Updated February 2026 – Reading Time: ~8 minutes

Key Takeaways

• Market risk arises from market movements. Factors such as geopolitics, interest and exchange rate changes, and natural disasters can cause large asset price swings. Regulators now expect banks to model these extreme but plausible scenarios due to increased volatility.
• Value at Risk (VaR) remains a standard risk measure. VaR estimates the maximum expected loss for a portfolio over a chosen horizon and confidence level. It can be calculated using three methods—historical simulation, variance–covariance and Monte Carlo—each with tradeoffs.
• VaR has limitations. Different estimation methods can produce different results and VaR does not capture losses beyond the chosen percentile. It can also underestimate extreme market events.
• Expected Shortfall (ES) captures tail risk. ES, or conditional VaR, measures the average loss that exceeds the VaR threshold. Basel’s Fundamental Review of the Trading Book (FRTB) replaces VaR with ES and introduces varying liquidity horizons.
• Scenario analysis, stress testing and sensitivity measures complement VaR. Scenario and stress tests evaluate portfolio performance under extreme conditions. Sensitivity measures like beta, duration, delta and gamma quantify exposure to individual risk factors.
• Robust models need accurate data and hedging strategies. Effective market risk management depends on reliable input data, prudent assumptions and mitigation techniques such as diversification and derivatives hedging.

Introduction

Market risk—the potential for losses due to changes in market prices, interest rates, exchange rates or commodity prices—is a core concern for banks, asset managers and regulators. Following the 2007–2009 financial crisis, the Fundamental Review of the Trading Book (FRTB) raised the bar for market risk measurement by replacing Value at Risk (VaR) with Expected Shortfall (ES) for regulatory capital calculations. Understanding how market risk is modelled and measured helps financial professionals meet regulatory expectations and safeguard portfolios.

What Is Market Risk Modeling?

Market risk modeling refers to the quantitative process of estimating potential losses arising from market movements. Risk factors include geopolitical events, monetary and fiscal policy shifts, changes in interest and foreign exchange rates, terrorist events and natural disasters. As volatility has increased, regulators require banks to test portfolios against extreme but plausible scenarios.

Price Risk and Valuation Models

Price risk is the risk that a security’s value declines or is mispriced. Before investing, analysts use valuation models—such as the Discounted Cash Flow model for bonds or the Gordon Growth Model for dividendpaying equities—to estimate fair value. After investment, price risk can be mitigated through diversification and hedging using futures, options and swaps.

Value at Risk (VaR)

Value at Risk (VaR) estimates the maximum expected loss over a specified horizon at a given confidence level. For example, a oneweek VaR of $2 million at 95 % confidence means there is a 95 % probability the portfolio will not lose more than $2 million over one week. VaR is widely used in risk management because it provides a single, communicable number and facilitates capital allocation.

Methods to Estimate VaR

There are three primary methods for estimating VaR:

1. Historical simulation. This approach reorders actual historical returns and assumes the future will resemble the past. It captures real market events without making distributional assumptions, but its accuracy depends on the relevance of historical data.
2. Variance–covariance (parametric) method. Assuming returns follow a normal distribution, this method uses expected returns, variances and covariances to estimate VaR. It is computationally efficient but may underestimate risk for nonnormal or optionheavy portfolios.
3. Monte Carlo simulation. This technique generates thousands of random scenarios based on specified return distributions. It can handle complex instruments but requires significant computational resources and careful model design.

Advantages and Limitations of VaR

VaR condenses complex risk exposures into a single metric, making it easy to communicate and compare across portfolios. Regulatory frameworks historically relied on VaR, and it remains useful for performance evaluation and capital allocation. However, VaR has notable limitations:

• Subjective design. VaR depends on choices of confidence level, time horizon and data window, making results sensitive to assumptions.
• Tail ignorance. VaR does not capture losses beyond the chosen percentile; extreme events can be much worse.
• Distributional and correlation assumptions. The variance–covariance method assumes normality and can underestimate option risk. Calculating covariances for large portfolios is challenging and can produce inconsistent results.

