Beyond Value at Risk: Forecasting Portfolio Loss at Multiple Horizons

Lisa R. Goldberg

University of California, Berkeley; Aperio Group

Guy Miller

Merrill Lynch & Co.

Jared Weinstein

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: G00

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Date posted: May 15, 2008  

Suggested Citation

Goldberg, Lisa R. and Miller, Guy and Weinstein, Jared, Beyond Value at Risk: Forecasting Portfolio Loss at Multiple Horizons. Available at SSRN: http://ssrn.com/abstract=1132357

Contact Information

JOIM Editor (Contact Author)
Journal of Investment Management (JOIM) ( email )
3658 Mt. Diablo Blvd.
Suite 200
Lafayette, CA 94549
United States
925-299-7800 (Phone)
925-299-7815 (Fax)
Lisa R. Goldberg
University of California, Berkeley ( email )
Department of Statistics
367 Evans Hall
Berkeley, CA 94720-3860
United States
Aperio Group ( email )
3 Harbor Drive
Suite 315
Sausalito, CA 94965
United States
Guy Miller
Merrill Lynch & Co. ( email )
World Financial Center - North Tower
19th Floor
New York, NY 10281-1319
United States
Jared Weinstein
University of California, Los Angeles (UCLA) ( email )
405 Hilgard Avenue
Box 951361
Los Angeles, CA 90095
United States
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