Multivariate Stochastic Variance Models
AbstractChanges in variance, or volatility, over time can be modeled using the approach based on autoregressive conditional heteroscedasticity. Another approach is to model variance as an unobserved stochastic process. Although it is not easy to obtain the exact likelihood function for such stochastic variance models, they tie in closely with developments in finance theory and have certain statistical attractions. This article sets up a multivariate model, discusses its statistical treatment, and shows how it can be modified to capture common movements in volatility in a very natural way. The model is then fitted to daily observations on exchange rates. Copyright 1994 by The Review of Economic Studies Limited.
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Bibliographic InfoArticle provided by Wiley Blackwell in its journal Review of Economic Studies.
Volume (Year): 61 (1994)
Issue (Month): 2 (April)
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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0034-6527
Other versions of this item:
- Harvey, Andrew & Ruiz, Esther & Shephard, Neil, 1995. "Multivariate Stochastic Variance Models," Open Access publications from Universidad Carlos III de Madrid info:hdl:10016/4783, Universidad Carlos III de Madrid.
- Tom Doan, . "RATS programs to estimate multivariate stochastic volatility models," Statistical Software Components RTZ00093, Boston College Department of Economics.
- Mod - Business Administration and Business Economics; Marketing; Accounting - - - - -
- Con - Mathematical and Quantitative Methods - - - - -
- and - - - - - -
- Es - Macroeconomics and Monetary Economics - -
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