Post by Sapphire Capital on Jul 11, 2008 22:03:43 GMT 4
Beyond Value at Risk: Forecasting Portfolio Loss at Multiple Horizons
LISA R. GOLDBERG
MSCI Barra
GUY MILLER
Merrill Lynch & Co.
JARED WEINSTEIN
University of California, Los Angeles
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Journal of Investment Management, Vol. 6, No. 2, Second quarter 2008
Abstract:
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.
papers.ssrn.com/sol3/papers.cfm?abstract_id=1132357
LISA R. GOLDBERG
MSCI Barra
GUY MILLER
Merrill Lynch & Co.
JARED WEINSTEIN
University of California, Los Angeles
--------------------------------------------------------------------------------
Journal of Investment Management, Vol. 6, No. 2, Second quarter 2008
Abstract:
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.
papers.ssrn.com/sol3/papers.cfm?abstract_id=1132357