Volatility Forecasting during Stock Disaster in China A-share Stock Market

This study explores the performance of volatility prediction models in Chinses stock market during stock market crash period. The index of interest is CSI 300 and the time interval employed ranges from 09/08/2007 to 19/12/2008 (2008 financial crisis) and from 12/06/2015 to 10/05/2016 (China stock ma...

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Main Author: BAO, JINGYI
Format: Dissertation (University of Nottingham only)
Language:English
Published: 2018
Subjects:
Online Access:https://eprints.nottingham.ac.uk/53692/
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author BAO, JINGYI
author_facet BAO, JINGYI
author_sort BAO, JINGYI
building Nottingham Research Data Repository
collection Online Access
description This study explores the performance of volatility prediction models in Chinses stock market during stock market crash period. The index of interest is CSI 300 and the time interval employed ranges from 09/08/2007 to 19/12/2008 (2008 financial crisis) and from 12/06/2015 to 10/05/2016 (China stock market crash in 2015) respectively. Besides, the models tested in this paper is EWMA, GARCH (1, 1) and EGARCH (1, 1). Both GARCH family models are fitted with t distribution and GED. The forecasting horizon is one day ahead in both periods with daily data and Parkinson estimator is treated as proxy of realized volatility. The way to evaluate performance of each model is based on MSE, QLIKE and HMAE loss functions. The results prove that EWMA has the most robust ability of volatility prediction in both periods under all three loss functions. Moreover, EGARCH models is generally superior to simple GARCH model.
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format Dissertation (University of Nottingham only)
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publishDate 2018
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spelling nottingham-536922022-03-11T16:26:20Z https://eprints.nottingham.ac.uk/53692/ Volatility Forecasting during Stock Disaster in China A-share Stock Market BAO, JINGYI This study explores the performance of volatility prediction models in Chinses stock market during stock market crash period. The index of interest is CSI 300 and the time interval employed ranges from 09/08/2007 to 19/12/2008 (2008 financial crisis) and from 12/06/2015 to 10/05/2016 (China stock market crash in 2015) respectively. Besides, the models tested in this paper is EWMA, GARCH (1, 1) and EGARCH (1, 1). Both GARCH family models are fitted with t distribution and GED. The forecasting horizon is one day ahead in both periods with daily data and Parkinson estimator is treated as proxy of realized volatility. The way to evaluate performance of each model is based on MSE, QLIKE and HMAE loss functions. The results prove that EWMA has the most robust ability of volatility prediction in both periods under all three loss functions. Moreover, EGARCH models is generally superior to simple GARCH model. 2018-09-04 Dissertation (University of Nottingham only) NonPeerReviewed application/pdf en https://eprints.nottingham.ac.uk/53692/1/4299218%20JingYi%20Bao.pdf BAO, JINGYI (2018) Volatility Forecasting during Stock Disaster in China A-share Stock Market. [Dissertation (University of Nottingham only)] volatility prediction Chinese stock market stock disaster
spellingShingle volatility prediction
Chinese stock market
stock disaster
BAO, JINGYI
Volatility Forecasting during Stock Disaster in China A-share Stock Market
title Volatility Forecasting during Stock Disaster in China A-share Stock Market
title_full Volatility Forecasting during Stock Disaster in China A-share Stock Market
title_fullStr Volatility Forecasting during Stock Disaster in China A-share Stock Market
title_full_unstemmed Volatility Forecasting during Stock Disaster in China A-share Stock Market
title_short Volatility Forecasting during Stock Disaster in China A-share Stock Market
title_sort volatility forecasting during stock disaster in china a-share stock market
topic volatility prediction
Chinese stock market
stock disaster
url https://eprints.nottingham.ac.uk/53692/