About V-Lab

The Volatility Laboratory (V-Lab) at NYU Stern was established to advance the understanding of financial market dynamics through rigorous measurement and modeling. Our original research focus centered on:

Over time, V-Lab has expanded to address a wider array of financial risks that reflect today’s increasingly complex markets. These include:

This broader scope reflects our commitment to providing timely, transparent, and data-driven insights into the full spectrum of risks. Each application offers open-access models and real-time data to support decision-making by academics, market participants, and policymakers. V-Lab is committed to providing the financial community with the tools and insights needed to navigate the ever-changing landscape of global markets.

Our Mission

V-Lab is dedicated to developing and disseminating cutting-edge research on risks affecting global financial markets. We seek to be the world’s leading authority on measuring, modeling, and managing economies and financial markets. Our goal is to enhance the stability and efficiency of these markets by providing real-time data and analyses that inform decision-making processes.​ Our research helps investors, regulators, and society understand the risks associated with financial markets and make better decisions.

Key Initiatives and Research Areas

Our research spans foundational areas in financial econometrics as well as new domains driven by evolving market conditions and policy concerns. Key initiatives include:

• Financial Volatility Forecasting
  V-Lab offers volatility forecasts for a wide range of asset classes, including equities, indices, currencies, and commodities. These forecasts help users identify periods of heightened risk, adjust portfolio exposures, and evaluate volatility-targeting strategies. Market practitioners use this information for risk management and derivatives pricing, while researchers rely on it to study asset dynamics and market efficiency.
• Asset Correlation Estimation
  By measuring time-varying correlations between asset returns, V-Lab enables users to understand how relationships among assets evolve—especially during periods of market stress. This supports more robust portfolio construction, stress testing, and systemic risk assessment. Our models allow users to capture contagion effects and shifting diversification benefits in real time.
• Systemic Risk Assessment
  V-Lab’s systemic risk measures help users identify vulnerabilities in the financial system and assess the potential impact of shocks. Our models capture the interconnectedness of financial institutions and markets, providing insights into the transmission of risk across sectors and regions. SRISK estimates the expected capital shortfall of financial institutions in a crisis scenario, making it a key tool for regulators and policymakers monitoring financial stability. Academics also use SRISK to study the buildup of systemic vulnerabilities across institutions, sectors, and countries.
• Long-Run Value at Risk
  V-Lab’s long-run Value at Risk (VaR) estimates provide a forward-looking measure of downside risk over extended horizons. These estimates help users assess tail risk and evaluate the potential impact of extreme events on their portfolios. LRVAR extends traditional VaR analysis to account for persistent volatility and long-horizon risk. It is especially useful for institutional investors, pension funds, and endowments that need to manage tail risk over multi-year horizons. It also provides insights into how shocks propagate and decay over time.
• Market Liquidity Monitoring
  V-Lab’s market liquidity measures help users assess the depth, breadth, and resilience of financial markets. Our models capture the impact of trading activity on asset prices and the cost of executing trades. These measures are used by market participants to gauge liquidity conditions, identify trading opportunities, and manage execution risk. They are also used by regulators and policymakers to monitor market functioning and detect signs of stress. V-Lab’s liquidity measures are especially valuable during periods of market turbulence, when liquidity can evaporate quickly and exacerbate price volatility.
• Fixed Income Forecasting
  V-Lab’s fixed income forecasting models provide real-time estimates of interest rates and yield curves. These forecasts help users assess the impact of monetary policy, inflation expectations, and credit risk on fixed income markets. They are used by investors to manage interest rate risk, evaluate bond valuations, and construct yield curve strategies. They are also used by policymakers to monitor financial conditions, support macroeconomic forecasting, and assess the effectiveness of monetary policy. Researchers use these forecasts to test term structure models and monetary policy transmission mechanisms.
• Climate Risk Indicators
  V-Lab’s climate risk indicators help users assess the financial implications of climate change and transition risks. Our models capture the exposure of assets and portfolios to the risks associated with climate change. These indicators are used by investors to evaluate climate-related risks in their portfolios, identify opportunities in sustainable investments, and align their strategies with climate goals. They are also used by regulators and policymakers to monitor climate-related risks in the financial system, support climate stress testing, and promote sustainable finance. Researchers use these indicators to study the financial implications of climate change, assess the materiality of climate risks, and develop climate-aware investment strategies.
• Common Volatility Risk Monitoring
  V-Lab’s common volatility risk measures help users assess the co-movement of asset prices and the transmission of volatility across markets. Our models capture the interconnectedness of asset volatility, providing insights into the common sources of risk that drive market dynamics. These measures are used by investors to identify diversification opportunities, manage portfolio risk, and hedge against common shocks. They are also used by regulators and policymakers to monitor market interdependencies, detect signs of contagion, and assess the resilience of financial markets.

Each of these tools is publicly available and continuously updated, reflecting our mission to democratize access to high-quality financial risk analytics. Whether you're an academic exploring new models, a policymaker monitoring systemic exposures, or an investor managing risk, V-Lab provides transparent and actionable data to inform your work.

Global Collaborations

V-Lab collaborates with leading institutions, researchers, and practitioners around the world to advance the understanding of financial market dynamics and risks. Our global partnerships enable us to leverage diverse expertise, data sources, and perspectives to enhance the quality and relevance of our research. By working with a network of collaborators, we aim to foster innovation, knowledge sharing, and best practices in financial risk management. Notably, our partnership with the Volatility Institute at NYU Shanghai focuses on research pertinent to Chinese financial markets, fostering international cooperation and knowledge exchange.

Our Team

The Volatility Laboratory is led by a team of dedicated researchers, data scientists, and technologists who are committed to advancing the understanding of financial market dynamics and risks. Our team members bring diverse expertise in financial econometrics, data analytics, and software development, enabling us to develop cutting-edge research and tools that inform decision-making by academics, market participants, and policymakers.

• Rob Engle
• Rob Capellini
• Brian Reis
• Gianluca DeNard
• Susana Campos-Martins

We also acknowledge the invaluable contributions of past team members and collaborators who have played a significant role in our journey.​

• Prab Agarwalla
• Mario D’Avirro
• Christian Brownlees
• Christian Conrad
• Hseu-Ming Chen
• Gauri Manglik
• Breno Neri
• Harshal Patil
• Gonzalo Rangel
• Michael Robles
• Guillaume Roussellet
• Tianyue Ruan
• Tal Safran
• Preethi Sampath
• Emil Siriwardane
• Michael Verrilli
• Norm White
• Robin Wurl