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Stress tests are risk management tools widely used by both institutional investment managers and regulatory authorities. While annual Dodd-Frank Act stress tests of large financial institutions garner lots of media coverage, the same tools are applied by portfolio managers, compliance officers and risk analysts on a daily basis to analyze portfolio performance and gauge sensitivity to adverse market scenarios.

A number of methods have evolved to help investment firms implement realistic stress tests. This Insight examines the two most widely used tools, Value at Risk (VaR) and Stress Testing, and discusses their applicability and limitations. While there is an active debate among practitioners and academics about the relative merits and weaknesses of each tool, this misses the point that VaR and Stress Testing are largely complementary. Knowing how and when to use each tool is key to implementing effective risk management policies.

Value at Risk calculates the worst case loss over a given time period that won’t be exceeded with a given level of confidence. For example, a daily VaR of $10 million at the 99% confidence level means the investment manager can expect a loss greater than $10 million one out of every 100 trading days. The single, dollar denominated value makes VaR easy to understand. But VaR has some important limitations that constrain its use to ‘normally functioning’ markets. VaR doesn’t provide information on the direction of portfolio exposure and it can’t quantify the potential magnitude of losses when a loss threshold is exceeded. Depending on how VaR is implemented, it may use simplifying assumptions about cross-asset correlations or security returns’ distributions that often break down during times of market stress.

Stress Testing takes a very different, non-statistical approach, using a technique called Scenario Analysis. This makes it an ideal complement to VaR for analyzing portfolio risk. Scenario Analysis determines the effect of extreme but realistic events on a portfolio’s performance by constructing detailed scenarios of different market conditions. The scenario to be analyzed consists of one or more stress factors, including interest rate and FX shifts, credit spread changes, inflation shocks, prepayments and equity market movements. Scenario Analysis requires no historical data or statistical calculations, nor is it bound by historical correlations between asset classes. It is, however, highly dependent on the assumptions used to define the scenarios. It also requires extensive computational resources to calculate and analyze the scenarios typically formulated to robustly stress test a large, multi-asset portfolio.

The Charles River Investment Management Solution provides the front and middle office with extensive capabilities for analyzing market and credit risk, embedded directly in the portfolio manager workbench. Leveraging Charles River’s comprehensive foundational data sources ensures that all analyses are based on a single, complete and accurate data set. By gaining visibility into exposures and actionable risk metrics throughout the investment process, portfolio managers can make timelier and more informed asset allocation and hedging decisions.

Designed for performance and scalability, the solution supports VaR and Scenario Analysis for large, multi-asset class portfolios in near real-time, a necessity in fast moving markets. PMs can see which sectors and categories contribute most too overall VaR and which positions they might liquidate to reduce VaR. Scenario Analysis capabilities support either parallel or custom interest rate, credit, FX, equity and inflation shifts. Portfolio managers can analyze the effectiveness of their hedging strategies and see how proposed trades react under stressed conditions. Charles River also supports Expected Shortfall, a risk metric that quantifies the potential magnitude of losses when a VaR loss threshold is exceeded.

The growing importance of risk management in the front office requires solutions capable of providing comprehensive and actionable risk metrics, coupled with the scalability and performance needed to handle large portfolios and computationally intensive risk calculations and scenarios.

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