Stylized Facts and Discrete Stochastic Volatility Models
This paper highlights the ability of the discrete stochastic volatility models to predict some important properties of the data, i.e. leptokurtic distribution of the returns, slowly decaying autocorrelation function of squared returns, the Taylor effect and the asymmetric response of volatility to return shocks. Although, there are many methods proposed for stochastic volatility model estimation, in this paper Markov Chain Monte Carlo techniques were considered. It was found that the existent specifications in the stochastic volatility literature are consistent with the empirical properties of the data. Thus, from this point of view the discrete stochastic volatility models are reliable tools for volatility estimation.
|Date of creation:||Jun 2008|
|Date of revision:|
|Contact details of provider:|| Postal: 6 ROMANA PLACE, 70167 - BUCHAREST|
Web page: http://www.dofin.ase.ro/carfib/
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- He, Changli & Terasvirta, Timo, 1999.
"Properties of moments of a family of GARCH processes,"
Journal of Econometrics,
Elsevier, vol. 92(1), pages 173-192, September.
- He, Changli & Teräsvirta, Timo, 1997. "Properties of Moments of a Family of GARCH Processes," SSE/EFI Working Paper Series in Economics and Finance 198, Stockholm School of Economics.
- Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993.
" On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks,"
Journal of Finance,
American Finance Association, vol. 48(5), pages 1779-1801, December.
- Lawrence R. Glosten & Ravi Jagannathan & David E. Runkle, 1993. "On the relation between the expected value and the volatility of the nominal excess return on stocks," Staff Report 157, Federal Reserve Bank of Minneapolis.
- Sandmann, Gleb & Koopman, Siem Jan, 1998. "Estimation of stochastic volatility models via Monte Carlo maximum likelihood," Journal of Econometrics, Elsevier, vol. 87(2), pages 271-301, September.
- Andersen, Torben G. & Chung, Hyung-Jin & Sorensen, Bent E., 1999. "Efficient method of moments estimation of a stochastic volatility model: A Monte Carlo study," Journal of Econometrics, Elsevier, vol. 91(1), pages 61-87, July.
- Manabu Asai & Michael McAleer & Jun Yu, 2006. "Multivariate Stochastic Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 25(2-3), pages 145-175.
When requesting a correction, please mention this item's handle: RePEc:cab:wpaefr:10. 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: (Ciprian Necula)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.