کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
978186 | 933259 | 2011 | 12 صفحه PDF | دانلود رایگان |

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.
► This paper introduces wavelet-based extreme value theory.
► Wavelets and EVT are combined for volatility forecasting to estimate a hybrid model.
► Wavelet-based extreme value theory is applied on two major emerging stock markets.
► It is found that this new model increases predictive performance of forecasting.
Journal: Physica A: Statistical Mechanics and its Applications - Volume 390, Issue 12, 15 June 2011, Pages 2356–2367