Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures

65 Pages Posted: 28 Jun 2023 Last revised: 6 Jul 2023

See all articles by Blanka Horvath

Blanka Horvath

Mathematical Institute, University of Oxford and Oxford Man Institute; Oxford University; The Alan Turing Institute

Zach Issa

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

Date Written: June 27, 2023

Abstract

In this work we present a non-parametric online market regime detection method for multidimensional data structures using a path-wise two-sample test derived from a maximum mean discrepancy-based similarity metric on path space that uses rough path signatures as a feature map. The latter similarity metric has been developed and applied as a discriminator in recent generative models for small data environments, and has been optimised here to the setting where the size of new incoming data is particularly small, for faster reactivity.

On the same principles, we also present a path-wise method for regime clustering which extends our previous work. The presented regime clustering techniques were designed as ex-ante market analysis tools that can identify periods of approximatively similar market activity, but the new results also apply to path-wise, high dimensional-, and to non-Markovian settings as well as to data structures that exhibit autocorrelation.

We demonstrate our clustering tools on easily verifiable synthetic datasets of increasing complexity, and also show how the outlined regime detection techniques can be used as fast on-line automatic regime change detectors or as outlier detection tools, including a fully automated pipeline. Finally, we apply the fine-tuned algorithms to real-world historical data including high-dimensional baskets of equities and the recent price evolution of crypto assets, and we show that our methodology swiftly and accurately indicated historical periods of market turmoil.

Keywords: Stochastic processes, signatures, regime classification, regime detection, unsupervised learning

JEL Classification: C12, C22, C58, G11

Suggested Citation

Horvath, Blanka and Issa, Zacharia, Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures (June 27, 2023). Available at SSRN: https://ssrn.com/abstract=4493344 or http://dx.doi.org/10.2139/ssrn.4493344

Blanka Horvath

Mathematical Institute, University of Oxford and Oxford Man Institute ( email )

Andrew Wiles Building
Woodstock Road
Oxford, OX2 6GG
United Kingdom

Oxford University ( email )

The Alan Turing Institute ( email )

Zacharia Issa (Contact Author)

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

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