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The impact of estimation frequency on Value at Risk (VaR) and Expected Shortfall (ES) forecasts: an empirical study on conditional extreme value models

This study investigates extreme market events which occur in the tails of a distribution. The extreme events occur with a very low probability, but with significant consequences, which is what makes them of interest. In this study 20 years of data from both the S&P 500 and the JSE All Share index ha...

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Main Author: Coyne, Alice Elizabeth
Other Authors: Clark, Allan
Format: Thesis
Language:English
Published: Department of Statistical Sciences 2021
Subjects:
Mathematical Statistics
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access_status_str Open Access
author Coyne, Alice Elizabeth
author2 Clark, Allan
author_browse Clark, Allan
Coyne, Alice Elizabeth
author_facet Clark, Allan
Coyne, Alice Elizabeth
author_sort Coyne, Alice Elizabeth
collection Thesis
description This study investigates extreme market events which occur in the tails of a distribution. The extreme events occur with a very low probability, but with significant consequences, which is what makes them of interest. In this study 20 years of data from both the S&P 500 and the JSE All Share index have been used. An extreme value approach has been taken to quantify the risks associated with extreme market events. To achieve this a two phased process is used to calculated the Value at Risk and Expected Shortfall. The first phase involved running the daily returns through the GARCH model, and then extracting the residuals. The second phase involves using the Block Maxima Method, or Peaks over Threshold method to fit the residuals to the Generalized Extreme Value Distribution or the Generalized Pareto Distribution. Finally, the impact of estimation frequency is considered for each of the models. In conclusion, taking an extreme value approach to provide a statistically sound method to calculate risk, even when the parameters of the model are updated less frequently, this is preferable to simpler models where the parameter estimates are updated daily.
format Thesis
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institution University of Cape Town (South Africa)
language eng
last_indexed 2026-06-10T12:42:25.361Z
license_str Not specified — see source repository
provenance_str_mv Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository
publishDate 2021
publishDateRange 2021
publishDateSort 2021
publisher Department of Statistical Sciences
publisherStr Department of Statistical Sciences
record_format dspace
source_str UCTD — University of Cape Town Open Access Repository
spelling oai:open.uct.ac.za:11427/32558 The impact of estimation frequency on Value at Risk (VaR) and Expected Shortfall (ES) forecasts: an empirical study on conditional extreme value models Coyne, Alice Elizabeth Clark, Allan Mathematical Statistics This study investigates extreme market events which occur in the tails of a distribution. The extreme events occur with a very low probability, but with significant consequences, which is what makes them of interest. In this study 20 years of data from both the S&P 500 and the JSE All Share index have been used. An extreme value approach has been taken to quantify the risks associated with extreme market events. To achieve this a two phased process is used to calculated the Value at Risk and Expected Shortfall. The first phase involved running the daily returns through the GARCH model, and then extracting the residuals. The second phase involves using the Block Maxima Method, or Peaks over Threshold method to fit the residuals to the Generalized Extreme Value Distribution or the Generalized Pareto Distribution. Finally, the impact of estimation frequency is considered for each of the models. In conclusion, taking an extreme value approach to provide a statistically sound method to calculate risk, even when the parameters of the model are updated less frequently, this is preferable to simpler models where the parameter estimates are updated daily. 2021-01-19T12:06:15Z 2021-01-19T12:06:15Z 2020 2021-01-19T09:57:16Z Master Thesis Masters MSc http://hdl.handle.net/11427/32558 eng application/pdf Department of Statistical Sciences Faculty of Science
spellingShingle Mathematical Statistics
Coyne, Alice Elizabeth
The impact of estimation frequency on Value at Risk (VaR) and Expected Shortfall (ES) forecasts: an empirical study on conditional extreme value models
thesis_degree_str Master's
title The impact of estimation frequency on Value at Risk (VaR) and Expected Shortfall (ES) forecasts: an empirical study on conditional extreme value models
title_full The impact of estimation frequency on Value at Risk (VaR) and Expected Shortfall (ES) forecasts: an empirical study on conditional extreme value models
title_fullStr The impact of estimation frequency on Value at Risk (VaR) and Expected Shortfall (ES) forecasts: an empirical study on conditional extreme value models
title_full_unstemmed The impact of estimation frequency on Value at Risk (VaR) and Expected Shortfall (ES) forecasts: an empirical study on conditional extreme value models
title_short The impact of estimation frequency on Value at Risk (VaR) and Expected Shortfall (ES) forecasts: an empirical study on conditional extreme value models
title_sort impact of estimation frequency on value at risk var and expected shortfall es forecasts an empirical study on conditional extreme value models
topic Mathematical Statistics
url http://hdl.handle.net/11427/32558
work_keys_str_mv AT coynealiceelizabeth theimpactofestimationfrequencyonvalueatriskvarandexpectedshortfallesforecastsanempiricalstudyonconditionalextremevaluemodels
AT coynealiceelizabeth impactofestimationfrequencyonvalueatriskvarandexpectedshortfallesforecastsanempiricalstudyonconditionalextremevaluemodels

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