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Multivariate Stochastic Variance Models

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Author Info
Harvey, Andrew
Ruiz, Esther
Shephard, Neil

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Abstract

Changes 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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Publisher Info
Article provided by Blackwell Publishing in its journal Review of Economic Studies.

Volume (Year): 61 (1994)
Issue (Month): 2 (April)
Pages: 247-64
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Handle: RePEc:bla:restud:v:61:y:1994:i:2:p:247-64

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This page was last updated on 2009-11-22.

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