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.
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Bibliographic InfoPaper provided by Universidad Carlos III de Madrid in its series Open Access publications from Universidad Carlos III de Madrid with number info:hdl:10016/4783.
Length: 278 p.
Date of creation: 1995
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Web page: http://www.uc3m.es
Model Construction and Estimation (C510); Multiple or Simultaneous Equation Models; Time-Series Models; Dynamic Quantile Regressions (C320); Foreign Exchange (F310);
Other versions of this item:
- Mod - Business Administration and Business Economics; Marketing; Accounting - - - - -
- Con - Mathematical and Quantitative Methods - - - - -
- and - - - - - -
- Es - Macroeconomics and Monetary Economics - -
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