. The paper then identifies additional potential contagion risks and financial stability risks, including liquidity risk, market risk, concentration risk, and loss of confidence more broadly. Although there are a number of research articles addressing these topics with respect to designated CCPs or OTC derivatives, this working paper includes the perspective looking at U.S. futures CCPs and non-designated CCPs.

The Future of Innovation in an Inclusive Chicago

Chicago is a global hub for innovation in finance, technology, and other sectors. Yet continued innovation is not a given, and the makeup of the city’s population has not been reflected in its most innovative industries. How can Chicago’s history of innovation continue, and how can it include all residents of the city?

Law & Compliance Division Conference

Robert Steigerwald participated in a panel discussion of “CCP Risk” at the Futures Industry Association’s Virtual Law & Compliance Conference. The panel discussed CCP risk management issues, including the CCP risk whitepaper, as well as newly finalized amendments to Part 39 regulations.

What Risk Managers from NASDAQ and Options Clearing Corp Learned From the Covid-19 Crisis: Perspectives on Resilience and Challenges During the Pandemic

September 2020

This episode of LaSalle Street welcomes chief risk officers from Options Clearing Corporation and Nasdaq Inc. to discuss what the pandemic is teaching us about risk management and global financial markets.

Clearinghouse Risk, Reference Rates, and Cryptocurrency with Former CFTC Chair J. Christopher Giancarlo

The Honorable J. Christopher Giancarlo was on the frontlines of the biggest issues shaping global financial markets as the 13th chairman of the U.S. Commodity Futures Trading Commission (CFTC). Known by some as "CryptoDad," Giancarlo visits LaSalle Street to discuss his reflections a year after leaving the CFTC, key issues he faced during his tenure, and emerging issues shaping the markets today. The conversation includes discussion of clearinghouse risk and the work of the Financial Stability Board, the risks embedded in reference rates, and why regulators should be investing time in the future of digital currency.

The Influence and Limits of Central Bank Backstops

August 2020 By Sam Schulhofer-Wohl

In this post, I explain how central banks’ “backstop” lending facilities can influence financial conditions even when the facilities see relatively little borrowing. The knowledge that credit is available, even if at a relatively high penalty interest rate, can calm markets and encourage confidence in the economy. The availability of a backstop can also influence interest rates on private-sector transactions. However, backstops aim to support normal market functioning—not to make credit cheaper or more plentiful than what a normally functioning market would deliver.

Chicago Fed Hires Ketan B. Patel as Policy Advisor and Head of Financial Markets Risk Analysis

Patel will be responsible for analyzing the public policy implications of risks in financial markets and financial market infrastructures. The group’s research on financial market institutions, technology and infrastructure helps inform and foster stable and efficient national monetary, financial and payments systems.

Second Joint Deutsche Bundesbank - European Central Bank - Federal Reserve Bank of Chicago Conference on CCP Risk Management

February 2020 Conference agenda

Monetary Policy Implementation With an Ample Supply of Reserves

January 2020 By Gara Afonso, Kyungmin Kim, Antoine Martin, Ed Nosal, Simon Potter and Sam Schulhofer-Wohl

Chicago Fed Hires Maggie Sklar as Senior Policy Advisor and Director of International Engagement

December 2019

Maggie Sklar joined the economic research department as senior policy advisor and director of international engagement in the financial markets group. Sklar reports to Vice President Alessandro Cocco.

Can Broader Access to Direct CCP Clearing Reduce the Concentration of Cleared Derivatives?

December 2019 By Nahiomy Alvarez

Chicago Fed Hires Vice President of Financial Markets Group

October 2019

The Federal Reserve Bank of Chicago announced that Alessandro Cocco will join the economic research department on October 21 as vice president of the financial markets group. Cocco will report to senior vice president Sam Schulhofer-Wohl.

Understanding Recent Fluctuations in Short-Term Interest Rates

October 2019 By Sam Schulhofer-Wohl

6th Annual CCP Risk Management Conference

October 2019 Conference agenda

The Concentration of Cleared Derivatives: Can Access to Direct CCP Clearing for End-Users Address the Challenge?

August 2019 By Nahiomy Alvarez and John W. McPartland

Symposium on OTC Derivatives and Central Clearing

April 2019 Conference agenda

First Joint Deutsche Bundesbank - European Central Bank - Federal Reserve Bank of Chicago Conference on CCP Risk Management

February 2019 Conference agenda

February 2019 Conference summary

Managing Risk in Global Financial Markets: CCP Governance, Supervisory Stress Testing, and Default Management Auctions

December 2018 By Nahiomy Alvarez

International Regulators Conference

October 2018 Conference agenda

Fifth Annual Risk Management Conference: Examining Regulatory Initiatives

A “principled” approach to international guidance for central counterparties.

