Mixtures of t-distributions for Finance and Forecasting
AbstractWe explore convenient analytic properties of distributions constructed as mixtures of scaled and shifted t-distributions. A feature that makes this family particularly desirable for econometric applications is that it possesses closed-form expressions for its anti-derivatives (e.g., the cumulative density function). We illustrate the usefulness of these distributions in two applications. In the first application, we use a scaled and shifted t-distribution to produce density forecasts of U.S. inflation and show that these forecasts are more accurate, out-of-sample, than density forecasts obtained using normal or standard t-distributions. In the second application, we replicate the option-pricing exercise of Abadir and Rockinger (2003) using a mixture of scaled and shifted t-distributions and obtain comparably good results, while gaining analytical tractability.
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Bibliographic InfoPaper provided by Institute for Advanced Studies in its series Economics Series with number 216.
Length: 27 pages
Date of creation: Oct 2007
Date of revision:
Postal: Institute for Advanced Studies - Library, Stumpergasse 56, A-1060 Vienna, Austria
Other versions of this item:
- Giacomini, Raffaella & Gottschling, Andreas & Haefke, Christian & White, Halbert, 2008. "Mixtures of t-distributions for finance and forecasting," Journal of Econometrics, Elsevier, vol. 144(1), pages 175-192, May.
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-10-20 (All new papers)
- NEP-ECM-2007-10-20 (Econometrics)
- NEP-ETS-2007-10-20 (Econometric Time Series)
- NEP-FOR-2007-10-20 (Forecasting)
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