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We present a new procedure for detecting multiple additive outliers in GARCH(1,1) models at unknown dates. The outlier candidates are the observations with the largest standardized residual. First, a likelihood-ratio based test determines the presence and timing of an outlier. Next, a second...
Persistent link: https://www.econbiz.de/10011257361
We present a new procedure for detecting multiple additive outliers in GARCH(1,1) models at unknown dates. The outlier candidates are the observations with the largest standardized residual. First, a likelihood-ratio based test determines the presence and timing of an outlier. Next, a second...
Persistent link: https://www.econbiz.de/10010325338
Persistent link: https://www.econbiz.de/10003764109
We present a new procedure for detecting multiple additive outliers in GARCH(1,1) models at unknown dates. The outlier candidates are the observations with the largest standardized residual. First, a likelihood-ratio based test determines the presence and timing of an outlier. Next, a second...
Persistent link: https://www.econbiz.de/10011346470
Persistent link: https://www.econbiz.de/10003204040
Persistent link: https://www.econbiz.de/10003228832
Persistent link: https://www.econbiz.de/10002652109
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Persistent link: https://www.econbiz.de/10001526108
We investigate several aspects of GARCH(p,q) models which are relevant for empirical applications. In particular, we note that the inclusion of a dummy variable as regressor can lead to multimodality in the GARCH likelihood. This makes standard inference on the estimated coefficient impossible....
Persistent link: https://www.econbiz.de/10005345564