|
Activity Number:
|
119
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Monday, August 3, 2009 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Section on Risk Analysis
|
| Abstract - #303358 |
|
Title:
|
Evaluating Value-at-Risk Models via Quantile Regressions
|
|
Author(s):
|
Luiz Renato Lima*+ and Wagner Gaglianone and Oliver Linton
|
|
Companies:
|
University of Illinois at Urbana-Champaign and Central Bank of Brazil and London School of Economics
|
|
Address:
|
Department of Economics, Urbana, IL, 61801,
|
|
Keywords:
|
risk ; quantile regression ; VaR
|
|
Abstract:
|
This paper is concerned with evaluating value at risk estimates. It is well known that using only binary variables to do this sacrifices too much information. However, most of the specification tests (also called backtests) available in the literature, such as Christoffersen (1998) and Engle and Maganelli (2004) are based on such variables. In this paper we propose a new backtest that does not rely solely on binary variable. It is shown that the new backtest provides a sufficient condition to assess the performance of a quantile model whereas the existing ones do not. The proposed methodology allows us to identify periods of an increased risk exposure based on a quantile regression model (Koenker & Xiao, 2002). Our theoretical findings are corroborated through a Monte Carlo simulation and an empirical exercise with daily S&P500 time series.
|
