Recent developments in volatility modeling and applications
| Autor: | C. R. Bector, Aerambamoorthy Thavaneswaran, Srimantoorao S. Appadoo |
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| Přispěvatelé: | University of Manitoba |
| Rok vydání: | 2006 |
| Předmět: |
Statistics and Probability
Heteroscedasticity Volatility clustering lcsh:Mathematics Applied Mathematics Autoregressive conditional heteroskedasticity General Decision Sciences lcsh:QA1-939 Normal distribution Computational Mathematics Financial models with long-tailed distributions and volatility clustering Econometrics Economics Kurtosis lcsh:Q Time series Volatility (finance) lcsh:Science |
| Zdroj: | Journal of Applied Mathematics and Decision Sciences, Vol 2006 (2006) |
| ISSN: | 1532-7612 1173-9126 |
| Popis: | In financial modeling, it has been constantly pointed out that volatility clustering and conditional nonnormality induced leptokurtosis observed in high frequency data. Financial time series data are not adequately modeled by normal distribution, and empirical evidence on the non-normality assumption is well documented in the financial literature (details are illustrated by Engle (1982) and Bollerslev (1986)). An ARMA representation has been used by Thavaneswaran et al., in 2005, to derive the kurtosis of the various class of GARCH models such as power GARCH, non-Gaussian GARCH, nonstationary and random coefficient GARCH. Several empirical studies have shown that mixture distributions are more likely to capture heteroskedasticity observed in high frequency data than normal distribution. In this paper, some results on moment properties are generalized to stationary ARMA process with GARCH errors. Application to volatility forecasts and option pricing are also discussed in some detail. |
| Databáze: | OpenAIRE |
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