Abstract

http://ssrn.com/abstract=424363
 
 

References (18)



 
 

Citations (5)



 


 



Estimation Error in the Assessment of Financial Risk Exposure


Stephen Figlewski


New York University - Stern School of Business

June 29, 2003

EFA 2003 Annual Conference Paper No. 799

Abstract:     
Value at Risk and similar measures of financial risk exposure require predicting the tail of an asset returns distribution. Assuming a specific form, such as the normal, for the
distribution, the standard deviation (and possibly other parameters) are estimated from recent historical data and the tail cutoff value is computed. But this standard procedure
ignores estimation error, which we find to be substantial even under the best of conditions. In practice, a "tail event" may represent a truly rare occurrence, or it may simply be a not-so-rare occurrence at a time when the predicted volatility underestimates the true volatility, due to sampling error. This problem gets worse the further in the tail one is trying to predict.

Using a simulation of 10,000 years of daily returns, we first examine estimation risk when volatility is an unknown constant parameter. We then consider the more realistic, but more problematical, case of volatility that drifts stochastically over time. This substantially increases estimation error, although strong mean reversion in the variance tends to dampen the effect. Non-normal fat-tailed return shocks makes overall risk assessment much worse, especially in the extreme tails, but estimation error per se does not add much beyond the effect of tail fatness. Using an exponentially weighted moving average to downweight older data hurts accuracy if volatility is constant or only slowly changing. But with more volatile variance, an optimal decay rate emerges, with better performance for the most extreme tails being achieved using a relatively greater rate of
downweighting.

We first simulate non-overlapping independent samples, but in practical risk management, risk exposure is estimated day by day on a rolling basis. This produces strong autocorrelation in the estimation errors, and bunching of apparently extreme events. We find that with stochastic volatility, estimation error can increase the probabilities of multi-day events, like three 1% tail events in a row, by several orders of magnitude. Finally, we report empirical results using 40 years of daily S&P 500 returns which confirm that the issues we have examined in simulations are also present in the real world.

Number of Pages in PDF File: 48

working papers series


Download This Paper

Date posted: July 23, 2003  

Suggested Citation

Figlewski, Stephen, Estimation Error in the Assessment of Financial Risk Exposure (June 29, 2003). EFA 2003 Annual Conference Paper No. 799. Available at SSRN: http://ssrn.com/abstract=424363 or http://dx.doi.org/10.2139/ssrn.424363

Contact Information

Stephen Figlewski (Contact Author)
New York University - Stern School of Business ( email )
44 West 4th Street
Department of Finance Suite 9-160
New York, NY 10012-1126
United States
212-998-0712 (Phone)
212-995-4220 (Fax)
Feedback to SSRN


Paper statistics
Abstract Views: 2,365
Downloads: 360
Download Rank: 40,136
References:  18
Citations:  5

© 2014 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright   Contact Us
This page was processed by apollo1 in 0.907 seconds