Detecting Multivariate Market Regimes Via Clustering Algorithms

39 Pages Posted: 27 Mar 2024

See all articles by James Mc Greevy

James Mc Greevy

Kaiju Capital Management

Aitor Muguruza

Imperial College London; Kaiju Capital Management

Zach Issa

King’s College London - Faculty of Natural and Mathematical Sciences

Cristopher Salvi

The Alan Turing Institute; Imperial College London

Jonathan Chan

Kaiju Capital Management

Zan Zuric

Kaiju Capital Management; Imperial College London - Department of Mathematics

Date Written: March 13, 2024

Abstract

In this paper we study the joint dynamics of multivariate time series using an unsupervised learning technique and demonstrate its use in pairs trading and portfolio design. We present a novel non-parametric market regime detection method for multidimensional data. The detection procedure is based on a k-means clustering algorithm which makes use of distances between distributions to discriminate between different market regimes, which are categorised by their mean, variance and correlation. In particular, we empirically investigate the performance of the algorithm endowed with either Wasserstein distances or Maximum Mean Discrepancies. We suggest a two-step approach to clustering multivariate data using this new method, which we show to be effective on both synthetic and real world data. We demonstrate how our new approach can be used to obtain approximations to the mean, variance and correlation between two assets at a given point in time. We further show that these values can be used in the context of Modern Portfolio Theory to form profitable trading strategies when using two assets.

Keywords: k-means clustering, p-Wasserstein, MMD, Mean-Variance optimisation, Stochastic processes, numerical methods, stochastic volatility models, regime classification, unsupervised learning

JEL Classification: C02, C14, C15, C45, C82, G6, G11

Suggested Citation

Mc Greevy, James and Muguruza, Aitor and Issa, Zacharia and Salvi, Cristopher and Chan, Jonathan and Zuric, Zan, Detecting Multivariate Market Regimes Via Clustering Algorithms (March 13, 2024). Available at SSRN: https://ssrn.com/abstract=4758243 or http://dx.doi.org/10.2139/ssrn.4758243

James Mc Greevy (Contact Author)

Kaiju Capital Management ( email )

Aitor Muguruza

Imperial College London ( email )

South Kensington Campus
Exhibition Road
London, Greater London SW7 2AZ
United Kingdom

Kaiju Capital Management ( email )

Zacharia Issa

King’s College London - Faculty of Natural and Mathematical Sciences ( email )

Strand
London, England WC2R 2LS
United Kingdom

HOME PAGE: http://https://www.kcl.ac.uk/people/zacharia-issa

Cristopher Salvi

The Alan Turing Institute

Imperial College London

Jonathan Chan

Kaiju Capital Management ( email )

Zan Zuric

Kaiju Capital Management ( email )

Imperial College London - Department of Mathematics ( email )

South Kensington Campus
Imperial College
LONDON, SW7 2AZ
United Kingdom

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