Journal of Risk

Estimation risk for value-at-risk and expected shortfall

Paul Kabaila and Rheanna Mainzer

  • Exact Value-at-Risk (VaR) and Expected Shortfall (ES) risk measures cannot be found because of parameter estimation errors.
  • Consequently, approximate VaR and ES risk measures are used.
  • The difference between approximate and exact risk measures is found to be substantial.

For a given time series of daily losses that display volatility clustering, the exact next- day and ten-day value-at-risk (VaR) and expected shortfall (ES) are unknown. The usual procedure is to approximate these values by replacing true parameter values with estimates in the formulas for VaR and ES. Parameter estimation errors for a GARCH(1,1) model for this time series lead to approximate VaR and ES that differ from the exact VaR and ES, respectively. Accurate estimation of the VaR and ES is very important for the proper management of financial risks. In this paper, we find linear regression models in which the response variable is the approximate VaR (ES) and the explanatory variable is the exact VaR (ES). We use these linear regression models to determine the properties of the approximate VaR (ES), conditional on the corresponding exact value. For a given value of the exact VaR (ES), the approximate VaR (ES) is close to being an unbiased estimator of the corresponding exact value, but it may differ from this exact value by more than 10% of the exact value with substantial probability.

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to View our subscription options

You need to sign in to use this feature. If you don’t have a account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here