The New Systemic Risks

Climate change-induced pan-financial market and the contagion of systemic financial risks are becoming important issues in the financial sector. The paper measures the temperature difference in terms of the degree and direction of deviation of the actual temperature relative to the average temperature of the same historical period. Based on the high-dimensional time-series variable LASSO-VAR-DY framework, we construct a pan-financial market volatility correlation network consisting of 112 Chinese listed companies in banking, insurance, securities, real estate, traditional energy, and new energy, use eigenvector centrality to measure the systematic risk of each firm, and then empirically test the effect of temperature difference on systematic risk under pan-financial market scenario. The results of the study show that (ⅰ) There is a significant difference among the systemic risk of financial sectors such as banking, insurance, and securities in the financial market pan-financial market scenario and the systemic risk when the financial market pan-financial market is not taken into account;(ⅱ) Higher temperature significantly exacerbates systemic financial risk, while colder temperature significantly mitigates systemic risk, but both have an asymmetric effect on systemic risk, and there is sectoral heterogeneity.(ⅲ) From the dynamic evolutionary characteristics, there are significant differences in the response of systemic financial risk to positive and negative temperature shocks;(iv) The results of the systemic risk variance decomposition indicate that the temperature change contributes more to the variance of systemic risk in the banking and securities sectors in pan-financial market;(ⅴ) The contagion source of financial systemic risk shows an obvious path of leaping and changing characteristics, and the contagion source of systemic risk (source of impact) shows the evolution law of "bank → real estate → new energy → temperature difference," which means that the temperature difference has become the contagion source of systemic financial risk. This study provides a reference for preventing and resolving systemic risks under pan-financial market scenario and provides a basis for improving the current macroprudential regulatory framework.

Since the outbreak of the global financial crisis, forestalling and defusing systemic financial risks has been a hot topic of social concerns. In China, with constant development and innovation of the financial system, higher level financial deepening and openness, and economic downside pressure under new normal” economy, risk-prevention becomes much more complicated. In this case, the financial system should better serve the real economy, reduce financial risks and deepen financial reforms—three tasks of China’s financial work. The report of the 19th National Congress of the Communist Party of China further emphasized that the government should improve the financial regulatory system to forestall systemic financial risks. Therefore, ensuring China’s financial stability and preventing systemic risks have become the priority and major challenges for China’s financial regulatory authorities. Accurate measurement of systemic risks is the basis for risk prevention, the improvement of financial regulations, and any effective regulatory actions. However, existing domestic studies measure financial institutions’ systemic risks from only one aspect—systemic risk contribution or systemic risk exposure, and lack a clear distinction between the two measures in theoretical and policy implications. Some scholars even use systemic risk exposure metrics to measure the systemic risk contribution of financial institutions and assess its systemic importance. Actually, the aggregate risks of financial institutions include both risk contribution and risk exposure—the former focuses on systemic importance while the latter underlines systemic vulnerability, so we should take both sides into risk measurement. This paper uses ΔCoVaR and Exposure-ΔCoVaR to comprehensively measure the systemic risks of financial institutions from both sides—systemic importance and systemic vulnerability. This paper finds no significant correlation between the systemic importance and vulnerability of financial institutions in the cross-sectional dimension, but significant correlation in the time-series dimension, which means the systemic importance and vulnerability of financial institutions change simultaneously and periodically. The results imply that, in China, the systemic importance of bank and insurance industry exceed that of securities industry, while the latter’s systemic vulnerability exceeds that of the former. These differences exist persistently in the time-series dimension. The big four” banks have high systemic importance but low systemic vulnerability, while a handful of financial institutions have both significantly high systemic importance and vulnerability. Furthermore, the size of financial institutions’ asset is an important influencing factor of systemic importance, and the leverage is an important influencing factor of systemic vulnerability, while the margin trading of securities has a significant positive effect on systemic vulnerability but no significant effect on systemic importance. This paper accurately measures the systemic risks of 33 listed financial institutions in China from two aspects—risk contribution and risk exposure, and makes a precise assessment on their systemic importance and vulnerability. We also investigate the influencing factors of financial institutions’ systemic importance and vulnerability. These findings help to understand the systemic risks of China’s financial institutions in cross-sectional and time-series dimensions and correct some wrong perceptions in existing academic studies, and further provide useful empirical references and policy suggestions to China’s financial regulatory authorities to forestall systemic risks and improve macro-regulation. The policy implications of the results are mainly reflected in the following three aspects. First, regulators need to select targeted regulatory objectives and policy tools to make differential regulations based on the features of institutions in systemic importance and vulnerability. Second, different institutions are different in systemic importance and vulnerability, so regulatory authorities should pick out key financial institutions through their performance in systemic importance and vulnerability, and enhance the supervision of key institutions. Third, financial regulators are able to choose proper and effective regulatory tools according to the main drivers of systemic importance and vulnerability.

