Risk Of Financial Institutions Research Articles

Reputational Risk for financial institutions: a proposal of quantitative approach

The reputation of an institution refers to its public image in terms of competence, integrity and trustworthiness, which results from the awareness of its stakeholders. The related risk, i.e. “Reputational Risk”, is defined as the current or prospective risk of a decline in profits or capital resulting from a negative perception of the financial institution image by clients, counterparties, shareholders, investors or supervisory authorities. In this scenario, the reputation and the assessment of the associated risk component represent a decisive factor for ensuring long-lasting profitability. In recent years, the importance of managing and monitoring Reputational Risk is growing in importance with supervisory authorities, but nevertheless, there are no specific guidelines yet that the institutions can follow. The lack of a precise orientation means that the risk component is still considered discretionary, subjective and highly prone to interpretation. Considering that in the economic literature there is not a universally accepted approach, the aim of the paper is to provide a quantitative and objective methodology, a Quantitative Model, to assess the Reputational Risk in order to overcome the limits of a qualitative approach, by using exclusively numerical and objective analysis drivers, and to meet the increasing attention of the supervisory authorities on the issue. The Quantitative Model structure allows firms to study and to monitor the phenomenon from a managerial point of view. This approach provides financial institutions, in particular the Risk Management Department, a model to evaluate the reputational risk arising from economic magnitudes that characterise the business model of the financial institution. This means that the quantitative Model enables financial institutions to steering possible negative situations and promptly intervening with any corrective measures or actions deemed appropriate.

An analysis of methods for assessing information security risks of financial institutions

The paper presents an analysis of the existing methods for assessing information security risks, their features, advantages and disadvantages, as well as determines the possibility of using such techniques for assessing information security risks in financial institutions. Criteria for comparing information security risk assessment methods have been formed, the advantages and disadvantages of the methods are described. It is shown that, despite the requirements of regulators for assessing information security risks, most of the regulatory documents deal with operational risks. The evaluation of information security risks of credit and financial institutions does not have sufficient regulation and formalization.The authors substantiate the necessity of developing a method for assessing information security risks for credit and financial organizations, taking into account the features of risk assessment inherent to the mentioned organizations. The paper considers the need to create lists of existing threats to the credit and financial sector and their linking to existing vulnerabilities to optimize the process of assessing information security risks. The development of a methodology for assessing information security risks will increase the degree of compliance of credit and financial institutions with the requirements of international, state and industry standards through an optimal set of protection measures and models for evaluating information security risks.

How can an economic scenario generation model cope with abrupt changes in financial markets?

PurposeIt is quite possible that financial institutions including life insurance companies would encounter turbulent situations such as the COVID-19 pandemic before policies mature. Constructing models that can generate scenarios for major assets to cover abrupt changes in financial markets is thus essential for the financial institution's risk management.Design/methodology/approachThe key issues in such modeling include how to manage the large number of risk factors involved, how to model the dynamics of chosen or derived factors and how to incorporate relations among these factors. The authors propose the orthogonal ARMA–GARCH (autoregressive moving-average–generalized autoregressive conditional heteroskedasticity) approach to tackle these issues. The constructed economic scenario generation (ESG) models pass the backtests covering the period from the beginning of 2018 to the end of May 2020, which includes the turbulent situations caused by COVID-19.FindingsThe backtesting covering the turbulent period of COVID-19, along with fan charts and comparisons on simulated and historical statistics, validates our approach.Originality/valueThis paper is the first one that attempts to generate complex long-term economic scenarios for a large-scale portfolio from its large dimensional covariance matrix estimated by the orthogonal ARMA–GARCH model.

Estimating the market risk of clean energy technologies companies using the expected shortfall approach

In this study, we assess and estimate the market risk of firms that use clean energy technologies in their production process by using the expected shortfall (ES) regression-based backtest approach and value at risk (VaR) method. We use the WilderHill Clean energy Index from 2001 to 2018 and jointly assess the tail distribution of the risk model. Our findings show that ES forecast results are not misleading during the financial turmoil or during the full sample period. Therefore, our findings indicate that the ES approach can be an alternative valuable diagnostic tool to VaR for the estimation of market risk for financial institutions and regulators.

Information content and market liquidity in the fixed income market: Evidence from the swaption market

In this paper, I investigate the relationship between market liquidity and the information content of implied volatility (IV) in the fixed income market. For its part, financial regulation including Basel III relies heavily on historical volatility (HV) in capturing the financial risk of financial institutions. One of the main reasons for this is that many countries may not necessarily obtain a meaningful measure of IV in their option markets because of the lack of liquidity. Using US dollar and Japanese yen swaption data, I find that the information content of IV critically depends on the measure of liquidity. This finding empirically justifies the use of HV instead of IV as a financial risk measure, especially in countries where option market liquidity is low.

