Asymmetric stochastic volatility in emerging stock markets
Faruk Selcuk
Applied Financial Economics, 2005, vol. 15, issue 12, 867-874
Abstract:
Daily stock market volatility in a sample of emerging market economies is investigated utilizing an asymmetric stochastic volatility (ASV) model which is estimated with Markov Chain Monte Carlo (MCMC) method. The results indicate that the ASV model captures the volatility dynamics in those stock markets successfully. Particularly, it is shown that volatility has a significant persistency and the variability of volatility is higher as compared to advanced economies. The paper also provides evidence for significant negative correlation between shocks to the stock market index and shocks to volatility, the so-called 'leverage effect'. Furthermore, the estimation results show that the persistency in volatility and the variability of volatility are negatively related: higher variability of volatility implies lower persistency in volatility series and vice versa. In addition, persistency in volatility and the magnitude of leverage effect are negatively correlated: high persistency is associated with relatively lower leverage effect.
Date: 2005
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)
Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/09603100500077136 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:apfiec:v:15:y:2005:i:12:p:867-874
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAFE20
DOI: 10.1080/09603100500077136
Access Statistics for this article
Applied Financial Economics is currently edited by Anita Phillips
More articles in Applied Financial Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().