Wavelet based Filtered historical simulation in different time horizons in Tehran Stock Exchange

Abstract

Expected Shortfall (ES) is the average return on a risky asset conditional on the return being below some quantile of its distribution, namely its Value-at-Risk (VaR). The Basel III Accord, which will be implemented in the years leading up to 2023, places new attention on ES, but unlike VaR, there is little existing work on modeling ES. This research seeks to introduce the most accurate and reliable models to estimate Expected shortfall among both conventional and advanced models including Historical simulation model, ARMA-GARCH model, Filtered historical simulation model, Conditional extreme value theory model and a new hybrid semiparametric model called "Wavelet based Filtered historical simulation" in the Tehran Stock Exchange. Based on the results of research, using the 11-year daily main index of Tehran Stock Exchange return from 2010/4/7 to 2020/4/1 (more than 2400 daily of index return data) and applying various backtesting method, the proposed new wavelet based Filtered historical simulation hybrid model has outperforms other competing models in all time horizons and confidence levels.

Keywords

• expected shortfall
• wavelet theory
• conditional extreme value theory model
• wavelet based filtered historical simulation
• backtest