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Tail-Related Risk Measurement and Forecasting in Equity Markets

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Abstract

Parametric, simulation-based and hybrid methods are utilized to estimate various risk measures such as Value-at-Risk (VaR), Conditional VaR and coherent Expected Shortfall. An exhaustive backtesting analysis is performed for London’s FTSE 100 index and a comparative evaluation of the predictability of the investigated models is performed with the use of various statistical tests. We show that optimal tail risk forecasting necessitates that many factors be considered such as asset structure and capitalization and specific market conditions i.e., normal or crisis periods. Specifically, for large capitalization stocks and long investment horizons parametric modeling accounted for relatively better risk estimation in normal quantiles, whilst for short-term trading strategies, the non-parametric methods are more suitable for measuring extreme tail risk of small-cap stocks.

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Correspondence to Stelios Bekiros.

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We are grateful to Dietmar Maringer and Alexandros Potamianos for valuable suggestions. Moreover, we thank the faculty members at the Economics Department of the European University Institute (EUI) for helpful comments and discussions. The usual disclaimers apply.

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Bekiros, S., Loukeris, N., Eleftheriadis, I. et al. Tail-Related Risk Measurement and Forecasting in Equity Markets. Comput Econ 53, 783–816 (2019). https://doi.org/10.1007/s10614-017-9766-5

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