Level Of Systemic Risk Research Articles

Optimal multivariate financial decision making

Agents who pursue optimal portfolio choice by optimizing a univariate objective (e.g., an expected utility) obtain optimal payoffs that are increasing with each other (situation of no diversification). This situation may lead to an undesirable level of systemic risk for society. A regulator may consider a global perspective and aim to enforce diversification among the various portfolios by optimizing a suitable multivariate objective. We explain that optimal solutions satisfy a notion of multivariate cost-efficiency and provide an algorithm to obtain multivariate cost-efficient payoffs.We also assess the cost of diversification and provide the strategy that the regulator should pursue for obtaining the desired level of diversification.

Analysis of systematic risk of global ETFs with maximum ESG rating

The article is discussed to the analysis of ESG rating factors, profitability and risk in relation to global ETFs. The hypothesis under study is that global ETFs with a maximum ESG rating are heterogeneous in terms of systematic risk level. Based on the FOREL clustering algorithm, two ETF clusters are identified. Thus, an investor, having a priority maximum ESG rating and a certain level of β-coefficient, can select any object from the cluster, since all ETFs in the cluster are similar in level of systematic risk.

Forecasting crash risk in U.S. bank returns—The role of credit booms

We propose a new credit variable called probability of aggregate loan trend deviations (pltd) that forecasts crash risk in U.S. bank stock returns. Compared to more classic measures of credit, the main innovation in pltd is that it incorporates a large amount of macroeconomic information in the form of money and interest rate variables known to drive the aggregate loan supply. Positive changes in pltd increase the level of systemic risk in the economy and also trigger lower bank returns and cash flows up to horizons of one year. To disentangle the effect our credit variable has on future bank returns we break down the total variance into individual components and estimate series for news of discount-rates and cash flows. We find that pltd only leads the cash-flow news component without significant effects on the discount-rate news. Finally, we show that pltd is not only useful for predicting the mean of future bank stock returns in-sample and out-of-sample but is also an important driver of the tail of the return distribution proxied by CATFIN.

Reducing systemic risk in a multi-layer network using reinforcement learning

This paper introduces a novel framework to assess and manage systemic risk in a multi-layer financial network by taking advantage of reinforcement learning (RL). The reduction of systemic risk in the financial network is achieved by applying the deep deterministic policy gradient algorithm (DDPG) to reorganize the interbank lending structure of the network into an orientation that better mitigates the spread of contagion. The reorganization procedure itself was constrained in order to preserve the balance sheet of every bank. To achieve this, we develop a constraint DDPG model consisting of a safety layer coupled with a linear mapping to satisfy the total borrowing and lending amounts of each bank. Moreover, we propose a new multi-layer DebtRank (DR) algorithm taking into account how contagion spreads from one layer to another. Testing against networks of varying size and depth, our DDPG agent was able to reduce systemic risk levels by significant amounts, suggesting the feasibility and utility of employing RL in managing systemic risk through aiding regulatory policy design. We observe an increase in sparsity and an increase in network dissimilarity between the different layers of the network after optimization.

Did COVID-19 Affect the Financial Performance and Risk of International Shipping Companies?

This study focuses on the shipping sector and examines how it has been affected by the COVID-19 pandemic. More specifically, the paper examines the extent to which stock price returns, corporate systematic risk, and key financial performance variables were affected, as well as how all these variables correlated with the spread of COVID-19. Moreover, the paper examines the correlation of these variables with key shipping indexes, such as the Baltic Dry Index and Baltic International Tanker Route, and bunker prices, and how this correlation changed. Our sample consists of shipping companies listed on US stock exchanges, and the study period is 2015–2021. The findings reveal that the shipping companies’ level of systematic risk increased after the pandemic. In addition, the beta coefficient of shipping companies is more affected by changes in bunker prices since the pandemic, and the average daily returns and bunker prices have a lower correlation after the pandemic.

Enterprise Financing Risk Control of Machine Learning Combined with Blockchain Technology

In order to scientifically and reasonably control the financing risk of supply chain enterprises and minimize the financing risk of the enterprises, this paper introduces the relevant theories of machine learning algorithm and blockchain technology, proposes an enterprise financing risk control model based on the combination of machine learning algorithm and blockchain technology, and uses system dynamics to dynamically evaluate the system risk. Finally, the specific changes of each subsystem over time are obtained. Through the risk evaluation results, it can be seen that the risk level of the blockchain system has decreased from the initial 4.3 to 0.3 after two months of transition, and the risk control effect is very significant.

