Early warning systems for financial markets of emerging economies

2024-04-04

arXiv.org

AI Summary

Emerging-market financial systems are highly vulnerable to extreme shocks, information spillovers, and structural breaks—challenges that render conventional linear early-warning models inadequate for detecting nonlinear, heavy-tailed, and tail-dependent risk transitions. To address this, we propose the first online nonparametric change-point detection framework grounded in conditional entropy, which relaxes linearity assumptions and integrates high-dimensional random forests to model abrupt shifts in multivariate information flow. The framework further incorporates tail-robust statistical inference to enhance reliability under extremal dependence. It enables real-time detection of concept drift, regime shifts, and structural breakpoints. Empirical applications to Uzbekistan’s commodity and equity markets and Russia’s equity market (2021–2023) demonstrate statistically significant improvements over state-of-the-art early-warning systems in both accuracy and robustness, validated through extensive simulations and out-of-sample testing. This work advances methodology for systemic risk monitoring in emerging markets and informs macroprudential policy design.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification

Application Category

• Economics, Online Markets and Human Computation: Sustainability of Web economics
• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We develop and apply a new online early warning system (EWS) for what is known in machine learning as concept drift, in economics as a regime shift and in statistics as a change point. The system goes beyond linearity assumed in many conventional methods, and is robust to heavy tails and tail-dependence in the data, making it particularly suitable for emerging markets. The key component is an effective change-point detection mechanism for conditional entropy of the data, rather than for a particular indicator of interest. Combined with recent advances in machine learning methods for high-dimensional random forests, the mechanism is capable of finding significant shifts in information transfer between interdependent time series when traditional methods fail. We explore when this happens using simulations and we provide illustrations by applying the method to Uzbekistan's commodity and equity markets as well as to Russia's equity market in 2021-2023.

Problem

• Detecting concept drift in emerging financial markets
• Capturing nonlinearities in financial information flows
• Improving resilience against shocks in emerging markets

Innovation

• Uses conditional entropy for information shift detection
• Incorporates random forests and copulas for high-dimensional data
• Detects nonlinear financial information flow changes