Modelling and Testing Financial Risk

Abstract

This dissertation centres on the modelling and testing of risk with the emphasis on gauging novel issues in finance. The first chapter models the stability of financial system using prominent systemic risk measures, and examines for various risk factors affecting financial stability including the risky practice of shadow insurance. The collected dataset suggests that shadow insurance has been increasingly exploited to reduce risk exposure and that entities exploiting shadow insurance are generally riskier and more interconnected with the financial system. Using panel analysis, I find statistical evidence that the practice of shadow insurance does affect financial stability based on two distinct systemic risk measures. In the second chapter, I propose a new multivariate econometric strategy for examining the spillover of volatility—the most fundamental risk measure. The asymptotic theory of the testing strategy is established under regularity conditions. The chapter includes an extensive simulation study to confirm the finite sample performance of the proposed econometric strategy and an empirical study based on the new test to examine volatility spillover between the North American and European financial markets before and after the Brexit referendum. In the third chapter, I consolidate the comprehensive literature on Granger causality methods, and I apply the unified methodology to examine different components of risk spillover between international crude oil and the Chinese equity markets that are fuel intensive. This unified analysis disentangles the complex oil-equity nexus to find that it has been nontrivially related to various factors such as demand and supply of oil, economic growth rate, government subsidies and the Chinese oil pricing reformation