Risk Spillover: A New Perspective on the Study of Financing Difficulties for SMEs—Evidence from China

Existing studies on the financing difficulties of middle- and small-sized enterprises (SMEs) have neglected the quantitative analysis of SMEs’ risk spillovers to banks. Therefore, taking China as an example, we have analyzed the financing difficulties of SMEs from the perspective of risk spillover. The GARCH time-varying copula-CoVaR model based on the skewed-t distribution was used to measure the risk spillover effects of SMEs on banks. Furthermore, the heterogeneous impacts of risk spillovers on different scale banks were analyzed, including state-owned banks, joint-stock banks, and city commercial banks. The study found that SMEs always have obvious risk spillover effects on banks; it is particularly difficult for SMEs to obtain loans from the largest state-owned banks because in extreme cases, SMEs have the highest risk spillover effects on state-owned banks. The changes in risk spillover effects are attributed to two reasons. One is that the degree of association between SMEs and various banks is different, and the other is that there are varying degrees of risk spillover effects among various banks.

Analysis of risk spillover effect of copper option in China

Purpose The purpose of this paper is to compare the changes in the risk spillover effects between the copper spot and futures markets before and after the issuance of copper options, analyze the risk spillover effects between the three markets after the issuance of the options and can provide effective suggestions for regulators and investors who hedge risks. Design/methodology/approach The MV-CAViaR model is an extended form of the vector autoregressive model (VAR) to the quantile model, and it is also a special form of the MVMQ-CAViaR model. Based on the VAR quantile model, this model has undergone continuous promotion of the Conditional Autoregressive Value-at-Risk Model (CAViaR) and the Multi-quantile Conditional Autoregressive Value-at-Risk Model (MQ-CAViaR), and finally got the current form of the model. Findings The issuance of options has led to certain changes in the risk spillover effect between the copper spot and its derivative markets, and the risk aggregation effect in the futures market has always been significant. Therefore, when supervising the copper product market and investors using copper derivatives to avoid market risks, they need to pay attention to the impact of futures on the spot market, the impact of options on the futures market and the risk spillover effects of spot and futures on the options market. Practical implications The empirical results of this paper can be used to hedge market risk investment strategies, and the changes in market relationships also provide an effective basis for the supervision of the copper product market by the supervisory authority. Originality/value It is the first literature research to discuss the risk and the impact of spillover effects of copper options on China copper market and its derivative markets. The MV-CAViaR model can capture the mutual risk influence between markets by modeling multiple markets simultaneously.

Dependence and Risk Spillover among Hedging Assets: Evidence from Bitcoin, Gold, and USD

Understanding the dependence and risk spillover among hedging assets is crucial for portfolio allocation and regulatory decision making. Using various copula and conditional Value-at-Risk (CoVaR) measures, this paper quantifies the dependence and risk spillover effects between three traditional and emerging hedging assets: Bitcoin, gold, and USD. Furthermore, we investigate these effects at various short- and long-term horizons using a variational model decomposition (VMD) method. The empirical results show that there is strong negative dependence between gold and USD, but Bitcoin and gold are weakly and positively connected. Secondly, risk spillovers exist only between Bitcoin and gold and between gold and USD. The risk spillover effect between Bitcoin and gold are not stable, that is, if Bitcoin or gold faces the downward or upward risk, both the downward and upward risk of another asset have the chance to increase. The negative risk spillover between gold and USD is stable, especially in long-term horizons. Finally, the risk spillover between Bitcoin and gold as well as between gold and USD are asymmetric at downward and upward market environment.

Research on the Dependence Structure and Risk Spillover of Internet Money Funds Based on C-Vine Copula and Time-Varying t-Copula

Internet money funds (IMFs) are the most widely involved products in the Internet financial products market. This research utilized the C-vine copula model to study the risk dependence structure of IMFs and then introduces the time-varying t-copula model to analyze the risk spillover of diverse IMFs. The results show the following: (1) The risks of Internet-based IMFs, bank-based IMFs, and fund-based IMFs have obvious dependence structure, and the degree of risk dependence among different categories of IMFs is significantly different. (2) There are risk spillover effects among diverse IMFs, and their risk dependence relationship is characterized by cyclical feature. (3) The risk spillover effect among diverse IMFs is pronounced, and dynamic risk dependence between IMFs is characterized by synchronization.

On Spillover Effect of Systemic Risk of Listed Securities Companies in China Based on Extended CoVaR Model

Based on the daily data from January 2, 2019, to September 30, 2020, this paper uses the extended CoVaR model to measure the spillover effect of systemic risk among top 10 securities companies by market value in China, All Share Brokerage Index, All Share Financials Index, All Share Insurance Index, and CSI Banks Index. The conclusions are as follows: (1) there are risk spillover effects among 10 securities companies, which are asymmetric and bidirectional and highly volatile in a short period of time; (2) the spillover effect of systematic risk of securities companies is not necessarily related to the market value of securities companies but has a strong relationship with the stock market; (3) there are risk spillover effects between the sample securities companies and the four major indexes, but there are significant differences in the size of the spillover effects; (4) the securities industry has a great risk spillover effect on the financial industry, but the risk spillover effect of other financial sectors on the securities industry is very small. Finally, we put forward countermeasures and suggestions.

Simulation of financial risk spillover effect based on ARMA-GARCH and fuzzy calculation model

Convergence and spillover are the characteristics shown in the process of financial development. By verifying whether there is convergence and spillover in financial development within a certain region and between regions, the stage of financial development in the region can be more accurately judged. This paper combines the actual needs of financial analysis to construct a financial risk spillover effect model based on ARMA-GARCH and fuzzy calculation. The model uses ARMA-GARCH and fuzzy algorithm to verify the financial multiple risk factors. Moreover, in order to verify the effect of the model, this paper uses case data analysis to study the model effect and combines mathematical statistics to process the model data. The research results show that the model constructed in this paper has a certain effect, and the ARMA-GARCH model is suitable for analysis and research on financial risk spillover effects. At the same time, when the statistical distribution is used to fit its error distribution, the fitting and prediction effect of the model is better.

Risk spillover in financial markets based on support vector quantile regression

In terms of financial market risk research, with the rapid popularization of non-linear perspectives and the improvement of theoretical reasoning, scholars have slowly broken through the cage of linear ideas and derived new and more practical methods from non-linear perspectives to make up for the shortcomings of traditional research. Based on the support vector classification regression algorithm, this research combines the typical facts and characteristics of financial markets, from the perspective of quantile regression and SVR intelligent technology in computer science, to explore the research method of financial market risk spillover effects from a nonlinear perspective. Moreover, this research integrates statistical research, machine learning and other related research methods, and applies them to the measurement of financial risk spillover effects. The empirical analysis shows that the method proposed in this paper has certain effects, and financial risk analysis can be performed based on the risk spillover effect measurement model constructed in this paper.