Choice of Environmental and Economic Path for Building a Supply Chain Financial Cloud Ecosystem under the Background of “Internet +”

With the development of the Internet age, the gradual deepening of reforms, and the continuous improvement of marketization, the financial ecological environment is directly related to the risk of financial institutions’ loans and the enthusiasm to support local economic development. Capital is accompanied by the physical development and evolution of the production and circulation of goods. After thousands of years of historical development and evolution, it has formulated its own development law and internal logic. In view of the unclear environmental and social evaluation indicators in supply chain research and the model’s failure to accurately describe the real supply chain network, an adaptive dynamic relaxation approximation algorithm model is proposed. This model introduces upstream and downstream transit stations as distributors and as a hub between customers, the transportation mode is improved to horizontal and vertical coordinated transportation, and multiple transportation methods are used for transportation. In this paper, the experimental group adopts a new model based on the approximate algorithm of dynamic relaxation factors, and the control group adopts the original old model. The two groups conduct comprehensive data analysis on social indicators, self‐indicators, initial indicators, economic indicators, and environmental indicators in each year. Starting from the dimension, comprehensively consider the selection of indicators to measure the level of enterprise performance. The experiment proves that the transportation loss in the green supply chain is between the new model and the old model. When the number of partners is small, the degree of the optimal solution between the new and old models is not obvious, and the degree of optimization of the new model relative to the old model is increasing. It has a significant statistical difference (P < 0.05). This shows that efforts to build a good financial ecological environment and an honest and reliable and healthy economic ecological environment are of great importance for promoting the continuous and rapid growth of the social economy.

A deep metric learning approach for weakly supervised loan default prediction1

To deal with the systematic risk of financial institutions and the rapid increasing of loan applications, it is becoming extremely important to automatically predict the default probability of a loan. However, this task is non-trivial due to the insufficient default samples, hard decision boundaries and numerous heterogeneous features. To the best of our knowledge, existing related researches fail in handling these three difficulties simultaneously. In this paper, we propose a weakly supervised loan default prediction model WEAKLOAN that systematically solves all these challenges based on deep metric learning. WEAKLOAN is composed of three key modules which are used for encoding loan features, learning evaluation metrics and calculating default risk scores. By doing so, WEAKLOAN can not only extract the features of a loan itself, but also model the hidden relationships in loan pairs. Extensive experiments on real-life datasets show that WEAKLOAN significantly outperforms all compared baselines even though the default loans for training are limited.

Gravitational and intellectual data analysis to assess the money laundering risk of financial institutions

Gravitational and intellectual data analysis to assess the money laundering risk of financial institutions

Corporate governance and the insolvency risk of financial institutions

We investigate whether shareholder-friendliness of corporate governance mechanisms is related to the insolvency risk of financial institutions. Using a large sample of U.S. financial institutions over the period 2005–2010, we find that corporate governance is positively related to the insolvency risk of financial institutions as proxied by Merton’s distance to default measure and credit default swap (CDS) spread. We also find that “stronger” corporate governance increases insolvency risk relatively more for larger financial institutions and during the period of the financial crisis. Lastly, our results suggest that shareholder-friendliness of corporate governance mechanisms is viewed unfavorably in the bond market.

CREDIT WORTHINESS OF AGRICULTURAL ENTERPRISES

Background. Agriculture is a source of increased credit risk for banks and other lenders, which requires special approaches to assessing the credit worthiness of the enter­prises in this industry. The assessment improving of the credit worthiness of the agricu­ltural enterprises is one of the key scientific and practical problems. The aim of the article is to identify and systematize the factors influencing the level of credit worthiness of the agricultural enterprises. Materials and methods. The theoretical basis of the research is a systematic app­roach that has been used to identify and analyze the factors of credit worthiness of the agricultural enterprises. The methodological basis consisted of system-structural, compa­rative analysis and logical generalization methods. The information basis of the research is analytical materials, scientific researches, results of own researches. Results. The purpose of assessing the credit worthiness of the agricultural enterprise is to obtain a quantitative and qualitative assessment of the borrower’s activities by a cre­ditor. Based on this, creditor assesses the risk of lending and decides about the possibility and con­ditions of lending or refusal to the borrower. Existing methods of assessing the credit worthiness of the agricultural enterprises do not often take into account the peculiarities of their activities, which directly affect the ability of these enterprises to repay the loan. During the assessing the credit worthiness of the agricultural enterprise, it is appropriate to take into account financial indicators that characterize its financial position and non-financial factors that describe the actual and potential conditions of its management. Based on the results of systematization of existing approaches and own conclusions, it is proposed to classify the factors influencing the credit worthiness of the agricultural enterprises by the source of influence on the company, the possibility of influence by the company, the method of measurement, nature. Conclusion. The article presents the author’s vision of the classification factors influencing the agricultural enterprises ability to obtain credit and expand their list by addition them with factors of soil quality and organic production, which will reduce the credit risk of financial institutions in lending.

