A Conditional Extreme Value Theory Approach in Value-at-Risk Forecasting: Evidence from Southeastern Europe and USA market
Abstract
As a consequence of the recent financial crisis, the adequacy of different Value-at-Risk (VaR) methodologies was heavily questioned. Current practice in VaR assessment relies on modeling the whole distribution of returns. As an alternative, in this paper we model tail behavior of returns, and thus VaR, using conditional Extreme Value Theory (EVT), which combines EVT and GARCH methodology. Moreover, we examine the performance of conditional EVT with the daily returns of seven stock market indices, of which six are from Southeastern Europe (BelexLine, BET, BUX, CROBEX, SBITOP, SOFIX) from the period of September 2004 - April 2013, and one from USA market (Standard&Poors 500 Index) from the period January 1998 - April 2013. Backtesting of historical daily returns proves that conditional EVT model gives good predictions for all indices and for all confidence levels.
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