Christophe Boucher

Learning by Failing: A Simple VAR Buffer

We study in this article the problem of model risk in VaR computations and document a procedure for correcting the bias due to specification and estimation errors. This practical method consists of learning from model mistakes, since it dynamically relies on an adjustment of the VaR estimates – based on a back-testing framework – such as the frequency of past VaR exceptions always matches the expected probability. We finally show that integrating the model risk into the VaR computations implies a substantial minimum correction to the order of 10–40% of VaR levels.

Forecasting without Persistence

The forecasting literature has identified three important and broad issues: the predictive content of explanatory variable is most of the times unstable over time, in-sample and out-of-sample results are often discordant and precise statistical inference with highly persistent predictors is challenging. In this paper, we simultaneously address these three issues, proposing to directly treat the persistence of forecasting variables before forecasting. We thus cut-out the low frequency components and show, in simulations and on real financial data, that this pre-treatment improves the predictive power of the predictor.

Testing the fed and the Graham & Dodd models: asymmetric vs. symmetric adjustment

We examine the empirical validity of the Fed model and the Grahamu2009&u2009Dodd model for five countries and over a time period spanning three decades by applying the Enders and Granger (1998) and Enders and Siklos (2001) threshold unit-root and cointegration tests. Our results support the hypothesis that the adjustment back to equilibrium is asymmetric.