July 2018 By Rebecca Lewis

Central Counterparty Risk Management: Beyond Default Risk

December 2017 By Rebecca Lewis

Blockchain and Financial Market Innovation

November 2017 By Rebecca Lewis, John W. McPartland and Rajeev Ranjan

Fourth Annual Conference on CCP Risk Management

October 2017 Conference agenda

Symposium on OTC Derivatives—A Conference Summary

August 2017 By Rebecca Lewis and Ning Yu

Statement on 'Clearing the Next Crisis: Resilience, Recovery and Resolution of Derivatives Clearinghouses'

June 2017 By Robert Steigerwald

Symposium on OTC Derivatives

May 2017 Conference agenda

Symposium on Central Clearing

Non-default loss allocation at ccps.

April 2017 By Rebecca Lewis and John W. McPartland

A CCP Is a CCP Is a CCP

April 2017 By Robert Cox and Robert Steigerwald

The Goldilocks Problem: How to Get Incentives and Default Waterfalls 'Just Right'

March 2017 By John W. McPartland and Rebecca Lewis

Taking a Deep Dive into Margins for Cleared Derivatives

December 2016 By Rebecca Lewis

Third Annual Conference on CCP Risk Management

October 2016 Conference agenda

Resolving Central Counterparties after Dodd-Frank: Are They Eligible for 'Orderly Liquidation'?

September 2016 By Robert Steigerwald and David W. DeCarlo

Conference on CCP Public Quantitative Disclosure

July 2016 Conference agenda

Issues in clearing and settlement

March 2016 Conference agenda

2015 Conference on CCP Risk Management — Central Counterparty Resolution

November 2015 Conference agenda

A New Approach to Stock Market Execution

September 2015 By John W. McPartland and Rebecca Lewis

2015 Symposium on Central Clearing

April 2015 Conference agenda

CCP Recovery and Resolution Conference

November 2014 Conference agenda

2014 Derivatives Symposium

April 2014 Conference agenda

The Role of Time-Critical Liquidity in Financial Markets

July 2013 By Robert Steigerwald and David Marshall

Recommendations for Equitable Allocation of Trades in High Frequency Trading Environments

May 2013 By John W. McPartland

2013 Derivatives Symposium

April 2013 Conference agenda

How Do Clearing Organizations Control the Risks of High Speed Trading?

June 2012 By John W. McPartland and Carol Clark

Public Policy Symposium on Central Clearing of OTC Derivatives

April 2012 Conference agenda

What Tools Do Vendors Provide to Control the Risks of High Speed Trading?

October 2011 By Carol Clark, Richard Heckinger, Rajeev Ranjan and John W. McPartland

What Is Clearing and Why Is It Important?

September 2010 By Robert Steigerwald and Ed Nosal

Clearing and Settlement of Exchange Traded Derivatives

October 2009 By John W. McPartland

Policymakers, Researchers, and Practitioners Discuss the Role of Central Counterparties

November 2006 By Douglas D. Evanoff, Daniela Russo, and Robert Steigerwald

Derivatives Clearing and Settlement: A Comparison of Central Counterparties and Alternative Structures

November 2006 By Robert Steigerwald and Robert R. Bliss

Foreign Exchange Trading and Settlement: Past and Present

February 2006 By John W. McPartland

Featured publication

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Central counterparties (CCPs) are an important part of contemporary financial market infrastructure. The orderly risk management operations and financial resilience of CCPs and other market infrastructures are essential for financial stability. This paper discusses many differences between CCPs and banks and the significance of those differences, including their business models and risk profiles, with CCPs acting as risk managers that are uniquely subject to the credit and liquidity risk of clearing member default.

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Stock Market Volatility and Return Analysis: A Systematic Literature Review

Roni bhowmik.

1 School of Economics and Management, Jiujiang University, Jiujiang 322227, China

2 Department of Business Administration, Daffodil International University, Dhaka 1207, Bangladesh

Shouyang Wang

3 Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China; nc.ca.ssma@gnawys

In the field of business research method, a literature review is more relevant than ever. Even though there has been lack of integrity and inflexibility in traditional literature reviews with questions being raised about the quality and trustworthiness of these types of reviews. This research provides a literature review using a systematic database to examine and cross-reference snowballing. In this paper, previous studies featuring a generalized autoregressive conditional heteroskedastic (GARCH) family-based model stock market return and volatility have also been reviewed. The stock market plays a pivotal role in today’s world economic activities, named a “barometer” and “alarm” for economic and financial activities in a country or region. In order to prevent uncertainty and risk in the stock market, it is particularly important to measure effectively the volatility of stock index returns. However, the main purpose of this review is to examine effective GARCH models recommended for performing market returns and volatilities analysis. The secondary purpose of this review study is to conduct a content analysis of return and volatility literature reviews over a period of 12 years (2008–2019) and in 50 different papers. The study found that there has been a significant change in research work within the past 10 years and most of researchers have worked for developing stock markets.

