A Portfolio Manager Questions a Risk Engine IV:
Sensitivity Analysis in Investment Risk Modeling

Yannis Sardis, 19 April 2021

Quantitative sensitivity analysis is a prerequisite of advanced investment model building, as a quality assurance contributor of any simulation framework with multiple parameters.

We continue our Q&A session between a PM and our risk engine, KlarityRisk (KR), focusing on the ability of an investor to drill down to the core sources of a portfolio’s risk exposure. We recall that the aim of our notes is to ensure that a money manager’s ongoing pursue for out-performance does not unwillingly create uncontrollable long term portfolio risks.

PM: I manage a multi-asset-class portfolio; I would like to assess how sensitive it is to the various market movements. Can I accomplish this via the use of a risk engine?

KR: Our system provides you with an extensive list of sensitivity analysis metrics that can be utilized right out of the box. The optimal choice of the subset of metrics to be used depends mainly on the composition of your portfolio. More specifically, let us answer your questions:

PM: How sensitive is my Fixed Income portfolio to a movement in Interest Rates?

KR: The 1st step is to assess the portfolio’s first-order sensitivity to interest rates. This is estimated with Duration, which measures the price sensitivity of a bond or other fixed income instrument to changes in interest rates. The portfolio’s Modified Duration will give you the nominal amount of the portfolio’s Market Value for each one basis point change in interest rates. The 2nd step is the portfolio’s second-order sensitivity to interest rates, measured by Convexity, to enable you estimate the change of the portfolio’s Duration with regards to a marginal movement in the interest rate term structure.

PM: How can I assess the sensitivities of my portfolio’s Options component?

KR: The Option Greeks are measures of the sensitivity of an option price to several market factors, such as the: Delta (option price sensitivity to price changes of the underlying asset), Vega (option price sensitivity to the market volatility of the underlying asset), Theta (option price sensitivity to the passage of time), Rho (option price sensitivity to interest rate changes movements), and Gamma (rate of change in the option Delta with respect to changes in the price of the underlying asset).

PM: How does my portfolio’s volatility compare to that of the market’s? In other words, how sensitive is my portfolio to the market volatility as a whole?

KR: The portfolio’s Beta, calculated by the system, is a measure of the volatility, or systematic risk, of an individual instrument in comparison to the unsystematic risk of the entire market. In statistical terms, Beta represents the slope of the line through a regression of data points from an individual asset’s returns against those of the market.

PM: How can I monitor the divergence of my portfolio’s performance and risk from that of the Benchmark determined by the mandate’s investment policy?

KR: The Tracking Error, calculated by the system, measures the divergence between the price behavior of a portfolio and that of a particular (user-defined) Benchmark. It is frequently applied in the context of a hedge fund, mutual fund or Exchange-Traded Fund (ETF), the core strategy of which aims to track a representative market index.

PM: Is there a metric that I can use for assessing how my portfolio’s Value at Risk (VaR) would be affected if I increased the allocation of a portfolio component?

KR: Yes, this can be estimated using the Marginal VaR (MVaR) which is a VaR derivative measure. The MVaR allows you to estimate the reaction of the portfolio’s Total VaR to an additional dollar (or reference currency) exposure to a particular portfolio holding.

PM: As a summary of our discussion, remind me the range of risk metrics you can provide.

KR: Our risk engine encompasses a wide arsenal of risk metrics, including Value at Risk (VaR), Conditional VaR, Component VaR (Expected Shortfall), Volatility, Tracking Error, Beta, Diversification Benefit, Downside Deviation, Maximum Drawdown, Sharpe Ratio, Sortino Ratio, Duration, Convexity, Kurtosis, Skewness and Option Greeks.

All metrics are calculated at the portfolio or portfolio group or modeled strategy level, and at all levels of granularity such as geography, currency, sector and security level, to provide a detailed view of the portfolio’s sensitivity to all its factors and fundamental sources of risk.

Our next session will discuss how a fully automated Risk Limits Management module can ensure the compliance of your investment strategies with the ongoing regulatory rules and internal policy guidelines.

FINVENT Software Solutions is a provider of financial software applications and custom engineering services. The award-winning KlarityRisk platform specializes in investment risk analytics and fixed income performance attribution reporting and it is offered to Private Wealth institutions, Asset Managers, Hedge Fund Managers and Family Offices.

Would you like to know more about our Portfolio and Risk Management solutions? Please contact us at info@finvent.com and we will get in touch.