文章基本信息

标题：Modelling Stock Market Return Volatility: Evidence from India
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作者：Saurabh Singh ; L.K Tripathi
期刊名称：Research Journal of Finance and Accounting
印刷版ISSN：2222-1697
出版年度：2016
卷号：7
期号：13
页码：93-101
语种：English
出版社：The International Institute for Science, Technology and Education (IISTE)
摘要：This paper empirically investigates the volatility pattern of Indian stock market based on time series data which comprises of daily closing prices of the S&P CNX Nifty Index for a fifteen year period from 1 st April 2001 to 31 st March 2016. For this study the analysis has been done using both symmetric and asymmetric models of Generalized Autoregressive Conditional Heteroscedastic (GARCH). For capturing the symmetric and asymmetric volatility GARCH-M (1, 1) and EGARCH (1, 1) estimations are found to be the most appropriate model as per the Akaike Information Criterion (AIC), Schwarz Information Criterion (SIC) and Log Likelihood ratios. The study also provides evidence for the existence of a positive and insignificant risk premium as per GARCH-M (1, 1) model. The asymmetric leverage effect captured by the parameter of EGARCH (1, 1) and TGARCH (1, 1) models show that negative shocks have a significant effect on conditional variance (volatility).
其他摘要：This paper empirically investigates the volatility pattern of Indian stock market based on time series data which comprises of daily closing prices of the S&P CNX Nifty Index for a fifteen year period from 1 st April 2001 to 31 st March 2016. For this study the analysis has been done using both symmetric and asymmetric models of Generalized Autoregressive Conditional Heteroscedastic (GARCH). For capturing the symmetric and asymmetric volatility GARCH-M (1, 1) and EGARCH (1, 1) estimations are found to be the most appropriate model as per the Akaike Information Criterion (AIC), Schwarz Information Criterion (SIC) and Log Likelihood ratios. The study also provides evidence for the existence of a positive and insignificant risk premium as per GARCH-M (1, 1) model. The asymmetric leverage effect captured by the parameter of EGARCH (1, 1) and TGARCH (1, 1) models show that negative shocks have a significant effect on conditional variance (volatility). Keywords: ARCH Effects, GARCH Models, Leverage Effect, Stock Returns, Volatility.
关键词：ARCH Effects; GARCH Models; Leverage Effect; Stock Returns; Volatility.