Jump, Non-Normal Error Distribution And Stock Price Volatility — A Nonparametric Specification Test
This paper examines a wide variety of popular volatility models for stock index return, including Random Walk model, Autoregressive model, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, and extensive GARCH model, GARCH-jump model with Normal, and Student t-distribution assumption as well as nonparametric specification test of these models. We fit these models to Dhaka stock return index from 20 November 1999 to 9 October 2004. There has been empirical evidence of volatility clustering, alike to findings in previous studies. Each market contains different GARCH models, which fit well. From the estimation, we find that the volatility of the return and the jump probability were significantly higher after 27 November 2001. The model introducing GARCH jump effect with normal and Student t-distribution assumption can better fit the volatility characteristics. We find that RW-GARCH-t, RW-AGARCH-t RW-IGARCH-t and RW-GARCH-M-t can pass the nonparametric specification test at 5% significance level. It is suggested that these four models can capture the main characteristics of Dhaka stock return index.
Volume (Year): 54 (2009)
Issue (Month): 01 ()
|Contact details of provider:|| Web page: http://www.worldscinet.com/ser/ser.shtml |
|Order Information:|| Email: |
When requesting a correction, please mention this item's handle: RePEc:wsi:serxxx:v:54:y:2009:i:01:p:101-121. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Tai Tone Lim)
If references are entirely missing, you can add them using this form.