A note on calculating expected shortfall for discrete time stochastic volatility models

Abstract In this paper we consider the problem of estimating expected shortfall (ES) for discrete time stochastic volatility (SV) models. Specifically, we develop Monte Carlo methods to evaluate ES for a variety of commonly used SV models. This includes both models where the innovations are independ...

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Main Authors: Michael Grabchak, Eliana Christou
Format: Article
Language:English
Published: SpringerOpen 2021-06-01
Series:Financial Innovation
Subjects:
Online Access:https://doi.org/10.1186/s40854-021-00254-0
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author Michael Grabchak
Eliana Christou
author_facet Michael Grabchak
Eliana Christou
author_sort Michael Grabchak
collection DOAJ
description Abstract In this paper we consider the problem of estimating expected shortfall (ES) for discrete time stochastic volatility (SV) models. Specifically, we develop Monte Carlo methods to evaluate ES for a variety of commonly used SV models. This includes both models where the innovations are independent of the volatility and where there is dependence. This dependence aims to capture the well-known leverage effect. The performance of our Monte Carlo methods is analyzed through simulations and empirical analyses of four major US indices.
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spelling doaj.art-749760341edc422da6292602cbdefb3f2022-12-21T22:10:46ZengSpringerOpenFinancial Innovation2199-47302021-06-017111610.1186/s40854-021-00254-0A note on calculating expected shortfall for discrete time stochastic volatility modelsMichael Grabchak0Eliana Christou1Department of Mathematics and Statistics, University of North Carolina at CharlotteDepartment of Mathematics and Statistics, University of North Carolina at CharlotteAbstract In this paper we consider the problem of estimating expected shortfall (ES) for discrete time stochastic volatility (SV) models. Specifically, we develop Monte Carlo methods to evaluate ES for a variety of commonly used SV models. This includes both models where the innovations are independent of the volatility and where there is dependence. This dependence aims to capture the well-known leverage effect. The performance of our Monte Carlo methods is analyzed through simulations and empirical analyses of four major US indices.https://doi.org/10.1186/s40854-021-00254-0Expected shortfallStochastic volatilityValue-at-risk
spellingShingle Michael Grabchak
Eliana Christou
A note on calculating expected shortfall for discrete time stochastic volatility models
Financial Innovation
Expected shortfall
Stochastic volatility
Value-at-risk
title A note on calculating expected shortfall for discrete time stochastic volatility models
title_full A note on calculating expected shortfall for discrete time stochastic volatility models
title_fullStr A note on calculating expected shortfall for discrete time stochastic volatility models
title_full_unstemmed A note on calculating expected shortfall for discrete time stochastic volatility models
title_short A note on calculating expected shortfall for discrete time stochastic volatility models
title_sort note on calculating expected shortfall for discrete time stochastic volatility models
topic Expected shortfall
Stochastic volatility
Value-at-risk
url https://doi.org/10.1186/s40854-021-00254-0
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