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- W1978494725 abstract "Abstract This empirical study examines the extent of non–linearity in a multivariate model of monthly financial series. To capture the conditional heteroscedasticity in the series, both the GARCH(1,1) and GARCH(1,1)–in–mean models are employed. The conditional errors are assumed to follow the normal and Student– t distributions. The non–linearity in the residuals of a standard OLS regression are also assessed. It is found that the OLS residuals as well as conditional errors of the GARCH models exhibit strong non–linearity. Under the Student density, the extent of non–linearity in the GARCH conditional errors was generally similar to those of the standard OLS. The GARCH–in–mean regression generated the worse out–of–sample forecasts." @default.
- W1978494725 created "2016-06-24" @default.
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- W1978494725 date "2003-05-30" @default.
- W1978494725 modified "2023-09-25" @default.
- W1978494725 title "Using monthly returns to model conditional heteroscedasticity" @default.
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- W1978494725 doi "https://doi.org/10.1080/0003684021000088536" @default.
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