Measuring Downside Portfolio Risk

Abstract

Value at risk (VaR) is an approach used in risk management to measure downside risk. Not all VaRs, however, are created equal. Defining and accurately measuring market risk is a considerable task. VaR estimates depend on a number of inputs, including assumptions, data parameters, and methodology. Accordingly, comprehending the optimal use of various input and how they might impact the VaR forecast is necessary to avoid biasing portfolio risk estimates. The authors examine three equity portfolios of varying degrees of diversification, using twenty common VaR models developed through four VaR techniques to clearly demonstrate the ramifications of using inappropriate models. They find that particular characteristics of a portfolio must guide and determine which VaR model may be applied in order to extract accurate VaR estimates. Using the wrong VaR model will bias the behavior of portfolio managers and cause them to be exposed to much more risk than they desire.