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MULTIVARIATE STOCHASTIC VARIANCE MODELS

delete1994-04-01
delete713
delete
OA
AI
A
Andrew Harvey *
E
Esther Ruiz
N
Neil Shephard
DOI：10.2307/2297980delete
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Abstract

Abstract

En 中文
Changes in variance, or volatility, over time can be modelled using the approach based on autoregressive conditional heteroscedasticity (ARCH). However, the generalizations to multivariate series can be difficult to estimate and interpret. 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.
Keywords:
FOREIGN-CURRENCY OPTIONS
ARCH MODEL
HETEROSKEDASTICITY
VOLATILITY
RETURNS
RATES
TESTS
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Review of Economic Studies cover
Review of Economic Studies
IF:
6.4
Papers:
2.5K
Citations:
2.1W

Organization

No organization information available