The Bayesian Method in Estimating Polish and German Industry Betas. A Comparative Analysis of the Risk between the Main Economic Sectors from 2001–2020

This paper examines the long‑term dependence between the Polish and German stock markets in terms of industry beta risk estimates according to the Capital Asset Pricing Model (CAPM). The main objective of this research is to compare the Polish and German beta parameters of five Polish and three German sector indices using the Bayesian methodology in the period 2001–2020. The study has two detailed aims. First, to develop a modified, Bayesian approach (SBETA model) that generates significantly more precise beta than the traditional model. Second, to compare the results of different time‑varying industry betas in the Polish and German economies, giving a simple investment recommendation, i.e., which sector could be classified as aggressive or defensive. The betas were time‑varying in both markets but less persistent in the German industries, which seems characteristic of an advanced economy. The Banking sector betas were the highest in both markets, implying the aggressive nature of that industry in the last twenty years. For the Polish market industry, the betas of Construction, IT, Food and Drinks, and Telecom were classified as defensive. For the German economy, the Technologies (IT) sector was also classified as aggressive, but Telecom was defensive. The results give a valuable insight into the systematic risk levels in Poland and Germany, reflecting the investors' learning process and indicating that Polish Banking and German technologies outperformed the market in the last twenty years.

BANK CONFERENCE CALLS: A TEXTUAL ANALYSIS APPROACH

While firms are increasingly providing conference calls to help investors better understand and contextualize financial information, banks’ public disclosures are more opaque than that of non-banks. This study examines the likelihood and information content of bank conference calls as well as related market outcomes. The empirical results document that a higher level of bank systemic risk is associated with a lower likelihood of the bank providing earnings conference calls. However, banks with a higher level of disclosure transparency are more likely to provide conference calls to investors. Furthermore, the empirical evidence suggests that managers demonstrate a consistent behavior in terms of disclosure contents during conference calls. Higher obfuscation is associated with higher bank systemic risk and lower disclosure transparency. When banks have a higher level of disclosure transparency, information components in conference calls are higher, suggesting that managers disclose more information during conference calls. Finally, consistent with theoretical predictions, obfuscation (information) components in bank conference call disclosures are associated with higher (lower) information asymmetry (measured by market illiquidity and analyst forecast errors). Banks are black boxes. Money goes in, and money goes out, but the risks taken in the process of intermediation are hard to observe from outside the bank. —Donald P. Morgan (2002)

Safety Risk Assessment of Air Traffic Control System Based on the Game Theory and the Cloud Matter Element Analysis

With the ever-increasing demand for air traffic over the years, safety risk assessment has become significant in maintaining the operational safety of the air transport system for long-term development towards sustainability. This paper conducts a safety risk assessment of the air traffic control (ATC) system based on game theory and cloud matter element analysis. The safety risk of the ATC system is evaluated from four aspects, including human, machine, environment, and management. The Nash equilibrium is introduced from game theory to weigh the indicators. The cloud matter element assessment adopts the cloud model from fuzzy sets and probability theory to replace the certain value in conventional matter element theory, which takes the randomness, ambiguity, and incompatibility of the indicators into consideration. In this sense, the safety risk level of the ATC system can be evaluated by calculating the correlation degree of the standard cloud matter element between the indicators and the risks. This paper expands the research scope by introducing and combing game theory and cloud matter element analysis. Furthermore, the applicability and the robustness of the method are examined with a case study of the ATC system, which enriches the existing literature and points out the direction for future work.

The contribution of (shadow) banks and real estate to systemic risk in China

We empirically evaluate how accounting and financial variables affect the level of systemic risk in traditional and shadow banks, and in real estate finance services in China over the period 2006–2019. We also conduct some stability analysis by evaluating the impact of crisis sub-periods. We find that systemic risk increases in the Size of large financial institutions, particularly shadow entities, while it is insensitive to the Size of real estate finance services. Real estate finance services are instead particularly sensitive to Maturity Mismatch and Leverage. Finally, systemic risk differs across state and non state owned banks.

