A Hybrid Approach for the Assessment of Risk Spillover to ESG Investment in Financial Networks

In this paper, we present a framework for evaluating risk contagion by merging financial networks with machine learning techniques. The framework begins with building a financial network model based on the inter-institutional correlation network, followed by analyzing the structure and overall value changes of the financial network under the stress of a liquidation shock. We then examine the network's evolution over time. We also use three machine learning techniques to assess the abnormal volatility of important financial institutions in the financial network. Finally, we evaluate the spillover effects of risk volatility in financial networks on ESG investments. The findings suggest that the financial network becomes more robust as the connections among financial institutions become more intricate. This leads to an improvement in the ability of the financial network to withstand systemic risk events. Overall, our study provides evidence of the negative impact of risk spillovers in financial networks on ESG investments, highlighting the need for a more sustainable and resilient financial system. This innovative framework combining financial network modeling and machine learning prediction provides a deeper understanding of the evolution of financial networks and a more accurate evaluation of abnormal volatility in financial networks.

What impacts foreign capital flows to China's stock markets? Evidence from financial risk spillover networks

This study investigates the spillover effects between foreign capital in China's stock market and 11 other financial assets (e.g., major stock markets, financial products, and interest rates). Employing the Diebold–Yilmaz framework (VAR– and QVAR based) and a complex network approach, we found that China's stock market foreign capital is significantly connected to international markets;the U.S., Hong Kong, and the U.K. were the top net risk transmitters. Moreover, money flows were subject to global instability and emergencies, including Brexit, the Sino-US trade war, and the COVID-19 pandemic. The operation of the Northward Fund is sometimes ahead of many developed markets, indicating that capital movement was somehow "Smart Money”. Our study provides a new idea for risk warning and systematic risk prevention.

The Impact of the COVID-19 Pandemic on the Interconnectedness of Stocks in Bursa Malaysia

Coronavirus (COVID-19) pandemic has a massive impact on economic growth and the stock market. Due to COVID-19's high transmission rate, a movement control order (MCO) was enforced by the Malaysian government in four stages. Consequently, this situation has affected the stability and the relationship of stocks listed on Bursa Malaysia. Thus, this study is motivated to investigate and visualize a correlation of securities listed on FTSE Bursa Malaysia KLCI by employing a network analysis approach. The limitation of correlation analysis is that it only provides the strength and direction of the association, which is not visually shown in the graph. As a result, the network analysis technique is used to highlight the correlations between the stocks. The input of the network is based on the rate of return of each stock. The data is divided into two parts. The first duration is the period before implementation of MCO which is from 17th December 2020 to 17th March 2020. The second duration is during four stages of MCO which cover from 18th March 2020 to 3rd May 2020. There are changes in the interconnection between stocks in which seventeen stocks increased in the correlation measure during MCO. The importance of stocks is determined by applying centrality measures to disclose a topological structure of a network. This study finds that the stock with the highest connectivity based on the degree centrality before the MCO was unable to maintain the status as the central hub during the MCO. The results can assist the market participant to strategize the asset allocation to obtain well diversified portfolio. selection.

Systemic risk of multi-layer financial network system under macroeconomic fluctuations

As the global economy continues to integrate, COVID-19 is affecting businesses around the world, causing the financial system to become more complicated. The complicated relationship between various agents in the financial system makes potential hazards more easily transmitted. Most studies of systemic risks have focused on single-layer networks, and macroeconomic fluctuations have not been quantified in multi-layer models of financial networks. In this paper, three different macroeconomic shock scenarios (showing upward, downward, and random trends) are constructed to affect the firm's business activities, and a multi-layer financial network model is developed to simulate systemic risk under macroeconomic fluctuations. Firms with medium and high leverage and small asset sizes, as well as banks with smaller asset sizes and fewer bank-firm credit linkages, are found to be more likely to default. The study also found that average firm leverage exhibits two inflection points, causing banks' default probabilities to "rise, then fall, and then rise, " with the inflection point value being the lowest under the upward trend of macroeconomics. In addition, the higher the ratio of firm loans to total bank assets, the more likely the bank is to default. Appropriate loan maturity extension has also helped to reduce systemic risk, especially in light of the macroeconomic downward trend. Furthermore, improving the capital adequacy ratio can reduce the bank's default probability under macroeconomic fluctuations.

Network resilience in the financial sectors: advances, key elements, applications, and challenges for financial stability regulation

Security against systemic financial risks is the main theme for financial stability regulation. As modern financial markets are highly interconnected and complex networks, their network resilience is an important indicator of the ability of the financial system to prevent risks. To provide a comprehensive perspective on the network resilience of financial networks, we review the main advances in the literature on network resilience and financial networks. Further, we review the key elements and applications of financial network resilience processing in financial regulation, including financial network information, network resilience measures, financial regulatory technologies, and regulatory applications. Finally, we discuss ongoing challenges and future research directions from the perspective of resilience-based financial systemic risk regulation.

Financial Risk Meter for Emerging Markets

In this paper, the daily systemic risk measure FRM (Financial Risk Meter) is proposed for Emerging markets (FRM@ EM). The FRM@ EM is applied to capture systemic risk behavior embedded in the returns of the 25 largest EMs’ Financial Institutions (FIs), covering the BRIMST (Brazil, Russia, India, Mexico, South Africa, and Turkey), and thereby reflects the financial linkages between these economies. The results indicate that the FRM of EMs’ FIs reached its maximum during the US financial crisis following the COVID-19 crisis. In addition, we find that the Macro factors explain the BRIMST's FIs with various degrees of sensibility. Moreover, we propose a robust and well-diversified tail-event and cluster risk-sensitive portfolio allocation model named uplifted Hierarchical Risk Parity (upHRP) and compare it to more classical approaches. Results indicate that the upHRP approach provides better diversification. Moreover, the upHRP portfolio overweights low-central FIs and underweights high-central ones.