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. Also incorporating normal distributions for modelling the innovations, our model is capable of adapting to light-tailed innovations as well as to heavy-tailed innovations. Thus, it turns out to be a very flexible approach. For both the fitting of the model and for forecasting the distributions of future returns, we use local likelihood methods to estimate the parameters. We apply our model to the S&P 500 return series, observed over a period of 12 years. We show that it fits these data quite well and that it yields reasonable one-day-ahead forecasts.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Publisher Info
Article provided by Taylor and Francis Journals in its journal Quantitative Finance.