Cifter, Atilla2024-05-252024-05-2520110378-43711873-211910.1016/j.physa.2011.02.0332-s2.0-79954602201https://doi.org/10.1016/j.physa.2011.02.033https://hdl.handle.net/20.500.14517/542Cifter, Atilla/0000-0002-4365-742XThis 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. (C) 2011 Elsevier B.V. All rights reserved.eninfo:eu-repo/semantics/closedAccessExtreme value theoryWavelet-based extreme value theoryEmerging marketsArticleQ13901223562367WOS:00029051090001530