Extensions of VaR

To address some shortcomings, risk managers use several VaR variations:

• Conditional VaR (CVaR or Expected Shortfall) – the average loss exceeding the VaR threshold.
• Incremental VaR (IVaR) – the change in portfolio VaR when adding or removing a position.
• Marginal VaR (MVaR) – the sensitivity of VaR to small changes in position size.

These measures provide insights into tail risk, marginal contributions and diversification benefits.

Expected Shortfall and the FRTB

Expected Shortfall (ES) is defined as the average loss beyond the VaR threshold. It answers the question: “If things get bad, how bad can losses get on average?” By focusing on the worst outcomes, ES captures tail risk more effectively than VaR.

The Fundamental Review of the Trading Book (FRTB)—part of the Basel III/IV reforms—replaces VaR and stressed VaR with Expected Shortfall for calculating market risk capital. Under FRTB:

• Banks must estimate ES at a 97.5 % confidence level and use varying liquidity horizons (10, 20, 60, 120 and 250 days) to reflect different asset liquidity profiles.
• Assets that fail eligibility criteria must be modelled using a standardised approach or through stress testing.
• The shift to ES is expected to increase market risk capital requirements by over 60 % for large banks.

FRTB’s adoption underscores regulators’ intention to capture extreme market events and discourage excessive risktaking.

Scenario Analysis, Stress Testing and Sensitivity Measures

Scenario analysis evaluates how a portfolio performs under extreme or hypothetical conditions. Risk managers may use historical scenarios, replaying past crises, or hypothetical scenarios, combining extreme movements across markets. Stress tests apply severe shocks to key risk factors (e.g., interest rates, credit spreads, exchange rates) to estimate potential losses and capital needs.

Sensitivity measures quantify how a security or portfolio responds to small changes in risk factors. Examples include:

• Beta – sensitivity of an equity to market movements.
• Duration and convexity – interest rate sensitivity for bonds.
• Option Greeks (delta, gamma, vega) – sensitivities of options to changes in underlying price, curvature and volatility.

These measures complement VaR and ES because they highlight exposures to specific risk factors and help design hedging strategies.

Best Practices in Market Risk Modeling

Robust market risk models share several characteristics:

• Comprehensive risk factor identification. Models should capture market, credit, liquidity and operational risks.
• Use of multiple models. Combining VaR, expected shortfall, stress tests and sensitivity analyses captures different aspects of risk.
• Accurate data and rigorous model validation. Reliable input data and regular backtesting or model validation are critical for credible results.
• Hedging and diversification. Position limits and hedging with derivatives such as futures, options and swaps help mitigate price volatility.
• Regulatory compliance. Understanding FRTB requirements and staying abreast of evolving standards ensure that models meet regulatory expectations.

Conclusion

Market risk modeling is essential for sound financial management, capital planning and regulatory compliance. Value at Risk provides a foundational measure but must be supplemented with Expected Shortfall, stress tests, scenario analyses and sensitivity measures to capture tail risks and diverse exposures. The FRTB’s shift from VaR to ES underscores the importance of modeling extreme events and managing liquidity horizons. By combining quantitative tools with accurate data and prudent risk management practices, organisations can better anticipate losses, allocate capital efficiently and navigate turbulent markets.

References

1. RMA Blog – Market Risk Modeling (risk factors, scenario analysis and need for extreme scenarios).
2. RMA Blog – Price risk, hedging strategies and discussion of VaR strengths and limitations.
3. CFA Institute – Summary of VaR definition, estimation methods and limitations.
4. Investopedia – Explanation of VaR calculation methods and mechanics.
5. Investopedia – Discussion of VaR advantages and limitations.
6. Confluence Technologies – Definition of Expected Shortfall (CVaR).
7. Bank Policy Institute – FRTB introduction, expected shortfall and capital impact.
8. AnalystPrep – FRTB summary: shift to expected shortfall, tail risk focus and liquidity horizons