Pitfalls in backtesting Historical Simulation VaR models

Pei Pei, Juan Carlos Escanciano Coders: Pei Pei, Juan Carlos Escanciano

Code and Data Abstract

The dataset contains the returns for three portfolios based on three representative US stocks traded on the New York Stock Exchange (NYSE). The stocks are Walt Disney (DIS), General Electric (GE) and Merck & Company (MRK). Daily data on their market closure prices9 are collected over the period of 01/04/1999–12/31/2009, and then the daily returns are calculated as 100 times the difference of the log prices. The compositions of the three portfolios considered are (0.4, 0.1, 0.5), (0.1, 0.1, 0.8) and (0.3, 0.1, 0.6), respectively, where the numbers in each parentheses from left to right represent the portfolio weights on DIS, GE and MRK, respectively. These weights are chosen for illustrative purposes but

Paper Abstract

Abstract Historical Simulation (HS) and its variant, the Filtered Historical Simulation (FHS), are the most popular Value-at-Risk forecast methods at commercial banks. These forecast methods are traditionally evaluated by means of the unconditional backtest. This paper formally shows that the unconditional backtest is always inconsistent for backtesting HS and FHS models, with a power function that can be even smaller than the nominal level in large samples. Our findings have fundamental implications in the determination of market risk capital requirements, and also explain Monte Carlo and empirical findings in previous studies. We also propose a data-driven weighted backtest with good power properties to evaluate HS and FHS forecasts. A Monte Carlo study and an empirical application with three US stocks confirm our theoretical findings. The empirical application shows that multiplication factors computed under the current regulatory framework are downward biased, as they inherit the inconsistency of the unconditional backtest.

Pei Pei, Juan Carlos Escanciano Coders: Pei Pei, Juan Carlos Escanciano, et al. "Pitfalls in backtesting Historical Simulation VaR models." Journal of Banking and Finance (2012).     Retrieved 06/24/2019 from researchcompendia.org/compendia/2013.221/

Page Owner

sheila@codersquid.com

created 11/12/2013

modified 01/16/2014

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