Modeling and Decoupling Systemic Risk

TL;DR

This paper introduces a novel nonlinear time series model, AcAF, for identifying and decoupling systemic risk patterns across various complex systems, enhancing risk management and interpretability.

Contribution

It proposes the AcAF model and two new risk measures, providing a flexible, interpretable framework for systemic risk analysis with proven theoretical properties and empirical effectiveness.

Findings

AcAF model effectively captures heterogeneous data.

Proposed risk measures improve risk pattern detection.

Empirical results show superior performance in financial markets.

Abstract

Identifying systemic risk patterns in geopolitical, economic, financial, environmental, transportation, epidemiological systems and their impacts is the key to risk management. This paper proposes a new nonlinear time series model: autoregressive conditional accelerated Fr\'echet (AcAF) model and introduces two new endopathic and exopathic competing risk measures for better learning risk patterns, decoupling systemic risk, and making better risk management. The paper establishes the probabilistic properties of stationarity and ergodicity of the AcAF model. Simulation demonstrates the efficiency of the proposed estimators and the AcAF model's flexibility in modeling heterogeneous data. Empirical studies on the stock returns in S&P 500 and the cryptocurrency trading show the superior performance of the proposed model in terms of the identified risk patterns, endopathic and exopathic competing risks, being informative with greater interpretability, enhancing the understanding of the systemic risks of a market and their causes, and making better risk management possible.