Beyond Value at Risk: Forecasting Portfolio Loss at Multiple Horizons
Lisa R. Goldberg
University of California at Berkeley
Merrill Lynch & Co.
University of California, Los Angeles (UCLA)
Journal of Investment Management, Vol. 6, No. 2, Second quarter 2008
We develop a portfolio risk model that uses high-frequency data to forecast the loss surface, which is the set of loss distributions at future time horizons. Our model uses a fully automated, semi-parametric fitting procedure that has its basis in extreme value statistics. We take account of distributional asymmetry, heavy tails, heteroscedasticity and serial correlation. Loss distributions are time aggregated by taking products of characteristic functions. We test loss-surface-implied forecasts of value at risk and expected shortfall out of sample on a diverse set of portfolios and we compare our forecasts to industry-standard risk forecasts that are based on asset and factor covariance matrices. The empirical results make a compelling case for the application and further development of our approach.
Keywords: Extreme risk, loss surface, expected shortfall, peaks over thresholds, temporal risk aggregation
JEL Classification: G00Accepted Paper Series
Date posted: May 15, 2008
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