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Are you accurately measuring your risk?
By Christian Kahl PhD | August 1, 2018

Over the last few years, buy-side firms have increased their use of securities with embedded options such as callable, puttable and convertible bonds, ETF’s and asset-backed securities. They have also begun to embrace derivatives for hedging their portfolios. When investing in securities with optionality or complex risk profiles, it is important to consider all aspects of the risk profile and how the addition impacts the risk profile of the entire portfolio. 

The most common, and perhaps easiest way to value a security is to only look at the market value, however this can be risky in two key ways:

  1. The market price and the inherent value can differ often due to supply and demand, bubbles and market sentiment 
  2. The price is only one piece of information and doesn’t show your secondary risks, e.g. interest rates, volatility, and currency effects. 

For this reason it is important to individually model each security using fundamental pricing techniques and break the security into its constituent parts. It is therefore common to monitor the ‘Mark to Model’ of each security. This approach also allows you to break down the risks in each component and look at the security as a whole to obtain a full understanding of the risk. The addition of the security to the portfolio can then be assessed more accurately. 

Embedded options can significantly distort the risk distribution of securities. For example, we have many clients who use callable bonds. If not looked at in detail, exposure on an underlying can quickly change in these types of securities. The effects of FX, and interest rates, if ignored, can then significantly affect the risk of the portfolio. However, if these risks are understood they can be hedged accordingly, therefore optimizing returns. 

It is very common for risk managers to use historical VaR when assessing risk. Time series is often used as a proxy to understand the risk of the security. But, as we all know, ‘past performance is not an indicator of future returns’. With a significant part of the risk of the security being linked to the price of the embedded option, the ‘moneyness’ of the option will have a significant effect on the value of the security. 

To more accurately look at the risk of these securities, it is important to first revalue the instrument, not simply take the market price, and second, break down each risk factor in relation to the delta (or ‘moneyness’ of the embedded option).  It is then possible to run your VaR more accurately using either or both:

  • Historical VaR - Take the time series of risk factors 
  • Monte-Carlo VaR - Simulate the risk factors based on their statistics

Inaccurate risk measurement, especially when dealing with securities with embedded options, can cause a build-up of exposure to an underlying or risk factor without you realizing it. Using a more detailed analysis and breakdown of risk for each security can highlight pockets of risk and then allow for more accurate investment decisions. Understanding the portfolio accurately helps improve both hedging and investment decisions—ultimately maximizing returns for the investor. 

About the author
Christian Kahl PhD
Christian Kahl PhD
Director, Head of Client Services | FINCAD

Christian is responsible for all client facing quantitative topics and resources globally. In his role, he deals with a wide spectrum of financial industry clients from asset management, hedge funds and insurance companies to accounting firms and sell-side institutions. Additionally, Christian has extensive expertise dealing with regulatory and accounting requirements across all market segments.    

Before joining FINCAD in spring 2015, Christian was the deputy-head of Financial Engineering in the Equity Market and Commodity department of Commerzbank, where he worked for five years supporting various trading desks. Christian Kahl has 10 years experience as a quant in sell-side institutions implementing models in cross asset front office pricing libraries. His quantitative research is largely focused on stochastic volatility modelling and high performance computing.