1. Introduction

In the context of economic globalization, especially after the impact of the contemporary international financial crisis, the stock market has experienced unprecedented fluctuations. This volatility increases the uncertainty and risk of the stock market and is detrimental to the normal operation of the stock market. To reduce this uncertainty, it is particularly important to measure accurately the volatility of stock index returns. At the same time, due to the important position of the stock market in the global economy, the beneficial development of the stock market has become the focus. Therefore, the knowledge of theoretical and literature significance of volatility are needed to measure the volatility of stock index returns.

Volatility is a hot issue in economic and financial research. Volatility is one of the most important characteristics of financial markets. It is directly related to market uncertainty and affects the investment behavior of enterprises and individuals. A study of the volatility of financial asset returns is also one of the core issues in modern financial research and this volatility is often described and measured by the variance of the rate of return. However, forecasting perfect market volatility is difficult work and despite the availability of various models and techniques, not all of them work equally for all stock markets. It is for this reason that researchers and financial analysts face such a complexity in market returns and volatilities forecasting.

The traditional econometric model often assumes that the variance is constant, that is, the variance is kept constant at different times. An accurate measurement of the rate of return’s fluctuation is directly related to the correctness of portfolio selection, the effectiveness of risk management, and the rationality of asset pricing. However, with the development of financial theory and the deepening of empirical research, it was found that this assumption is not reasonable. Additionally, the volatility of asset prices is one of the most puzzling phenomena in financial economics. It is a great challenge for investors to get a pure understanding of volatility.

A literature reviews act as a significant part of all kinds of research work. Literature reviews serve as a foundation for knowledge progress, make guidelines for plan and practice, provide grounds of an effect, and, if well guided, have the capacity to create new ideas and directions for a particular area [ 1 ]. Similarly, they carry out as the basis for future research and theory work. This paper conducts a literature review of stock returns and volatility analysis based on generalized autoregressive conditional heteroskedastic (GARCH) family models. Volatility refers to the degree of dispersion of random variables.

Financial market volatility is mainly reflected in the deviation of the expected future value of assets. The possibility, that is, volatility, represents the uncertainty of the future price of an asset. This uncertainty is usually characterized by variance or standard deviation. There are currently two main explanations in the academic world for the relationship between these two: The leverage effect and the volatility feedback hypothesis. Leverage often means that unfavorable news appears, stock price falls, leading to an increase in the leverage factor, and thus the degree of stock volatility increases. Conversely, the degree of volatility weakens; volatility feedback can be simply described as unpredictable stock volatility that will inevitably lead to higher risk in the future.

There are many factors that affect price movements in the stock market. Firstly, there is the impact of monetary policy on the stock market, which is extremely substantial. If a loose monetary policy is implemented in a year, the probability of a stock market index rise will increase. On the other hand, if a relatively tight monetary policy is implemented in a year, the probability of a stock market index decline will increase. Secondly, there is the impact of interest rate liberalization on risk-free interest rates. Looking at the major global capital markets, the change in risk-free interest rates has a greater correlation with the current stock market. In general, when interest rates continue to rise, the risk-free interest rate will rise, and the cost of capital invested in the stock market will rise simultaneously. As a result, the economy is expected to gradually pick up during the release of the reform dividend, and the stock market is expected to achieve a higher return on investment.

Volatility is the tendency for prices to change unexpectedly [ 2 ], however, all kinds of volatility is not bad. At the same time, financial market volatility has also a direct impact on macroeconomic and financial stability. Important economic risk factors are generally highly valued by governments around the world. Therefore, research on the volatility of financial markets has always been the focus of financial economists and financial practitioners. Nowadays, a large part of the literature has studied some characteristics of the stock market, such as the leverage effect of volatility, the short-term memory of volatility, and the GARCH effect, etc., but some researchers show that when adopting short-term memory by the GARCH model, there is usually a confusing phenomenon, as the sampling interval tends to zero. The characterization of the tail of the yield generally assumes an ideal situation, that is, obeys the normal distribution, but this perfect situation is usually not established.

Researchers have proposed different distributed models in order to better describe the thick tail of the daily rate of return. Engle [ 3 ] first proposed an autoregressive conditional heteroscedasticity model (ARCH model) to characterize some possible correlations of the conditional variance of the prediction error. Bollerslev [ 4 ] has been extended it to form a generalized autoregressive conditional heteroskedastic model (GARCH model). Later, the GARCH model rapidly expanded and a GARCH family model was created.