The correlations among COVID-19, the effect of public opinion, and the systemic risks of China’s financial industries

In this paper, we use the improved event study method to analyze the changes in the systemic risk trends of various financial sectors after the outbreak of COVID-19. The analysis is based on the daily return data of 45 Chinese financial institutions from January 2, 2019, to November 30, 2020. The improved event study method is also used to explore the horizontal, trend, and public opinion effects of the systemic risk. The empirical analysis results show that: (1) the occurrence of COVID-19 will increase the level and volatility of systemic risk in the financial industry. (2) After the outbreak of COVID-19, there is no horizontal effect in all financial industries. The banking and securities industries have significant and longer-lasting positive trend effects, and from the perspective of trend effects, in the face of external shocks, the banking industry is more stable than the securities industry. (3) After the outbreak of COVID-19, the banking and securities industries have a public opinion effect, which is gradually weakened; but there is no public opinion effect in the insurance industry.

Data-Driven Intelligent Risk System in the Process of Financial Audit

Financial auditing is an important part of government control, and monitoring the risks of the financial system helps maintain the stability of a country’s financial system. This paper aims to study the operation of data-driven intelligent risk analysis system in the process of financial auditing. When the risk level of the financial system reaches a certain warning point, it needs to be adjusted, conduct strict audits on high-risk institutions, strengthen cooperation with financial audit institutions, and make them more systematic. Risk control within a reasonable range will neither have a significant impact on the financial system nor increase the impact on the industry. This paper proposes to integrate the internal and establish an internal control system for the financial system.. If the internal control of the financial system cannot be sustained, financial innovation will cause great damage to financial institutions if the compensation is not good. Especially in terms of access strategy, it has made a strong contribution to economic innovation. The experimental data in this paper show that more than 25% of data-driven intelligent risk analysis systems have begun to be used in the process of financial auditing. At the same time, its utilization rate is significantly increasing with the development of technology. This paper introduces the relevant content of financial auditing, analyzes the auditing systems in different fields, integrates resources, taps on multisectoral needs, establishes a cross-departmental governance system, and improves financial stability and the efficiency of a wide range of social services.

Interbank contagion risk in China under an ABM approach for network formation

AbstractThis paper uses an agent‐based model to construct an interbank network for the Chinese interbank market using a sample of 299 commercial banks from 2014 to 2019. We analyze the importance and vulnerability of banks using the DebtRank algorithm. Our results show that the Chinese interbank market bears a certain level of systemic risk, especially among lower‐tiered banks. The results also show a bank is more vulnerable if it has a higher interbank lending ratio and greater financial connectivity. Meanwhile, a bank is more influential if it has a larger net worth and greater financial connectivity.

A robust hybrid method using dynamic network analysis and Weighted Mahalanobis distance for modeling systemic risk in the international energy market

This paper proposes a novel hybrid method called Weighted Turbulence, which combines the dynamic network and weighted Mahalanobis distance to measure the overall systemic risk level of the international energy markets system. We simultaneously capture two essential drivers of systemic risk on international energy markets, including the abnormal price behavior of the single market and the interconnectedness among different energy markets, as the fundamental points of our modeling. It makes our proposed model can characterize the larger influential level of the abnormal behavior of the core energy market than that of the periphery markets on the systemic risk level and eliminate the negative impact of the non-systematic markets of the targeted markets system on the systemic risk measuring, which few existing schemes can achieve that. The empirical study further confirms the effectiveness and robustness of our proposed model. It can accurately detect the spikes of systemic risk in international energy markets, and the measuring results are unaffected by the interference of the noise data. The proposed method is applicable in practical systemic risk measurement, which has valuable theoretical and practical implications for related energy market participants.

Does the source of oil price shocks matter for the systemic risk?

This paper investigates the impact of different oil price shocks on systemic risk under different market conditions. We show that the negative impact of negative oil price shocks on systemic risk is greater than the positive impact of positive oil price shocks. Systemic risk is always negatively affected by oil-specific demand shocks but positively affected by oil supply shocks when the market is under medium and low systemic risk levels. By testing the effect of crises, we find that the influence of positive and negative oil price shocks on systemic risk was declined due to the COVID-19 pandemic.

A Collaborative optimization approach for grid equipment overhaul/retirement strategy considering system effectiveness and risk

In order to improve the science of grid equipment overhaul and decommissioning decisions, a collaborative optimization method of equipment overhaul/decommissioning strategy considering system effectiveness and risk is proposed. The interaction effect between overhaul and decommissioning is analysed from the perspective of system effectiveness, and 2 attributes of integrated cost input and system reliability improvement are proposed, and then a prospect model with cost/benefit attributes is established based on the prospect theory. Then, with the maximum integrated prospect value as the optimization objective, a collaborative optimization model of overhaul/decommissioning strategy is established with the system risk level as the constraint. Finally, the effectiveness of the proposed method is verified by taking the transformer equipment of an urban area distribution network as an example.

