大阪大学　数理・データ科学教育研究センター
Center for Mathematical Modeling and Data Science,Osaka University

Multivariate Asset Return Prediction with Mixture Models

Marc Paolella (Swiss Banking Institute, University of Zürich, and Swiss Finance Institute)

1. ホーム
2. セミナー・集中講義
3. セミナー
4. Multivariate Asset Return Prediction with Mixture Models

大阪大学 金融・保険セミナーシリーズ 第36回(CSFI-CRESTジョイントセミナー)

Multivariate Asset Return Prediction with Mixture Models

Marc Paolella (Swiss Banking Institute, University of Zürich, and Swiss Finance Institute)

The use of mixture distributions for modeling asset returns has a long history in finance. New methods of demonstrating evidence for their necessity in the multivariate case is provided. The use of a two-component multivariate normal mixture distribution, coupled with shrinkage via a quasi-Bayesian prior, is motivated, and shown to be numerically trivial and reliable to estimate, unlike the majority of multivariate GARCH models in existence. Equally important, it provides a clear improvement over use of GARCH models feasible for use with a large number of assets, such as CCC, DCC, and their extensions, with respect to out-of-sample density forecasting. A generalization to a mixture of multivariate Laplace distributions is motivated via univariate and multivariate analysis of the data, and an EM-algorithm is developed for its estimation in conjunction with a quasi-Bayesian prior. It is shown to deliver significantly better forecasts than the mixed normal, with fast and numerically reliable estimation. Crucially, the distribution theory required for portfolio theory and risk assessment is developed.