When employing GARCH family models to analyze and forecast return volatility, selection of input variables for forecasting is crucial as the appropriate and essential condition will be given for the method to have a stationary solution and perfect matching [ 5 ]. It has been shown in several findings that the unchanged model can produce suggestively different results when it is consumed with different inputs. Thus, another key purpose of this literature review is to observe studies which use directional prediction accuracy model as a yardstick from a realistic point of understanding and has the core objective of the forecast of financial time series in stock market return. Researchers estimate little forecast error, namely measured as mean absolute deviation (MAD), root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE) which do not essentially interpret into capital gain [ 6 , 7 ]. Some others mention that the predictions are not required to be precise in terms of NMSE (normalized mean squared error) [ 8 ]. It means that finding the low rate of root mean squared error does not feed high returns, in another words, the relationship is not linear between two.

In this manuscript, it is proposed to categorize the studies not only by their model selection standards but also for the inputs used for the return volatility as well as how precise it is spending them in terms of return directions. In this investigation, the authors repute studies which use percentage of success trades benchmark procedures for analyzing the researchers’ proposed models. From this theme, this study’s authentic approach is compared with earlier models in the literature review for input variables used for forecasting volatility and how precise they are in analyzing the direction of the related time series. There are other review studies on return and volatility analysis and GARCH-family based financial forecasting methods done by a number of researchers [ 9 , 10 , 11 , 12 , 13 ]. Consequently, the aim of this manuscript is to put forward the importance of sufficient and necessary conditions for model selection and contribute for the better understanding of academic researchers and financial practitioners.

Systematic reviews have most notable been expanded by medical science as a way to synthesize research recognition in a systematic, transparent, and reproducible process. Despite the opportunity of this technique, its exercise has not been overly widespread in business research, but it is expanding day by day. In this paper, the authors have used the systematic review process because the target of a systematic review is to determine all empirical indication that fits the pre-decided inclusion criteria or standard of response to a certain research question. Researchers proved that GARCH is the most suitable model to use when one has to analysis the volatility of the returns of stocks with big volumes of observations [ 3 , 4 , 6 , 9 , 13 ]. Researchers observe keenly all the selected literature to answer the following research question: What are the effective GARCH models to recommend for performing market volatility and return analysis?

The main contribution of this paper is found in the following four aspects: (1) The best GARCH models can be recommended for stock market returns and volatilities evaluation. (2) The manuscript considers recent papers, 2008 to 2019, which have not been covered in previous studies. (3) In this study, both qualitative and quantitative processes have been used to examine the literature involving stock returns and volatilities. (4) The manuscript provides a study based on journals that will help academics and researchers recognize important journals that they can denote for a literature review, recognize factors motivating analysis stock returns and volatilities, and can publish their worth study manuscripts.

2. Methodology

A systematic literature examination of databases should recognize as complete a list as possible of relevant literature while keeping the number of irrelevant knocks small. The study is conducted by a systematic based literature review, following suggestions from scholars [ 14 , 15 ]. This manuscript was led by a systematic database search, surveyed by cross-reference snowballing, as demonstrated in Figure 1 , which was adapted from Geissdoerfer et al. [ 16 ]. Two databases were selected for the literature search: Scopus and Web-of-Science. These databases were preferred as they have some major depositories of research and are usually used in literature reviews for business research [ 17 ].

Literature review method.

At first stage, a systematic literature search is managed. The keywords that were too broad or likely to be recognized in literature-related keywords with other research areas are specified below. As shown in Table 1 , the search string “market return” in ‘Title‘ respectively “stock market return”, “stock market volatility”, “stock market return volatility”, “GARCH family model* for stock return”, “forecasting stock return”, and GARCH model*, “financial market return and volatility” in ‘Topic’ separately ‘Article title, Abstract, Keywords’ were used to search for reviews of articles in English on the Elsevier Scopus and Thomson Reuters Web-of-Science databases. The asterisk (*) is a commonly used wildcard symbol that broadens a search by finding words that start with the same letters.

Literature search strings for database.

At second stage, suitable cross-references were recognized in this primary sample by first examining the publications’ title in the reference portion and their context and cited content in the text. The abstracts of the recognized further publications were examined to determine whether the paper was appropriate or not. Appropriate references were consequently added to the sample and analogously scanned for appropriate cross-references. This method was continual until no additional appropriate cross-references could be recognized.

At the third stage, the ultimate sample was assimilated, synthesized, and compiled into the literature review presented in the subsequent section. The method was revised a few days before the submission.

