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Modelling and forecasting stock and stock market volatility. / Craig Paul Gower

Swansea University Author: Craig Paul Gower

Abstract

The examination of stock price volatility has come under increased scrutiny due to the large swings in stock price movements that have occurred with greater frequency than the historical average. Additionally, the substantial increases in the volume of options trading has increased the importance of...

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Published: 2001
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
URI: https://cronfa.swan.ac.uk/Record/cronfa42339
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last_indexed 2018-08-03T10:09:53Z
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spelling 2018-08-02T16:24:28.8853957 v2 42339 2018-08-02 Modelling and forecasting stock and stock market volatility. e31ec40d3a02cae0e42ffbf2a31b1a99 NULL Craig Paul Gower Craig Paul Gower true true 2018-08-02 The examination of stock price volatility has come under increased scrutiny due to the large swings in stock price movements that have occurred with greater frequency than the historical average. Additionally, the substantial increases in the volume of options trading has increased the importance of accurate volatility forecasts due to the volatility forecast being the most important parameter affecting the pricing of options. Consequently, the aim of the thesis is to analyse the volatility of forty-five FTSE 100 stocks, the FTSE 100 index together with other major and emerging market stock indices. In particular, a comparison of the modelling and forecasting ability of GARCH type and stochastic volatility models is undertaken. The forecasting ability of the above models is compared against three benchmark models: the historical mean, random walk and exponential smoothing models. In terms of forecasting, the thesis is of interest because there have been few comparative studies for individual UK stocks. Additionally, the volatility-volume relationship is also considered in order to test the mixture of distributions hypothesis rationalisation for GARCH. In an extension of the current volatility volume literature, the CGARCH-volume model is used to examine the temporary volatility volume interactions. In terms of modelling ability, the stochastic volatility model performs on a par with the GARCH type models. In the forecasting analysis, the daily forecasts of FTSE 100 stocks perform poorly against the benchmark models with the four-weekly volatility forecasts performing relatively better. For the indices, the GARCH type models perform substantially better than for the FTSE 100 stocks. E-Thesis Finance.;Economics. 31 12 2001 2001-12-31 COLLEGE NANME Economics COLLEGE CODE Swansea University Doctoral Ph.D 2018-08-02T16:24:28.8853957 2018-08-02T16:24:28.8853957 Faculty of Humanities and Social Sciences School of Management - Economics Craig Paul Gower NULL 1 0042339-02082018162446.pdf 10798047.pdf 2018-08-02T16:24:46.7000000 Output 9402417 application/pdf E-Thesis true 2018-08-02T16:24:46.7000000 false
title Modelling and forecasting stock and stock market volatility.
spellingShingle Modelling and forecasting stock and stock market volatility.
Craig Paul Gower
title_short Modelling and forecasting stock and stock market volatility.
title_full Modelling and forecasting stock and stock market volatility.
title_fullStr Modelling and forecasting stock and stock market volatility.
title_full_unstemmed Modelling and forecasting stock and stock market volatility.
title_sort Modelling and forecasting stock and stock market volatility.
author_id_str_mv e31ec40d3a02cae0e42ffbf2a31b1a99
author_id_fullname_str_mv e31ec40d3a02cae0e42ffbf2a31b1a99_***_Craig Paul Gower
author Craig Paul Gower
author2 Craig Paul Gower
format E-Thesis
publishDate 2001
institution Swansea University
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Economics{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Economics
document_store_str 1
active_str 0
description The examination of stock price volatility has come under increased scrutiny due to the large swings in stock price movements that have occurred with greater frequency than the historical average. Additionally, the substantial increases in the volume of options trading has increased the importance of accurate volatility forecasts due to the volatility forecast being the most important parameter affecting the pricing of options. Consequently, the aim of the thesis is to analyse the volatility of forty-five FTSE 100 stocks, the FTSE 100 index together with other major and emerging market stock indices. In particular, a comparison of the modelling and forecasting ability of GARCH type and stochastic volatility models is undertaken. The forecasting ability of the above models is compared against three benchmark models: the historical mean, random walk and exponential smoothing models. In terms of forecasting, the thesis is of interest because there have been few comparative studies for individual UK stocks. Additionally, the volatility-volume relationship is also considered in order to test the mixture of distributions hypothesis rationalisation for GARCH. In an extension of the current volatility volume literature, the CGARCH-volume model is used to examine the temporary volatility volume interactions. In terms of modelling ability, the stochastic volatility model performs on a par with the GARCH type models. In the forecasting analysis, the daily forecasts of FTSE 100 stocks perform poorly against the benchmark models with the four-weekly volatility forecasts performing relatively better. For the indices, the GARCH type models perform substantially better than for the FTSE 100 stocks.
published_date 2001-12-31T03:52:46Z
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score 11.012678

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