Value-at-risk estimation with wavelet-based extreme value theory: Evidence from emerging markets

Atilla Cifter

Physica A: Statistical Mechanics and its Applications, 2011, vol. 390, issue 12, 2356-2367

Abstract: This paper introduces wavelet-based extreme value theory (EVT) for univariate value-at-risk estimation. Wavelets and EVT are combined for volatility forecasting to estimate a hybrid model. In the first stage, wavelets are used as a threshold in generalized Pareto distribution, and in the second stage, EVT is applied with a wavelet-based threshold. This new model is applied to two major emerging stock markets: the Istanbul Stock Exchange (ISE) and the Budapest Stock Exchange (BUX). The relative performance of wavelet-based EVT is benchmarked against the Riskmetrics-EWMA, ARMA–GARCH, generalized Pareto distribution, and conditional generalized Pareto distribution models. The empirical results show that the wavelet-based extreme value theory increases predictive performance of financial forecasting according to number of violations and tail-loss tests. The superior forecasting performance of the wavelet-based EVT model is also consistent with Basel II requirements, and this new model can be used by financial institutions as well.

Keywords: Extreme value theory; Wavelet-based extreme value theory; Emerging markets (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:390:y:2011:i:12:p:2356-2367

DOI: 10.1016/j.physa.2011.02.033

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