Additionally, the list of affiliation criteria in Table 2 , which is formed on discussions of the authors, with the summaries of all research papers were independently checked in a blind system method. Evaluations were established on the content of the abstract, with any extra information unseen, and were comprehensive rather than exclusive. In order to check for inter-coder dependability, an initial sample of 30 abstracts were studied for affiliation by the authors. If the abstract was not satisfactorily enough, the whole paper was studied. Simply, 4.61 percent of the abstract resulted in variance between the researchers. The above-mentioned stages reduced the subsequent number of full papers for examination and synthesis to 50. In order to recognize magnitudes, backgrounds, and moderators, these residual research papers were reviewed in two rounds of reading.

Affiliation criteria.

3. Review of Different Studies

In this paper, a large amount of articles were studied but only a few were well thought out to gather the quality developed earlier. For every published article, three groups were specified. Those groups were considered as index and forecast time period, input elements, econometric models, and study results. The first group namely “index and forecast time period with input elements” was considered since market situation like emerging, frontier, and developed markets which are important parameters of forecast and also the length of evaluation is a necessary characteristic for examining the robustness of the model. Furthermore, input elements are comparatively essential parameters for a forecast model because the analytical and diagnostic ability of the model is mainly supported on the inputs that a variable uses. In the second group, “model” was considered forecast models proposed by authors and other models for assessment. The last group is important to our examination for comparing studies in relationships of proper guiding return and volatility, acquired by using recommended estimate models, named the “study results” group.

Measuring the stock market volatility is an incredibly complex job for researchers. Since volatility tends to cluster, if today’s volatility is high, it is likely to be high tomorrow but they have also had an attractive high hit rate with major disasters [ 4 , 7 , 11 , 12 ]. GARCH models have a strong background, recently having crossed 30 years of the fast progress of GARCH-type models for investigating the volatility of market data. Literature of eligible papers were clustered in two sub groups, the first group containing GARCH and its variations model, and the second group containing bivariate and other multivariate GARCH models, summarized in a table format for future studies. Table 3 explains the review of GARCH and its variations models. The univariate GARCH model is for a single time series. It is a statistical model that is used to analyze a number of different kinds of financial data. Financial institutions and researchers usually use this model to estimate the volatility of returns for stocks, bonds, and market indices. In the GARCH model, current volatility is influenced by past innovation to volatility. GARCH models are used to model for forecast volatility of one time series. The most widely used GARCH form is GARCH (1, 1) and this has some extensions.

Different literature studies based on generalized autoregressive conditional heteroskedastic (GARCH) and its variations models.

Notes: APARCH (Asymmetric Power ARCH), AIC (Akaike Information Criterion), OHLC (Open-High-Low-Close Chart), NSE (National Stock Exchange of India), EWMA (Exponentially Weighted Moving Average), CGARCH (Component GARCH), BDS (Brock, Dechert & Scheinkman) Test, ARCH-LM (ARCH-Lagrange Multiplier) test, VAR (Vector Autoregression) model, VEC (Vector Error Correction) model, ARFIMA (Autoregressive Fractional Integral Moving Average), FIGARCH (Fractionally Integrated GARCH), SHCI (Shanghai Stock Exchange Composite Index), SZCI (Shenzhen Stock Exchange Component Index), ADF (Augmented Dickey–Fuller) test, BSE (Bombay Stock Exchange), and PGARCH (Periodic GARCH) are discussed.

In a simple GARCH model, the squared volatility σ t 2 is allowed to change on previous squared volatilities, as well as previous squared values of the process. The conditional variance satisfies the following form: σ t 2 = α 0 + α 1 ϵ t − 1 2 + … + α q ϵ t − q 2 + β 1 σ t − 1 2 + … + β p σ t − p 2 where, α i > 0 and β i > 0 . For the GARCH model, residuals’ lags can substitute by a limited number of lags of conditional variances, which abridges the lag structure and in addition the estimation method of coefficients. The most often used GARCH model is the GARCH (1, 1) model. The GARCH (1, 1) process is a covariance-stationary white noise process if and only if α 1 + β < 1 . The variance of the covariance-stationary process is given by α 1   /   ( 1 − α 1 − β ) . It specifies that σ n 2     is based on the most recent observation of φ t 2   and the most recent variance rate σ n − 1 2 . The GARCH (1, 1) model can be written as σ n 2 = ω + α φ n − 1 2 + β σ n − 1 2 and this is usually used for the estimation of parameters in the univariate case.