Under growing uncertainty and interdependence, systemic risks are essential for the effective functioning of the global financial system. Therefore, the subject of the proposed study is systemic risks for the global financial system. The goal of this work is to identify and disclose the role of systemic risks in carrying out investment activities. The article solves the following objectives: to identify and reveal key features and characteristics of systemic risks, to identify new challenges in systemic risk management, to identify new manifestations of systemic risks. To achieve the goal of the study, the following methods are used: system-structural, synergetic, method of comparative analysis, method of analysis and synthesis. The study reveals the following results. The main approaches to defining the concept of systemic risks are identified and their comparative analysis is carried out. The main approaches to measuring systemic risks and measurement criteria are identified. The differences between the concepts of systemic and systematic risk are revealed, and the mechanism of their interrelation is identified. New systemic risks in the conditions of global uncertainty are identified. The impact of the COVID-19 pandemic on systemic risks is determined. The main new types of risks and threats to financial stability in the long run are identified. The main directions of response of financial regulatory bodies to new systemic risks are determined. The main effects of the impact of measures to stimulate economic growth on the state of financial markets and investment activities are identified. The conclusions of the study are as follows. It is determined that there is no unanimous definition of systemic risk. Key features of systemic risks are identified, such as unpredictability, large-scale impact, spillover effect, impact on the real sector of the economy, etc. It is determined that when measuring systemic risk there are two problems: the measure of quantitative expression of systemic risk as a unit and the distribution of systemic risk between individual financial institutions. It is revealed that systemic risk can be a source of systematic risks. The COVID-19 pandemic, as an extraordinary macroeconomic shock, is belived to lead to new systemic risks. It is revealed that new types of systemic risks include, in particular, default risks, complexity of the macroeconomic environment, risks of sovereign financing, risk of lack of liquidity. The impact of new systemic risks on investment activities is revealed, in particular, changes in the business models of financial institutions, changes in the strategies of investment funds, lower ratings of debt securities, increasing the cost of debt financing, lack of liquidity.

With the rapid development of the financial market, the volume of financial data has become increasingly large and complex, and the advantages of using big data methods to analyze systemic financial risks have become increasingly prominent. This paper deeply discusses the application of financial big data in systemic risk early warning, aiming to provide theoretical support and practical guidance for improving financial risk prevention and control ability.Traditional systemic risk analysis mainly focuses on individual financial institutions, which has limitations and cannot fully capture the correlation risk exposure and the potential domino effect threat to the stability of financial markets. It is therefore crucial to assess exposures at the level of the financial system as a whole. However, the complexity of the financial system is increasing, and the financial data faced by risk analysis is characterized by huge volume, complex types and strong correlation, which brings challenges to the prevention and control of financial risks.Big data methods, including machine learning, data visualization, information extraction and other technologies, provide new solutions for systemic financial risk early warning. Information extraction technology helps to extract valuable information from unstructured text data, such as investor behavior, sentiment changes, etc. These data can reflect changes in the financial market in real time, and is an important source of supplementary information for systemic financial risk analysis. Machine learning algorithms can integrate multi-source data, automatically learn and identify different credit risk levels, and improve the accuracy and efficiency of risk assessment. Data visualization technology can clearly and intuitively show financial micro-data in the form of graphs and charts, and help analysts quickly understand the relationship and trend between data.This paper analyzes the application cases of big data technology in financial contagion mechanism analysis, financial supervision and other aspects, and demonstrates the practical effect of big data methods in systemic risk early warning. By integrating financial micro-data of various financial sectors and even different countries, and analyzing financial risks as a whole based on the data, big data methods can more comprehensively capture the potential threat of systemic risks and provide timely and accurate early warning information for financial regulators.At the same time, this paper also points out the challenges faced by big data in the early warning of financial systemic risk, such as data security and privacy protection, technical threshold and capital investment, and puts forward corresponding solutions. It is emphasized that financial institutions need to strengthen data protection measures, establish a sound risk prevention and control system, increase scientific and technological investment and personnel training, and ensure that the application of big data technology in the financial industry achieves positive results.To sum up, financial big data plays an important role in the early warning of systemic risks and provides strong support for improving the ability to prevent and control financial risks. In the future, with the continuous development and improvement of big data technology, its application prospects in financial risk early warning will be broader.