Assessing Systemic Risk Based on the CARE System

Expectile is a coherent and elicitable risk measure that responds to the catastrophic losses more properly than a quantile-based approach. In this paper, we extend the univariate conditional autoregressive expectile (CARE) model of Kuan et al. (2009) to a multivariate CARE system to model the systemic risk of financial institutions. We propose an estimation method and derive its asymptotic properties. In the empirical study, we model the 12-variate CARE system on the global systemically important banks' (G-SIBs') returns for the period Jan 2015 to Jun 2020. We show that the CARE system identifies the asymmetric transmissions of the banks' systemic risk, i.e., the square positive and the square negative lagged returns generate different patterns of associations in the expectile connectedness. In addition, the out-of-sample Expectile-based Value-at-Risk prediction captures the 2020 mid-March US trading curb turbulence.

Incidence Network, Risk Spillover and Systemic Risk of Financial Institutions

Systemic risk is closely related to the risk contagion of financial institutions. The risk of a single financial institution will spread among financial systems through the complex incidence network among financial institutions, and lead to the outbreak of crisis. However, the risk contagion paths of different financial institutions are different, and the degree of impact is significantly different. The research of using the reduced-form approach mostly focuses on measuring the risk level of a single institution in the system, but there is relatively little literature on the measurement of risk spillover level among financial institutions and different types of financial sectors. The existing research for complex network model analysis is still focused on the construction of multiple symmetric correlation matrix, and the analysis of financial institutions through the construction of asymmetric correlation matrix is still a small number. Based on the classification of commercial banks, securities companies, insurance institutions and other types of financial institutions, this paper measures the impact of specific financial institutions on systemic risk from the perspective of risk spillover degree of financial institutions. Then, according to the nonparametric Granger causality test, the incidence matrix of financial institutions is constructed, and the financial institutions are used as nodes to depict and describe the incidence network indicators and network center properties of financial institutions. At the same time, the risk area is divided by combining the risk spillover index and the input/output value of financial institutions, so as to clearly identify the vulnerability of risk tolerance and the infectivity of risk transmission of financial institutions, in order to judge the risk prevention core and the key of targeted measures. The empirical results show that banking financial institutions are the core of China’s financial incidence network, and they have high stability. Securities financial institutions are at a high level in the level of systemic risk spillover and the degree of financial network connection, and they are the key sectors for China’s systemic risk prevention. The vulnerability and infectivity of different financial institutions are different, and the vulnerability and infectivity of the same financial institution are also different. The increase of capital scale does not mean the increase of risk contagion and vulnerability. Therefore, the risk identification of financial institutions should be comprehensive, not limited to a single standard. On the basis of focusing on the scale of financial institutions, combined with the direction, path and degree of risk impact of specific financial institutions, the risk prevention core is determined. At the same time, according to the different characteristics of risk vulnerability and risk contagion of financial institutions, we should implement targeted regulatory measures.

Supply Chain Financial Risk Evaluation of Small- and Medium-Sized Enterprises under Smart City

Prevention and control of risks are an eternal theme of financial institutions. Although, to some extent, the emergence of supply chain finance can enhance the financing capacity of small- and medium-sized enterprises (SMEs) and reduce financial risks of financial institutions, with the development of smart city and smart finance, the financial risks of SMEs are more complex, infectious, dormant, and difficult to accurately identify and measure. Facing this status, financial institutions have been required to understand and evaluate the financial risks of SMEs from a new perspective. Therefore, this paper, based on the study of financial risks assessment of SMEs under the smart city and smart finance, innovatively constructs a new index evaluation system for supply chain finance, based on improved hesitant fuzzy linguistic PROMETHEE method, and the effectiveness and advantages of the model have been verified through an example. To a certain degree, the SMEs financing the evaluation model and improved PROMETHEE method can not only help financial institutions reduce the risks in the specific financial transactions but also reduce the survival threat of financial institutions. Moreover, it is of positive significance to the stable operation of the financial system.

Jump Risk in the US Financial Sector

In this paper we establish empirical evidence for the relationship between the systematic jump betas of financial institutions and two types of systemic risk index: a capital shortfall index and a interconnectedness index. Using high‐frequency data for US financial sector stocks, we show that equity market jumps are positively related to capital shortfall and negatively related to interconnectedness. Higher potential capital shortfall measures of systemic risk lead to a greater sensitivity to systematic jumps, while increased interconnectedness leads to greater resistance. Our findings, along with indicators such as size and leverage, provide a means to identify the possible trade‐offs that regulators might face when assessing the systemic risks of financial institutions, particularly in the context of the cross‐multiple influences within the sector.

