Giacomini, Raffaella Gottschling, Andreas Haefke, Christian White, Halbert
Year of Publication:
Reihe Ökonomie / Economics Series, Institut für Höhere Studien (IHS) 216
We 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-ofsample, 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.
ARMA-GARCH models neural networks nonparametric density estimation forecast accuracy option pricing risk neutral density