TrAffic LIght system for systemic Stress: TALIS3

For the purposes of financial stability, identifying financial institutions that, when in distress, could have a significant adverse impact on financial markets is important. A TrAffic LIght System for Systemic Stress (TALIS-cube) is proposed that provides a comprehensive color-based classification for grouping companies according to both the stress reaction level of the system when the company is in distress and the company’s stress level. TALIS³ can integrate multiple signals from the interaction between different risk metrics. Starting from specific risk indicators, companies are classified by combining two loss functions—one for the system and one for each company—evaluated over time and as a cross section. An aggregated index is also obtained from the color-based classification of companies. TALIS³ can be used to enhance the performance and robustness of existing systemic risk measures. An empirical analysis of the U.S. market is also provided.     

Risk spillover between the US and the remaining G7 stock markets using time-varying copulas with Markov switching: Evidence from over a century of data

This paper analyses the risk spillover effect between the US stock market and the remaining G7 stock markets by measuring the conditional Value-at-Risk (CoVaR) using time-varying copula models with Markov switching and data that covers more than 100 years. The main results suggest that the dependence structure varies with time and has distinct high and low dependence regimes. Our findings verify the existence of risk spillover between the US stock market and the remaining G7 stock markets. Furthermore, the results imply the following: 1) abnormal spikes of dynamic CoVaR were induced by well-known historical economic shocks; 2) The value of upside risk spillover is significantly larger than the downside risk spillover and 3) The magnitudes of risk spillover from the remaining G7 countries to the US are significantly larger than that from the US to these countries.     

商业银行行业风险敞口与商业银行绩效

商业银行的行业风险敞口与商业银行绩效密切相关。中国数据实证分析表明,银行财务报表中会计占比较大的行业并非是市场认为的银行风险敞口最大的行业;股份制商业银行对各行业风险敞口较高。整体上商业银行对各行业的风险敞口对商业银行股票波动率、收益率和市值与账面价值之比具有显著影响,但行业风险敞口对银行系统性风险贡献度的影响不显著。     

房地产业对金融机构的系统性风险溢出效应研究——基于行业与企业综合视角的实证分析

房地产业与金融业具有强烈的共生性，当房地产业陷入困境时，是否会迅速扩散到与其关联的各类金融机构，蔓延并危及整个金融系统，出现房地产业对金融机构的"系统性风险溢出"?文章综合运用房地产行业指数与房地产企业数据，基于CoVaR模型和分位数回归方法，测算了我国房地产业对各类金融机构的系统性风险溢出强度，分析了其时变趋势和影响因素。实证结果表明，我国房地产业对金融机构存在较为显著的系统性风险溢出效应，在时间维度上存在周期性；房地产业对股份制与城商行的风险溢出强度最大，其次是保险机构和信托，最小的是国有银行；房地产企业的自身风险、规模和负债水平对风险溢出强度具有显著影响。据此，文章对金融监管部门、金融机构与房地产行业提出了相应的政策建议。     

Good for one,bad for all: Determinants of individual versus systemic risk

We analyze a sample of large international banks in major advanced economies and examine the impact that bank-specific factors have on an institution's solvency risk and its contribution to systemic risk. We focus on the five categories that the Basel Committee on Banking Supervision has recently proposed as indicators of systemic importance. Our findings suggest that unstable funding is the main factor driving systemic risk. Furthermore, the combination of significant trading activities with global presence appears to exacerbate spillover risks to the global financial system. Interestingly, whereas trading activities contribute to the build-up of correlated or ‘wrong-way’ risk they help to mitigate individual solvency risk. Conversely, a decentralized approach to liquidity management seems to alleviate individual solvency risk but amplifies the transmission of financial distress across the financial system. This suggests that a macro-prudential approach to financial regulation should focus not only on scaling up micro-prudential measures but also on enabling the efficient transfer of risk between financial institutions.     

