Frontiers in VaR forecasting and backtesting

The interest in forecasting the Value at Risk (VaR) has been growing over the last two decades, due to the practical relevance of this risk measure for financial and insurance institutions. Furthermore, VaR forecasts are often used as a testing ground when fitting alternative models for representing the dynamic evolution of time series of financial returns. There are vast numbers of alternative methods for constructing and evaluating VaR forecasts. In this paper, we survey the new benchmarks proposed in the recent literature.Financial support from Project ECO2012-32401 by the Spanish Government is gratefully acknowledged by the second author. We are also grateful to the Editor Rob Hyndman for his support and to three anonymous reviewers for their detailed and constructive comments

Direct versus iterated multi-period Value at Risk

Although the Basel Accords require financial institutions to report daily predictions ofValue at Risk (VaR) computed using ten-day returns, a vast part of the literature deals withVaR predictions based on one-day returns. From the practitioner point of view, some ofthe conclusions about the best methods to estimate one-period VaR could not be directlygeneralized to multi-period VaR. Consequently, in the context of two-step VaR predictors,we use simulated and real data to compare direct and iterated predictions of multi-periodVaR based on ten-day returns assuming that the conditional variances of one-period returnsfollow a GARCH-type model. We show that multiperiod VaR predictions based on iteratingan asymmetric GJR model with normal or bootstrapped errors are often preferred whencompared with direct methods that are often biased and inefficient