A rapid development of time series models and methods addressing volatility in computational finance and econometrics are recently reported in the financial literature. This paper considers doubly stochastic volatility models with GARCH errors. General properties for process mean, variance and kurto
The specification of GARCH models with stochastic covariates
β Scribed by Jeff Fleming; Chris Kirby; Barbara Ostdiek
- Publisher
- John Wiley and Sons
- Year
- 2008
- Tongue
- English
- Weight
- 238 KB
- Volume
- 28
- Category
- Article
- ISSN
- 0270-7314
No coin nor oath required. For personal study only.
β¦ Synopsis
Abstract
A number of studies investigate whether various stochastic variables explain changes in return volatility by specifying the variables as covariates in a GARCH(1, 1) or EGARCH(1, 1) model. The authors show that these models impose an implicit constraint that can obscure the true role of the covariates in the analysis. They illustrate the problem by reconsidering the role of contemporaneous trading volume in explaining ARCH effects in daily stock returns. Once the constraint imposed in earlier research is relaxed, it is found that specifying volume as a covariate does little to diminish the importance of lagged squared returns in capturing the dynamics of volatility. Β© 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:911β934, 2008
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