Testing for Conditional Heteroscedasticity in Financial Time Series
نویسندگان
چکیده
In this paper we survey time series models allowing for conditional heteroscedas ticity and autoregression like AR GARCH type models These models reduce to a white noise model when some of the conditional heteroscedasticity parameters take their boundary value at zero and the autoregressive component is in fact not present We reproduce the asymptotic distribution of the pseudo log likelihood ratio statistics for testing the present conditional heteroscedasticity models on reduction to white noise The theoretical results are applied to nancial data i e log returns of stock prices We estimate the parameters for all models presented and further on we test on reduction to white noise The impact of these results on risk measure ment is discussed by comparing Value at Risk calculations under alternative model speci cations JEL Classi cation C C C
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