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

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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Bibliographic Details
Main Authors: Michael Grabchak, Eliana Christou
Format: Article
Language:English
Published: SpringerOpen 2021-06-01
Series:Financial Innovation
Subjects:
Expected shortfall
Stochastic volatility
Value-at-risk
Online Access:https://doi.org/10.1186/s40854-021-00254-0
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spelling doaj-749760341edc422da6292602cbdefb3f2021-06-13T11:26:30ZengSpringerOpenFinancial 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
collection DOAJ
language English
format Article
sources DOAJ
author Michael Grabchak
Eliana Christou
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
author_facet Michael Grabchak
Eliana Christou
author_sort Michael Grabchak
title 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_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_sort note on calculating expected shortfall for discrete time stochastic volatility models
publisher SpringerOpen
series Financial Innovation
issn 2199-4730
publishDate 2021-06-01
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.
topic Expected shortfall
Stochastic volatility
Value-at-risk
url https://doi.org/10.1186/s40854-021-00254-0
work_keys_str_mv AT michaelgrabchak anoteoncalculatingexpectedshortfallfordiscretetimestochasticvolatilitymodels
AT elianachristou anoteoncalculatingexpectedshortfallfordiscretetimestochasticvolatilitymodels
AT michaelgrabchak noteoncalculatingexpectedshortfallfordiscretetimestochasticvolatilitymodels
AT elianachristou noteoncalculatingexpectedshortfallfordiscretetimestochasticvolatilitymodels
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