This study analyzes systematic and non-systematic credit risk in mortgage portfolios given US loan-level information by controlling for time-varying observable information in relation to the borrower, the collateral and the macro economy. The total risk in relation to rating class default rates is decomposed into systematic and class-specic non-systematic risk by a state space model. The paper finds that the total risk relates to credit quality in a smile-shaped pattern: systematic risk is negatively related and non systematic risk is positively related to average default rate levels. In addition, total risk increases during and after the Global Financial Crisis. The impact of the crisis on systematic risk is persistent whereas the impact on non-systematic risk appears to be temporary. The analysis of regulatory capital suggests that mortgage risk models in conjunction with periodic updating warrant a sucient level of regulatory capital given the current regime. These findings are relevant to prudential regulators who are currently discussing the implementation of a monotone relationship between default probabilities and asset correlations under Basel III.

This study analyzes systematic and non-systematic credit risk in mortgage portfolios given US loan-level information by controlling for time-varying observable information in relation to the borrower, the collateral and the macro economy. The total risk in relation to rating class default rates is decomposed into systematic and class-specific non-systematic risk by a state space model. The paper finds that the total risk relates to credit quality in a smile-shaped pattern: systematic risk is negatively related and non systematic risk is positively related to average default rate levels. In addition, total risk increases during and after the Global Financial Crisis. The impact of the crisis on systematic risk is persistent whereas the impact on non-systematic risk appears to be temporary. The analysis of regulatory capital suggests that mortgage risk models in conjunction with periodic updating warrant a sufficient level of regulatory capital given the current regime. These findings are relevant to prudential regulators who are currently discussing the implementation of a monotone relationship between default probabilities and asset correlations under Basel III.

The multi-layer network nature of systemic risk and its implications for the costs of financial crises

This paper is a compilation and expansion of two earlier papers, one on systemic risk and the other on strategic risk management. Part 1 of the paper proposes a definition and assessment methodology for systemic financial risk that was inspired by systems accident research. Sociologist Charles Perrow found that industrial, aviation and marine systems are prone to failure if those systems are interactively complex and tightly coupled. Using that framework as a starting point, financial crisis research led to the definition of systemic financial risk as a function of financial complexity and excessive leverage. The paper presents practical criteria for applying these parameters, and then profiles the triggering mechanism of systemic financial risk — financial contagion — in a behavioral context consistent with my framework. Part 2 of the paper presents an approach for identifying the weak signals of developing ambiguous threats, such as systemic financial risk manifestation, as well as an approach for economically managing the risk of enterprise-threatening loss. Both parts of the paper are readily assessable to a broad array of financial agents and researchers.

Today's international situation is complex and volatile, and the impact of economic policy uncertainty on systemic financial risk has attracted much attention. In this essay, the TVP-VAR model is used to investigate the mechanism of economic policy uncertainty on systemic financial risk and real effective exchange rate. It is found that its effect on both was characterized by time-varying and nonlinear, and the impact on systemic financial risk is mainly long-term, while the impact of real effective exchange rate is mainly short- and medium-term as long as. Meanwhile, the impact of economic policy uncertainty on systemic financial risk and real effective exchange rate under some specific risk events is more lasting.

