Abstract
Financial time series data are typically observed to have heavy tails and time-varying volatility. Conditional heteroskedastic models to describe this behaviour have received considerable attention. In the present paper, our purpose is to examine some of these models in a general setting under some non-normal distributions. A likelihood based approach to estimation is used. New comparisons of estimators and their efficiencies are discussed.
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Liu, S., Heyde, C.C. On estimation in conditional heteroskedastic time series models under non-normal distributions. Statistical Papers 49, 455–469 (2008). https://doi.org/10.1007/s00362-006-0026-3
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DOI: https://doi.org/10.1007/s00362-006-0026-3