Scenario-based principal component value-at- risk when the underlying risk factors are skewed and heavy-tailed: an application to Italian banks' interest rate risk exposure

Roberta Fiori and Simonetta Iannotti

Abstract

ABSTRACT

The paper develops a value-at-risk (VAR) methodology to assess Italian banks’ interest rate risk exposure. By using five years of daily data, the exposure is evaluated through a principal component VAR based on Monte Carlo simulation according to a parametric and a non-parametric approach. The main contribution of the paper is a methodology for modeling interest rate changes when underlying risk factors are skewed and heavy-tailed. The comparison of results with those resulting from the Basel II standardized approach shows that Basel II gives an adequate description of risk, provided that duration parameters are changed to reflect market conditions. Finally, the methodology is used to perform a stress testing analysis.