Differentiating Asset Classes

Abstract

Representing continuously compounded returns by their four bilateral gamma parameter estimates a multiclass classification support vector machine is trained on a sample less than one percent of the data to predict the asset class. The asset classes considered are equities, volatility, commodities, foreign exchange, credit, bond markets and hedge funds. Linear classification is observed to perform poorly. The use of seven binary learners makes some improvement and twenty one, one on one, binary learners deliver a good classification algorithm also performing well out of sample.

Keywords: Bilateral Gamma Model, Digital Moment Estimation, Asset Allocation

JEL Classification: G10, G12, G15