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

TL;DR

This paper introduces a non-parametric, online regime detection and clustering method for multidimensional, path-dependent financial data using a signature-based similarity metric, enabling fast, accurate identification of market regimes and anomalies.

Contribution

It develops a novel path-wise, non-parametric approach for real-time market regime detection and clustering, extending previous methods to high-dimensional, non-Markovian, and autocorrelated data.

Findings

Successfully applied to synthetic datasets of increasing complexity

Effectively detects market regime changes in real-world financial data

Provides fast, automated regime change detection and outlier identification

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.