PurposeThe purpose of this paper is to investigate the effects of audit client importance on future bank risk and systemic risk in US-listed commercial banks.Design/methodology/approachThe authors use archival research method.FindingsThe authors mainly find that client importance is negatively related with future bank-specific crash risk and distress risk, and also with sector-wide systemic crash risk and systemic distress risk in the future. The authors also report some evidence that these relations become more pronounced during the crisis period than during the non-crisis period. Moreover, the effect of client importance on systemic risk is found to strengthen in banks audited by Big-N auditors, by auditors without clients who restate earnings, and by auditors with more industry expertise.Research limitations/implicationsThese findings contribute to the auditing and systemic risk literature.Practical implicationsThis study has implications for regulating the banking industry.Originality/valueThis study provides original evidence on how client importance affects bank-specific risk and systemic risk of the banking industry.

The real estate development industry includes a wide range of organizations and individuals and, it is inherently risky, with high barriers to entry reflecting the cyclic and capital-intensive nature of the sector, and the typically slow payback period. In certain cases, risk includes the prospect of losing the original investment. The risk is classified into different categories among them, systematic risks and unsystematic risks is one important category for an investor. Moreover, systematic risk is the type of risk caused by external factors that affect all investments and, systematic risk is the probability of a loss associated with the entire market or the segment and cannot be controlled whereas unsystematic risk is associated with a specific industry as well as it is controllable. In Sri Lankan context, though there are few researches on identifying the risk factors, but not explained what is systematic and what is unsystematic. Therefore, this research focus to categorize the already identified risk factors in to two areas as systematic and unsystematic. The data collection is based on the past literature and 25 published articles in indexed Journals, conference papers and reports relating real estate development risk factors up to 2020 were identified. Accordingly altogether 35 risk factors were identified and , ten risk factors which were recognized as most significant to the commercial real estate developments were selected for the purpose of the analysis. Results emphasized that climate changes, natural disaster, pandemic risk, council approval process and changes of tax policies as systematic risk factors. On the other hand, the community acceptability, duration, facility management, brand visibility as well as workforce availability as unsystematic risk factors. Hence having identified the risk factors on this basis will help to measure the beta coefficient of the systematic risk in commercial real estate developments to take better investment decisions while increasing the satisfaction of long-term investment goals as well as contribute to improve the risk management strategies in the real estate industry. Measures should be taken to eliminate the unsystematic risk thus the losses from systematic risk may automatically mitigate.
 Keywords: Systematic Risk, Unsystematic Risk, Risk Management, Real Estate Development

In the realm of portfolio management, the focus lies on constructing a well-diversified portfolio to mitigate unsystematic risk, allowing for the identification and measurement of systematic risk e.g. through uni-factor models, such as CAPM, and multi-factor models, such as APT. This approach is rooted in the belief that, with a sufficiently diversified portfolio, unsystematic risk in theory can be eliminated, making the remaining systematic risk more apparent. While diversification is the means to diversify the unsystematic risk in a portfolio management problem, pooling strategies, with a limited strategy of just expanding the pool members, necessitate a distinct approach to systematic risk. In such scenarios, the challenge lies in disentangling the impact of systematic factors from idiosyncratic influences within a pool. This paper explores the methodologies and considerations unique to pooling situations, shedding light on the complexities involved in identifying and quantifying systematic risk in a pool. In our effort to assess the concept of systematic risk in a pool, we adopt an approach that identifies the defining characteristics of systematic risks, which remain invariant regardless of the number of losses or any manipulations within a finite set of losses. To explore these principles, we find a framework of risk management on sequences in Banach lattices to be particularly suitable. In establishing these principles, we introduce the notion of “systematic compatibility”, signifying invariance to variations in finite changes within a sequence of losses. Consequently, we observe that while systematic risk often possesses an implicit representation in the risk space, it exhibits an explicit representation in the bi-dual space. Moreover, we introduce systematic compatible risk measures and establish their dual characterization. We demonstrate that risk measurement can naturally be represented as a split into a summation of systematic and unsystematic components. In practical applications, we employ these measures to address risk management problems, with a specific emphasis on risk pooling scenarios. In revisiting the traditional “principle of insurance” (POI), we propose an extension called the “principle of pooling” (POP). By showing that the principle of pooling holds if and only if the systematic risk is secure, we investigate this novel concept.