Though, GARCH model is not a complete model, and thus could be developed, these developments are detected in the form of the alphabet soup that uses GARCH as its key component. There are various additions of the standard GARCH family models. Nonlinear GARCH (NGARCH) was proposed by Engle and Ng [ 18 ]. The conditional covariance equation is in the form: σ t 2 = γ + α ( ε t − 1 − ϑ σ t − 1   ) 2 + β σ t − 1 2 , where α ,   β ,   γ > 0 . The integrated GARCH (IGARCH) is a restricted version of the GARCH model, where the sum of all the parameters sum up to one and this model was introduced by Engle and Bollerslev [ 19 ]. Its phenomenon might be caused by random level shifts in volatility. The simple GARCH model fails in describing the “leverage effects” which are detected in the financial time series data. The exponential GARCH (EGARCH) introduced by Nelson [ 5 ] is to model the logarithm of the variance rather than the level and this model accounts for an asymmetric response to a shock. The GARCH-in-mean (GARCH-M) model adds a heteroskedasticity term into the mean equation and was introduced by Engle et al. [ 20 ]. The quadratic GARCH (QGARCH) model can handle asymmetric effects of positive and negative shocks and this model was introduced by Sentana [ 21 ]. The Glosten-Jagannathan-Runkle GARCH (GJR-GARCH) model was introduced by Glosten et al. [ 22 ], its opposite effects of negative and positive shocks taking into account the leverage fact. The threshold GARCH (TGARCH) model was introduced by Zakoian [ 23 ], this model is also commonly used to handle leverage effects of good news and bad news on volatility. The family GARCH (FGARCH) model was introduced by Hentschel [ 24 ] and is an omnibus model that is a mix of other symmetric or asymmetric GARCH models. The COGARCH model was introduced by Klüppelberg et al. [ 25 ] and is actually the stochastic volatility model, being an extension of the GARCH time series concept to continuous time. The power-transformed and threshold GARCH (PTTGARCH) model was introduced by Pan et al. [ 26 ], this model is a very flexible model and, under certain conditions, includes several ARCH/GARCH models.

Based on the researchers’ articles, the symmetric GARCH (1, 1) model has been used widely to forecast the unconditional volatility in the stock market and time series data, and has been able to simulate the asset yield structure and implied volatility structure. Most researchers show that GARCH (1, 1) with a generalized distribution of residual has more advantages in volatility assessment than other models. Conversely, the asymmetry influence in stock market volatility and return analysis was beyond the descriptive power of the asymmetric GARCH models, as the models could capture more specifics. Besides, the asymmetric GARCH models can incompletely measure the effect of positive or negative shocks in stock market return and volatility, and the GARCH (1, 1) comparatively failed to accomplish this fact. In asymmetric effect, the GJR-GARCH model performed better and produced a higher predictable conditional variance during the period of high volatility. In addition, among the asymmetric GARCH models, the reflection of EGARCH model appeared to be superior.

Table 4 has explained the review of bivariate and other multivariate GARCH models. Bivariate model analysis was used to find out if there is a relationship between two different variables. Bivariate model uses one dependent variable and one independent variable. Additionally, the Multivariate GARCH model is a model for two or more time series. Multivariate GARCH models are used to model for forecast volatility of several time series when there are some linkages between them. Multivariate model uses one dependent variable and more than one independent variable. In this case, the current volatility of one time series is influenced not only by its own past innovation, but also by past innovations to volatilities of other time series.

Different literature studies based on bivariate and other multivariate GARCH models.

The most recognizable use of multivariate GARCH models is the analysis of the relations between the volatilities and co-volatilities of several markets. A multivariate model would create a more dependable model than separate univariate models. The vector error correction (VEC) models is the first MGARCH model which was introduced by Bollerslev et al. [ 66 ]. This model is typically related to subsequent formulations. The model can be expressed in the following form: v e c h   ( H t ) = ℂ + ∑ j = 1 q X j   v e c h   ( ϵ t − j   ϵ t − j ' ) + ∑ j = 1 p Y j   v e c h   ( H t − j   )   where v e c h is an operator that stacks the columns of the lower triangular part of its argument square matrix and H t is the covariance matrix of the residuals. The regulated version of the VEC model is the DVEC model and was also recommended by Bollerslev et al. [ 66 ]. Compared to the VEC model, the estimation method proceeded far more smoothly in the DVEC model. The Baba-Engle-Kraft-Kroner (BEKK) model was introduced by Baba et al. [ 67 ] and is an innovative parameterization of the conditional variance matrix H t . The BEKK model accomplishes the positive assurance of the conditional covariance by conveying the model in a way that this property is implied by the model structure. The Constant Conditional Correlation (CCC) model was recommended by Bollerslev [ 68 ], to primarily model the conditional covariance matrix circuitously by estimating the conditional correlation matrix. The Dynamic Conditional Correlation (DCC) model was introduced by Engle [ 69 ] and is a nonlinear mixture of univariate GARCH models and also a generalized variety of the CCC model. To overcome the inconveniency of huge number of parameters, the O-GARCH model was recommended by Alexander and Chibumba [ 70 ] and consequently developed by Alexander [ 71 , 72 ]. Furthermore, a multivariate GARCH model GO-GARCH model was introduced by Bauwens et al. [ 73 ].

