On a Simple Two-Stage Closed-form Estimator for a Stochastic Volatility in a General Linear Regression

Jean-Marie Dufour and Pascale Valéry

A chapter in Econometric Analysis of Financial and Economic Time Series, 2006, pp 259-288 from Emerald Group Publishing Limited

Abstract: In this paper, we consider the estimation of volatility parameters in the context of a linear regression where the disturbances follow a stochastic volatility (SV) model of order one with Gaussian log-volatility. The linear regression represents the conditional mean of the process and may have a fairly general form, including for example finite-order autoregressions. We provide a computationally simple two-step estimator available in closed form. Under general regularity conditions, we show that this two-step estimator is asymptotically normal. We study its statistical properties by simulation, compare it with alternative generalized method-of-moments (GMM) estimators, and present an application to the S&P composite index.

Date: 2006
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.101 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.101 ... d&utm_campaign=repec (application/pdf)
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:eme:aecozz:s0731-9053(05)20010-5

DOI: 10.1016/S0731-9053(05)20010-5

Access Statistics for this chapter

More chapters in Advances in Econometrics from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().