Mixing Probabilities, Priors and Kernels via Entropy Pooling

GARP Risk Professional, pp. 32-36, December 2011

12 Pages Posted: 1 Feb 2012

See all articles by Attilio Meucci

Attilio Meucci

ARPM - Advanced Risk and Portfolio Management

Date Written: December 7, 2011

Abstract

We show how to mix machine learning signals such as kernel smoothing and fuzzy memberships via the Entropy Pooling approach by Meucci (2008). We illustrate a case study, where we overlay an exponentially time-decayed prior to a pseudo-Gaussian kernel that emphasizes market scenarios where volatilities and interest rates are similar to today’s levels. The code for the case study is available for download.

Keywords: Black-Litterman, effective number of scenarios, fuzzy membership, Mahalanobis distance, pseudo-Gaussian kernel, crisp conditioning, exponential decay, machine learning

JEL Classification: C1, G11

Suggested Citation

Meucci, Attilio, Mixing Probabilities, Priors and Kernels via Entropy Pooling (December 7, 2011). GARP Risk Professional, pp. 32-36, December 2011, Available at SSRN: https://ssrn.com/abstract=1944303

Attilio Meucci (Contact Author)

ARPM - Advanced Risk and Portfolio Management ( email )

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