The bivariate models showed achieve better in most cases, compared with the univariate models [ 85 ]. MGARCH models could be used for forecasting. Multivariate GARCH modeling delivered a realistic but parsimonious measurement of the variance matrix, confirming its positivity. However, by analyzing the relative forecasting accuracy of the two formulations, BEKK and DCC, it could be deduced that the forecasting performance of the MGARCH models was not always satisfactory. By comparing it with the other multivariate GARCH models, BEKK-GARCH model was comparatively better and flexible but it needed too many parameters for multiple time series. Conversely, for the area of forecasting, the DCC-GARCH model was more parsimonious. In this regard, it was significantly essential to balance parsimony and flexibility when modeling multivariate GARCH models.

The current systematic review has identified 50 research articles for studies on significant aspects of stock market return and volatility, review types, and GARCH model analysis. This paper noticed that all the studies in this review used an investigational research method. A literature review is necessary for scholars, academics, and practitioners. However, assessing various kinds of literature reviews can be challenging. There is no use for outstanding and demanding literature review articles, since if they do not provide a sufficient contribution and something that is recent, it will not be published. Too often, literature reviews are fairly descriptive overviews of research carried out among particular years that draw data on the number of articles published, subject matter covered, authors represented, and maybe methods used, without conducting a deeper investigation. However, conducting a literature review and examining its standard can be challenging, for this reason, this article provides some rigorous literature reviews and, in the long run, to provide better research.

4. Conclusions

Working on a literature review is a challenge. This paper presents a comprehensive literature which has mainly focused on studies on return and volatility of stock market using systematic review methods on various financial markets around the world. This review was driven by researchers’ available recommendations for accompanying systematic literature reviews to search, examine, and categorize all existing and accessible literature on market volatility and returns [ 16 ]. Out of the 435 initial research articles located in renowned electronic databases, 50 appropriate research articles were extracted through cross-reference snowballing. These research articles were evaluated for the quality of proof they produced and were further examined. The raw data were offered by the authors from the literature together with explanations of the data and key fundamental concepts. The outcomes, in this research, delivered future magnitudes to research experts for further work on the return and volatility of stock market.

Stock market return and volatility analysis is a relatively important and emerging field of research. There has been plenty of research on financial market volatility and return because of easily increasing accessibility and availability of researchable data and computing capability. The GARCH type models have a good model on stock market volatilities and returns investigation. The popularity of various GARCH family models has increased in recent times. Every model has its specific strengths and weaknesses and has at influence such a large number of GARCH models. To sum up the reviewed papers, many scholars suggest that the GARCH family model provides better results combined with another statistical technique. Based on the study, much of the research showed that with symmetric information, GARCH (1, 1) could precisely explain the volatilities and returns of the data and when under conditions of asymmetric information, the asymmetric GARCH models would be more appropriate [ 7 , 32 , 40 , 47 , 48 ]. Additionally, few researchers have used multivariate GARCH model statistical techniques for analyzing market volatility and returns to show that a more accurate and better results can be found by multivariate GARCH family models. Asymmetric GARCH models, for instance and like, EGARCH, GJR GARCH, and TGARCH, etc. have been introduced to capture the effect of bad news on the change in volatility of stock returns [ 42 , 58 , 62 ]. This study, although short and particular, attempted to give the scholar a concept of different methods found in this systematic literature review.

With respect to assessing scholars’ articles, the finding was that rankings and specifically only one GARCH model was sensitive to the different stock market volatilities and returns analysis, because the stock market does not have similar characteristics. For this reason, the stock market and model choice are little bit difficult and display little sensitivity to the ranking criterion and estimation methodology, additionally applying software is also another matter. The key challenge for researchers is finding the characteristics in stock market summarization using different kinds of local stock market returns, volatility detection, world stock market volatility, returns, and other data. Additional challenges are modeled by differences of expression between different languages. From an investigation perception, it has been detected that different authors and researchers use special datasets for the valuation of their methods, which may put boundary assessments between research papers.

Whenever there is assurance that scholars build on high accuracy, it will be easier to recognize genuine research gaps instead of merely conducting the same research again and again, so as to progress better and create more appropriate hypotheses and research questions, and, consequently, to raise the standard of research for future generation. This study will be beneficial for researchers, scholars, stock exchanges, regulators, governments, investors, and other concerned parties. The current study also contributes to the scope of further research in the area of stock volatility and returns. The content analysis can be executed taking the literature of the last few decades. It determined that a lot of methodologies like GARCH models, Johansen models, VECM, Impulse response functions, and Granger causality tests are practiced broadly in examining stock market volatility and return analysis across countries as well as among sectors with in a country.