Evaluating the Systemic Importance of China’s Banks via Adjacency Information Entropy

Information entropy can be utilized to assess the complexity of the Banking System and the uncertainty of association information between banks. Based on information entropy and the balance sheet data of 167 banks from 2011 to 2020, we proposed a centrality indicator based on Adjacency Information Entropy, then applied it to directed weighted and directed unweighted interbank networks to identify banks with systemically important status, and compared the results with those of traditional centrality indicators. The outcomes show that the results of centrality indicator based on Adjacency Information Entropy are more accurate than those of traditional centrality indicators in identifying banks with systemically important status in interbank networks. Six large state‐owned commercial banks, nine joint‐stock commercial banks, two policy banks, and two urban commercial banks with large assets are identified as systemically important banks based on the proposed centrality indicator, which is highly consistent with the list of systemically important banks published by the Chinese banking regulator. Among the systemically important banks, changes in systemic risk levels of joint‐stock commercial banks and urban commercial banks are more susceptible to national policies than those of large state‐owned commercial banks. The findings of this paper have certain reference significance for strengthening the supervision of banks with systemically important status in interbank networks and formulating targeted policies for regulating large commercial banks and small‐ and medium‐sized commercial banks.

Peer-to-peer multi-energy distributed trading for interconnected microgrids: A general Nash bargaining approach

Energy sharing has significant potential in improving system energy utilization, improving economic efficiency, and reducing the impact on power system. The multi-microgrids distributed coordinated operation model is proposed in this paper to provide a reference for the coordinated operation of multi-microgrids considering factors such as electric and heat demand response, multi-energy interaction and uncertainties. First of all, the system operation framework is introduced and the function of each part is analyzed. Secondly, the multi-microgrids cooperative operation model is established, and generalized Nash bargaining method is used to assign total operating cost to ensure the enthusiasm of each microgrid to participate in cooperation. Then, considering the uncertainty of wind power and load in microgrids, a stochastic programming model based on conditional value-at-risk is established. Furthermore, alternate direction multiplier method is used to solve the proposed model to protect the data privacy of each microgrid participating in cooperation. Finally, through the analysis of calculation example, it is verified that the proposed model can reduce the operating cost of each microgrid and maintain enthusiasm of each microgrid for cooperation. In addition, the convergence and accuracy of algorithm are explained, and the influence of different connection modes and the interaction limit of electric and thermal power on the operating cost of microgrids is compared. Moreover, stochastic programming model also provides a reference plan for the trade-off between economic benefits and risk level of multi-microgrids system.

Mortgage-related bank penalties and systemic risk among U.S. banks

We analyze link between mortgage-related regulatory penalties levied on banks and the level of systemic risk in the U.S. banking industry. We employ a frequency decomposition of volatility spillovers (connectedness) to assess system-wide risk transmission with short-, medium-, and long-term dynamics. We find that after the possibility of a penalty is first announced to the public, long-term systemic risk among banks tends to increase. From the dynamic perspective, bank penalties represent an overlooked risk as they do not increase systemic risk immediately, but the risk accumulates and propagates over the long term. In this respect, bank penalties resemble still waters that run deep. In contrast, a settlement with regulatory authorities leads to a decrease in the long-term systemic risk. Our analysis is robust with respect to a number of relevant criteria.

A risk-based decision support system in local railways management

The management of a railway infrastructure involves the collaboration of different experts and the continuous choice between alternatives under the constraint of available economic funds with the need to maintain high levels of operational safety. This is even more significant for local railway networks where financial resources are limited. For this reason, decision-making tools play a central role, allowing to evaluate interventions based on their effects on system risk level. In this sense, the Risk Based-Decision Support Systems are becoming increasingly popular in railway sector, where are used for the optimization of the maintenance of the elements of the infrastructure, such as the track, civil works, level crossing and so on. A unified vision is not yet widespread and the application of the risk assessment and intervention programming process considering all the elements of the network and their interaction is still lacking. The aim of this work is to present a quantitative risk management methodology for railways to support decision-making processes for quantification and control of all the factors affecting the safety level of railway systems characterized by limited resources. The methodology is then applied on a local railway for the evaluation and prioritization of safety improvements interventions.