Warrington College of Business, University of Florida
The health of the Chinese stock market, which has been attracting increased attention, is the topic of two papers in this issue of The Journal of Risk, with particular attention paid to systemic risk and spillover from commodity markets. Additionally, the integration of data captured at different frequencies, such as through the mixed data sampling (MIDAS) method, is discussed in the issue, as is the effect of the interplay between interest rates and withdrawal rules on deposits set by banks in their liquidity risk strategies.
In the issue’s first paper, “Bayesian nonparametric covariance estimation with noisy and nonsynchronous asset prices”, Jia Liu proposes a Bayesian nonparametric method to estimate the ex post daily covariance matrix of asset returns using high-frequency data with parameters linked to the true daily integrated covariance. Liu uses Dirichlet priors to aid pooling, which helps to account for information sharing and enables some flexibility in setting the number of clusters. An empirical illustration shows that, as a result of this, estimation accuracy is improved and misspecification is minimized.
In “Reinvestigating international crude oil market risk spillovers to the Chinese financial market via a novel copula-GARCH-MIDAS model”, our second paper, Cuixia Jiang, Yuqian Li, Qifa Xu and Jun Wu develop a copula-GARCH-MIDAS approach to estimate the joint probability distribution of multiple variables in order to then derive conditional-value-at-risk-type (CoVaR-type) risk measures. In their empirical implementation, the authors illustrate the importance of macroeconomic fundamentals on these risk measures, as well as the significant spillover effect of crude oil markets on the Chinese financial market.
In “Systemic risk of the Chinese stock market based on the mobility measures of the marginal expected shortfall”, the third paper in this issue, Xiaohang Liu and Handong Li also study the Chinese stock market. They apply a dynamic mixture copula model in combination with a systemic risk measure – the marginal expected shortfall – to show that firms contribute more than banks to stock market risk, but that this risk is more sensitive to private banks than to state-owned banks. Liu and Li further identify a strong long-term memory effect on systemic risk associated with the impact of individual stocks.
Closing out this issue is “Modeling nonmaturing deposits: a framework for interest and liquidity risk management” by Emil Avsar and Benjamin Ruimy, who offer a framework to account for the interplay between different characteristics of nonmaturing deposits, such as interest rates offered by banks, if any, and limitations on withdrawals by customers to manage net interest income, a significant source of profitability. Based on different assumptions on market share and money supply growth, Avsar and Ruimy calibrate their framework using UK data to illustrate the effect of various interest rate scenarios on a bank’s decisions to help control its various deposit balances.
This paper introduces a Bayesian nonparametric method to estimate the ex post covariance matrix from high-frequency data.
This paper develops a copula-GARCH-MIDAS model to estimate the joint probability distribution of multivariate variables, and then derives CoVaR-type risk measures.
Systemic risk of the Chinese stock market based on the mobility measures of the marginal expected shortfall
This paper applies the dynamic mixture copula model method and proposes a mobility measure of the marginal expected shortfall to depict the changing systemic risk in China’s mainland stock market and Hong Kong’s stock market.
This paper presents a generic framework for modeling nonmaturing deposits that can be used by banks for interest and liquidity risk management, funds transfer pricing and dynamic balance sheet management.