Author Contributions

R.B. and S.W. proposed the research framework together. R.B. collected the data, and wrote the document. S.W. provided important guidance and advice during the process of this research. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

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A bibliographic overview of financial engineering in the emerging financial market

• REVIEW PAPERS
• Published: 14 September 2023
• Volume 14 , pages 2048–2065, ( 2023 )

Cite this article

• Jyoti Ranjan Jena 1 ,
• Saroj Kanta Biswal 1 ,
• Avinash K. Shrivastava 2 &
• Rashmi Ranjan Panigrahi   ORCID: orcid.org/0000-0002-2199-293X 3  

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Financial engineering is constantly changing and encountering new problems. Financial engineering helps us detect emerging trends and challenges, such as fintech’s effect on banking institutions or environmental change, and design novel solutions. Still, many areas remain open to exploring the contribution of FE research in finance. This study has adopted combined qualitative research approaches through bibliometric analysis. The research was conducted from 2007 to 2022. Study findings and conclusions are supported by an analysis of bibliographic coupling, co-occurrence & co-citation of 343 research publications taken from the Scopus database, and analysis was performed using software tools such as VOS-Viewer and Biblioshiny with R Studio. Based on the results of these analyses, the study was able to conclude the trends and characteristics of research on financial engineering in the financial market. The s tudy identifies prominent authors, journals, and institutions using bibliometric analysis. The current study highlighted the most cited research articles and identified the seven most emerging thematic clusters. The originality extracted from research findings compels and motivates extensive research in FE in the future. The emerging areas and themes identified from the study, i.e., (1) FE and adoption of AI & IOT Applications for RM, (2) investment decision and business crisis, and (3) recent developments and mathematical application in risk analysis. The novelty of the study lies in its focus on financial engineering in emerging financial markets, the adoption of a bibliographic overview methodology, the integration and evaluation of previous research, the identification of trends and research gaps, and its value as a resource for researchers and practitioners. These aspects make it a unique and valuable contribution to the field of financial engineering.

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Jena, J.R., Biswal, S.K., Shrivastava, A.K. et al. A bibliographic overview of financial engineering in the emerging financial market. Int J Syst Assur Eng Manag 14 , 2048–2065 (2023). https://doi.org/10.1007/s13198-023-02123-8

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DOI : https://doi.org/10.1007/s13198-023-02123-8

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Maintaining Buy Rating on Marinus Pharmaceuticals: Confidence in Research and Strategic Financial Health

Analyst Jason Butler from JMP Securities reiterated a Buy rating on Marinus ( MRNS – Research Report ) and decreased the price target to $12.00 from $15.00.

Jason Butler has given his Buy rating due to a combination of factors surrounding Marinus Pharmaceuticals. He acknowledges that the Phase 3 RAISE trial for their product, I.V. ganaxolone, in treating refractory status epilepticus (RSE) did not meet the early stopping criteria. However, the decision by an independent committee to continue the trial, with expectations to complete enrollment and announce full results in the near future, positively influences his outlook. Despite the initial setback, Butler maintains confidence in the company’s research trajectory and potential for successful outcomes. Furthermore, Butler is encouraged by Marinus’s operational strategies and its financial position. The nearing completion of the TrustTSC trial enrollment and the anticipated timely filing of a New Drug Application (NDA) signal progress in the company’s development pipeline. Additionally, the implementation of cost-saving strategies expected to extend the company’s cash runway, coupled with a solid cash position and preliminary revenues for ZTALMY, further substantiate his Buy rating. These strategic and financial measures indicate a robust framework for future growth and stability, underpinning Butler’s positive recommendation.

In another report released today, H.C. Wainwright also maintained a Buy rating on the stock with a $27.00 price target.

Based on the recent corporate insider activity of 26 insiders, corporate insider sentiment is positive on the stock. This means that over the past quarter there has been an increase of insiders buying their shares of MRNS in relation to earlier this year.

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Marinus (MRNS) Company Description:

Marinus Pharmaceuticals, Inc. is a biopharmaceutical company, which engages in the identification and development of neuropsychiatric therapeutics. Its clinical stage drug product candidate, ganaxolone, is a positive allosteric modulator being developed in three different dose forms: intravenous, capsule, and liquid. The company was founded by Geoffrey E. Chaiken, Harry H. Penner Jr., Vincent A. Pieribone and Kenneth R. Shaw on August 14, 2003 and is headquartered in Radnor, PA.

Read More on MRNS:

• Marinus Comments on patent challenge by Ovid Therapeutics
• Marinus Pharmaceuticals management to meet with Jefferies
• Marinus Pharmaceuticals’ ganaxolone approved by MHRA
• Marinus Pharmaceuticals sees FY24 U.S. ZTALMY revenue $32M-$34M
• Marinus Pharmaceuticals reports Q4 EPS (74c), consensus (62c)

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