Learning about Stock Volatility: The Local Scale Model with Homoskedastic Innovations
AbstractThe Local Scale Model of Shephard (1994) is a state-space model of volatility clustering similar in effect to IGARCH, but with an unobserved volatility that realistically evolves independently of the observed errors, instead of being mechanically determined by them. It has one fewer parameter to estimate than IGARCH, and a closed form likelihood. Although the errors are assumed to be Gaussian conditional on the unobserved stochastic variance, they are Student t when conditioned on experience, with degrees of freedom that grow to a finite bound. The present paper improves on the Shephard model by assigning equal variance to the innovations to the volatility. The implied volatility gain at first declines sharply as in the classical Local Level Model, rather than being constant throughout as in traditional IGARCH (McCulloch 1985; Engle and Bollerslev 1986). The improved model is fit to monthly stock returns. The ML estimates imply 7.76 limiting degrees of freedom. A short-lived â€œGreat Moderationâ€ is evident during the mid-1990â€™s, but expires by 1998. Otherwise the period since 1970 was generally more volatile than the 1950s and 60s, though less so than the 1930s.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 173.
Date of creation: 04 Jul 2006
Date of revision:
Local Scale Model; Adaptive Learning; IGARCH; State-Space Model; Stock volatility;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-07-15 (All new papers)
- NEP-ETS-2006-07-15 (Econometric Time Series)
- NEP-FIN-2006-07-15 (Finance)
- NEP-FMK-2006-07-15 (Financial Markets)
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral 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: (Christopher F. Baum).
If references are entirely missing, you can add them using this form.