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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Format: | Article |
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SpringerOpen
2021-06-01
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Series: | Financial Innovation |
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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. |
first_indexed | 2024-12-17T00:13:35Z |
format | Article |
id | doaj.art-749760341edc422da6292602cbdefb3f |
institution | Directory Open Access Journal |
issn | 2199-4730 |
language | English |
last_indexed | 2024-12-17T00:13:35Z |
publishDate | 2021-06-01 |
publisher | SpringerOpen |
record_format | Article |
series | Financial Innovation |
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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