Does Downside Risk of Financial Institutions Predict Future Economic Downturns and Housing Market Crashes?

Does Downside Risk of Financial Institutions Predict Future Economic Downturns and Housing Market Crashes?

Business connectedness or market risk? Evidence from financial institutions in China

We utilize the spectral representation of generalized forecast error variance decomposition to investigate the frequency dynamics of volatility connectedness and systemic risk of financial institutions in China from 2011 to 2018. We find that, first, high-frequency components account for the largest part of volatility connectedness (48.33%), followed by low-frequency components, and finally the medium-frequency components. Second, the low-frequency components reflect the business connectedness among financial institutions, while the high-frequency components capture the market risk. Third, the business connectedness among financial institutions will lead to a rise in overall connectedness as well as the accumulation of potential risks. Further, once a crisis breaks out, the potential risks have realized and the business connectedness among institutions declines; while market risk increases rapidly, which helps systemic financial risk stay at a high level. Lastly, among the financial sectors, the banking sector possesses a relatively higher level of business connectedness which plays an important role in the accumulation of potential financial risks; the securities sector features with higher market risk; while the insurance sector has both comparatively lower business connectedness and market risk.

Examining the Relationship Between Banking Competition and Solvency, Liquidity and Credit Risks in Pakistan

This study empirically examines the relationship between the banking competition and the risks faced by the financial sector (i.e. solvency, liquidity, and credit risks) considering 31 banks for the period 2001 to 2018. Banks are further sub-divided into three categories i.e. state-owned banks, foreign banks, and private/commercial banks. The results reveal that Pakistan’s banking industry is relatively elastic and an increase in competition is directly associated with solvency risk, liquidity risk and credit risk of financial institutions and these findings corroborate the competition fragility theory. Besides, state-owned banks have a lesser probability to cope with solvency risk, however, foreign banks appear to face the least liquidity risk whereas private banks appear to face the least credit risk among the entire cluster.

Addressing cyber risk in financial institutions and in the financial system

Regulators and financial institutions must let the financial industry focus on significant systems investments to prevent current and future cyber-infrastructure threats. This paper organises the latest research and views on cyber-risk management to enable insights into the frequency and severity of cyber-risk events, the magnitude of cyber-risk losses, and the best practices methods and frameworks for organising and implementing a cyber-risk management programme. This paper also describes cryptographic best practices and the evolving research on protection against cyberattacks in the coming quantum computing era. The paper concludes that governments must work with their financial market constituents to use one or more of the cyberrisk management frameworks to standardise collecting and reporting on cyber-risk events. Finally, government and industry members must work together to finance and rebuild financial industry infrastructure to enable cyber-risk management solutions.

Corporate governance, policy stability and systemic risk of financial institutions: a comparative analysis of the common law and civil law system

This paper examines the relationship between the quality of corporate governance, political stability and systemic risk of financial institutions in both, common law and civil law of the two countries groups. Thus, it is the subject of a comparative study in terms of the relationship between the three elements: corporate governance, political stability and systematic risk of financial institutions. Our study focuses on a sample of 304 financial institutions around the world for a ten-year period from 2004 to 2013. First, we have reached the empirical evidence which indicates that financial institutions with a strong corporate governance structure are exposed to high levels of systemic risk. Second, we have confirmed that the corporate governance standards applied in financial institutions belonging to the common law system contribute to the amplification of the level of systemic risk incurred by its institutions.

Review of International Supply Chain Risk within Banking Regulations in Asia, US and EU Including Cost Efficiency Proposals

Major financial institutions operate in different regions of the world facing different regulatory landscapes for Supply Chain risks. In this environment, the optimization issue arises how to best comply with the different regulations and reaching cost efficiency at the same time. In this research, the international regulatory landscape for Supply Chain risks of Financial Institutions is introduced and compared internationally. It is understood as an integral part of Supply Chain Risk Management of Financial Institutions, yet the latter is analysed as the research background. Additionally, expert interviews are conducted in order to link the regulation analysis to the current challenges that Financial Institutions face. Finally, recommendations are developed on how banks can be cost efficient, while remaining regulatory compliant, facing increased international regulation in the area of Supply Chain Risk Management. The outcome of the underlying research shows that banking regulation in the area of Supply Chain risks is an important lever in the banking sector to secure customers and financial markets. However, the regulatory landscape is heterogeneous and not consistent on an international scale. Regulation in Asia is highly diverse across different countries due to different states of economic development. The US applies a rather pragmatical approach towards supply chain risk regulation applying different standards of standard setting institutions. Lastly, the EU is very restrictive and strives to unify regulation across member states. Banks should follow a consistent management approach keeping in mind international locations and the strictest regulatory environment they are operating in, to improve cost efficiency yet being regulatory compliant. Also, collaboration with and amongst regulators and other banks internationally is recommended for improved cost efficiency.