Backtesting VaR models:a two-stage procedure

Timotheos Angelidis, Stavros Degiannakis

Abstract

ABSTRACT

Academics and practitioners have extensively studied value-at-risk (VaR) in order to propose a unique risk management technique that generates accurate VaR estimations for long and short trading positions. However, they have not succeeded yet as the developed testing frameworks have not been widely accepted. A two-stage backtesting procedure is proposed in order to enable a model that not only forecasts VaR but also predicts the loss beyond VaR to be selected. Numerous conditional volatility models that capture the main characteristics of asset returns (asymmetric and leptokurtic unconditional distribution of returns, power transformation and fractional integration of the conditional variance) under four distributional assumptions (normal, generalized error, Student-t and skewed Student-t ) have been estimated to find the best model for three financial markets (US stock, gold and dollar–pound exchange rate markets), long and short trading positions and two confidence levels. By following this procedure, the risk manager can significantly reduce the number of competing models.