Mixtures of t-distributions for Finance and Forecasting
Raffaella Giacomini (),
Christian Haefke () and
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Andreas Gottschling: Deutsche Bank AG, Credit RiskManagement
No 216, Economics Series from Institute for Advanced Studies
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-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.
Keywords: ARMA-GARCH models; neural networks; nonparametric density estimation; forecast accuracy; option pricing; risk neutral density (search for similar items in EconPapers)
JEL-codes: C63 C53 C45 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Journal Article: Mixtures of t-distributions for finance and forecasting (2008)
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