Bayesian Systemic Risk Analysis using Latent Space Network Models

Abstract

In financial markets, systemic risk is a type of risk in which the failure of one stock in the market triggers a sequence of failures. Our study proposes a Bayesian decision scheme to dynamically monitor systemic risk under any preferences and restrictions in financial risk management. We begin by capturing the moving correlations of stock returns because such correlations represent the strengths of the relationships among stocks. Then, we construct a dynamic financial network to link the stocks with strong relationships. Using the financial space, which is related to the position of stocks in the network plot, we locate two stocks in the financial space that are a short distance apart, because the relationship between these two stocks is strong. Using the distances between stocks in the financial space, together with the salient preferences and restrictions in financial risk management, we propose a systemic risk score. We then use 20 years of data to demonstrate the effectiveness of our proposed systemic risk score to give an early signal of global financial instabilities.

Keywords

• Dynamic networks
• latent space model
• Monte Carlo Markov chain
• network connectedness
• risk management
• systemic risk