Abstract: When an investor decides to invest in the capital market, investor would be understood that the investment will bring profits and contain risks. In the Capital Market, risk can be classified into Systematic Risk (Market Risk), which is the risk that is experienced by all issuers and cannot be diversified, and Non-Systematic Risk, which is the risk that exists in one particular company or industry so that it can still be minimized or diversified. This research aims to prove the effect of systematic and non-systematic risk on stock returns during the Covid 19 pandemic and after, where the moments that cause systematic risk and also non-systematic risk occur together. The Health Sector is a sector that experiences a direct impact because it acts as a basis for defense against the Covid 19 Epidemic. Systematic Risk uses the Stock Beta, Interest Rate, Foreign Exchange, and Inflation, while non-systematic Risk uses the Solvency Ratio (DER) and Profitability Ratio (ROE). Using a purposive sampling technique, 15 health sector companies were selected during 2018 - 2023. Statistical analysis carried out using Smart PLS 4.0 resulted that Systematic Risk and Non-Systematic Risk did not affect Share Returns of Health Sector Companies in the 2018 - 2023 period Keywords: Systematic Risk, Non-Systematic Risk, Stock Returns

Traditional risk management in stock markets distinguishes between systematic and idiosyncratic risks, whereas regional economic risk is often assessed in aggregate. This study aims to analyse regional economic risk by distinguishing between systematic and idiosyncratic components to evaluate their impact on the stability of development in a constituent entity of the Russian Federation. The study employs statistical analysis using a modified CAPM model adapted for regional economies and content analysis of regulatory legal acts related to socio-economic transformation. The latter is conducted through a keyword registry based on industry classifications included in sanctions packages. The key contribution of this study lies in the development and validation of a methodology for calculating regional systematic and idiosyncratic risks, which serves as a basis for refining strategic development priorities for Russian regions. Systematic risk arises from macroeconomic factors beyond the control of individual regions, with sanctions exerting similar economy-wide effects. Idiosyncratic risk, by contrast, stems from sanctions targeting specific legal entities and individuals. Rostov and Volgograd Oblasts exhibit the highest dependence on nationwide economic conditions, while Sevastopol, the Republic of Kalmykia, and Astrakhan Oblast show lower systematic risk but higher idiosyncratic risk due to regional economic structures. Content analysis indicates shifts in the risk profile of the Southern Federal Okrug in response to imposed sanctions. From a policy perspective, regions with higher systematic risk require greater federal support. The findings help evaluate differences in systematic risk across regions and guide strategies to mitigate its impact through federal budget allocations. Future research could focus on identifying the main factors influencing idiosyncratic risk and creating a unified framework for risk assessment, from individual financial portfolios to the broader macroeconomic level.

The study looked at the relationship between risk and return in the Nigerian stock market using Fama-French five-factor model and Higher Moment Fama-French five-factor model. The employed 90 companies’ stocks that are frequently traded out of the sample size of 113 companies’ stocks. The monthly stock prices, market index, risk-free rate (which was substituted with the rate on Treasury bills), ownership shareholdings, market capitalization, book value of equity, earnings before interest and taxes, and total assets were the data used in this study. The entire sample period covered from 2005-2020. The data was extracted from the Nigerian Group of Exchange (NGX) website, the Central Bank of Nigeria (CBN) website, and the Standard and Poor (S&P) database. The Fama-MacBeth two-step regression method was employed. It was found that systematic risk has significant negative effect on return while unsystematic risk has significant positive effect on return. The study concluded that the long standing view of hypothesised positive relationship between risk and return does not hold in the Nigerian stock market and the assumption that market risk is the only determinant of return is invalidated. Also, systematic coskewness risk is an important risk factor in the Nigerian stock market and higher moment FF5F model and CoFF5F model is superior to FF5F model. The study recommended that the investors should focused on how their investment return co-move with other dimension of risk such as unsystematic risk, systematic cokewness risk, systematic cokurtosis risk and non-market risk apart from the systematic risk.