Publication:
Multivariate Stochastic Variance Models

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URI: https://hdl.handle.net/10016/4783
ISBN: 9780198774327
ISBN: 019877432X
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multivariate_ruiz_1995.pdf (1.04 MB)
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1995
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Oxford University Press
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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.
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Publicado además en: The Review of Economic Studies, 1994, vol. 61, n. 2, p. 247-264
Publicado además en: Recent developments in Time Series, 2003, vol. 2, pp. 134-152
Publicado además en: Selected Readings for Stochastic Volatility, 2005, p. 156-176, ISBN10: 0199257191, ISBN13: 9780199257195
Keywords
Model Construction and Estimation (C510), Multiple or Simultaneous Equation Models, Time-Series Models, Dynamic Quantile Regressions (C320), Foreign Exchange (F310)
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ARCH: Selected readings, 1995, p. 256-276
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