基于动态Copula-CoVaR模型的影子银行风险溢出效应研究

依据2007-2017年中国金融市场运行数据,构建动态Copula-CoVaR模型,考量影子银行风险溢出效应。结果表明：影子银行与传统金融市场存在双向净风险溢出效应,随着时间的推移,溢出效应在逐渐增大;极端风险溢出效应存在不对称特征,影子银行对传统金融市场的冲击较大,且这种冲击具有滞后效应。鉴此,监管部门应夯实金融体系运行基础,创新影子银行监管工具,完善其协调监管模式。     

Measuring systemic risk of the Chinese banking industry: A wavelet-based quantile regression approach

In systemic risk measure, a large amount of literature has emerged, but few of them take into account the multi-scale natures of financial data. Considering these natures, we develop a novel W-QR-CoVaR method to measure systemic risk. To be specific, the W-QR-CoVaR method combines the wavelet multiresolution analysis (MRA) with the conditional value-at-risk (CoVaR) method based on the quantile regression (QR) framework. We then apply it to measure the systemic risk in the Chinese banking industry covering the period from September 2007 to September 2018. Our experiment results show that the hybrid W-QR-CoVaR method performs better than the traditional CoVaR method in terms of predictive accuracy. Furthermore, we also explore the relation between the systemic risk contribution of each individual bank and the bank-specific characteristics. Size and leverage appear to be the most robustness determinants. The findings suggest that regulators should pay more attention to the banks with smaller size and higher leverage.     

Asymmetric risk spillovers between Shanghai and Hong Kong stock markets under China’s capital account liberalization

In this paper, we investigate the asymmetric risk spillovers between Shanghai and Hong Kong stock markets under the backdrop of China’s capital account liberalization by measuring the Conditional Value-at-Risk (CoVaR) based on adjusted realized volatilities and variational mode decomposition based copula model. The empirical results show that, the asymmetric features of risk spillovers between the two markets are significant and manifest different states before and after the Shanghai-Hong Kong Stock Connect and Shenzhen-Hong Kong Stock Connect schemes. More specifically, first, the downside risk spillovers from Hong Kong to Shanghai are significantly larger than its upside risk spillovers, while the risk spillovers from Shanghai to Hong Kong is on the contrary. Second, the short-run risk spillovers are more drastic than the long-run risk spillovers, except the risk spillovers from Shanghai to Hong Kong after the Shenzhen-Hong Kong Stock Connect scheme. Finally, by comparing the risk spillovers from two directions, the importance of Shanghai stock market gradually rises up with the implementations of Stock Connect schemes.     

货币供应量、利率与银行系统性金融风险——基于CoVaR方法的实证研究

在常态化疫情防控的背景下,商业银行系统性金融风险有上升迹象,这对央行实施货币政策工具和力度的把握提出了更高要求。本文利用条件在险值模型检验了我国货币供应量、利率与银行系统性金融风险的关系。研究结果表明,货币供应量和利率与银行系统性风险之间的关系不是线性的,而是U型的,即存在最优的货币供应量和利率能够使商业银行的系统性金融风险最低。当货币供应量和利率小于这个最优组合时,增加货币供应量和提高利率能够有效降低商业银行的系统性金融风险;而当货币供应量和利率大于这个最优组合时,增加货币供应量和提高利率反而会增加银行系统性金融风险,进而降低商业银行的金融稳定性。     

Assessing the contribution of banks,insurance and other financial services to systemic risk

The aim of this paper is to contribute to the debate on systemic risk by assessing the extent to which distress within the main different financial sectors, namely, the banking, insurance and other financial services industries contribute to systemic risk. To this end, we rely on the $\mathrm{\Delta }\mathit{<font\; color="red">CoVaR</font>}$ systemic risk measure introduced by Adrian and Brunnermeier (2011). In order to provide a formal ranking of the financial sectors with respect to their contribution to systemic risk, the original $\mathrm{\Delta }\mathit{<font\; color="red">CoVaR</font>}$ approach is extended here to include the Kolmogorov–Smirnov test developed by Abadie (2002), based on bootstrapping. Our empirical results reveal that in the Eurozone, for the period ranging from 2004 to 2012, the other financial services sector contributes relatively the most to systemic risk at times of distress affecting this sector. In turn, the banking sector appears to contribute more to systemic risk than the insurance sector. By contrast, the insurance industry is the systemically riskiest financial sector in the United States for the same period, while the banking sector contributes the least to systemic risk in this area. Beyond this ranking, the three financial sectors of interest are found to contribute significantly to systemic risk, both in the Eurozone and in the United States.