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Local Likelihood Estimators in a Regression Model for Stock Returns

[journal article]

Jönck, Uwe Christian

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Abstract We consider a non-stationary regression type model for stock returns in which the innovations are described by four-parameter distributions and the parameters are assumed to be smooth, deterministic functions of time. Incorporating also normal distributions for modelling the innovations, our model is capable of adapting to light-tailed innovations as well as to heavy-tailed ones. Thus, it turns out to be a very flexible approach. Both, for the fitting of the model and for forecasting the distributions of future returns, we use local likelihood methods for estimation of the parameters. We apply our model to the S&P 500 return series, observed over a period of twelve years. We show that it fits these data quite well and that it yields reasonable one-day-ahead forecasts.
Classification Basic Research, General Concepts and History of Economics; Economic Statistics, Econometrics, Business Informatics
Method theory application
Free Keywords Financial time series; Statistics; Financial econometrics; Financial modelling
Document language English
Publication Year 2008
Page/Pages p. 619-635
Journal Quantitative Finance, 8 (2008) 6
Status Postprint; peer reviewed
Licence PEER Licence Agreement (applicable only to documents from PEER project)