Using the Tobin Q model to evaluate the impact of credit risks on the bank's market value during the corona pandemic

PurposeThis paper aims to assess the impact of credit risk on the market values of private banks during the corona pandemic. Design/methodology/approachThis study is identifying critical issues of credit risk at six great private banks. A conceptual framework is designed based on the Tobin Q model for investigating study hypotheses. Quantitative financial analysis methods have been used for processing data, such as financial ratios, arithmetic mean and multiple linear regression. FindingsThe most important result of this study is the lack of influence of credit risk on the market value of selected banks. Because the dimensions of credit risk have critical importance in increasing or decreasing the market value, these banks must continue to adopt quantitative financial analysis to measure credit risks to avoid their risk. Originality/valueThis study elaborates the need for financial indicators to help assess the market value of banks during the economic crises caused by the closure of commercial institutions during the corona pandemic. There is continued increase in bank credit to support these institutions, borrowers and cash withdrawals, which may affect their market reputation.

Bsds-balance sheet dynamics simulator (application abm)

Disrupting events like COVID-19, climate change or new competitors (e.g., GAFAM) can permanently change the structure of a bank's balance sheet and the bank's risk profile. Agent-based modeling (ABM) is a versatile, interdisciplinary bottom-up approach that can be used to consider such effects in dynamic simulations of the balance sheet development. The authors present a concept for an agent-based model that simulates the effects of macroeconomic scenarios and competitive boundaries on the balance sheet dynamics of banks. An implementation of such a model could be used to explore stylized balance sheet developments over time and thereby provide a valuable planning tool for qualitative and quantitative risk management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Analysis Of the Effectiveness of Risk- Based Internal Audit Implementation: A Case Study: Acces la Success

Background: Risk Based internal audit is a control and supervision activity carried out by internal auditors using the output of risk management. Purpose: The purpose of this study was to obtain a comprehensive understanding of the implementation of risk based internal audit at BPJS Ketenagakerjaan. Internal auditors need to know about risk management and risk maturity to identify key areas that require immediate supervision and follow-up. Method: This research is a case study qualitative research with a descriptive approach. Data was collected by means of interviews, observation and documentation. The analysis technique using triangulation is to collect data, reduce and draw conclusions. Results and findings: Researchers found that Social Security Agency while in Indonesian term is Badan Penyelenggara Jaminan Sosial (BPJS) Ketenagakerjaan as an institution that is mandated to manage labor social security membership fees and implement good governance and manage business risks. The risk management output is then used as the basis for conducting an internal audit. Discussion: BPJS Ketenagakerjaan carries out risk management to identify risks, identify areas that have potential risks and carry out risk profiling. This makes it easier for internal auditors to carry out the internal audit process. Impact: The results of risk management make it easier for auditors to identify risks and identify specific areas so that internal audits can run effectively and efficiently.

Is credit default swap spread a leading indicator of bank default risk? Evidence of Indian banks during the COVID-19 pandemic

The paper investigates whether credit default swaps (CDSs) spread of Indian banks is a leading indicator for bank default risk. The paper uses Merton-type models to estimate theoretical CDS spread of the sample of Indian banks and then compares it to their balance sheet ratios. Though theoretically, higher CDS spreads indicate higher default risk, the paper finds that in times of shocks, like the present COVID-19 crisis, it becomes difficult to isolate the spread movements due to true default risk versus those based on panic and speculation. The paper then correlates equity premiums and CDS premiums and finds negative correlation. The equity market returns lead the CDS market returns in capturing default risk. As default risk is priced better in equity markets, it is preferable for institutional investors to trade default risk of Indian banks in the stock markets rather than the CDS markets.

China: The Bankable State

The volume on China: The Bankable State rejects neoliberal consensus and focuses on crucial contributions of the Chinese state in shaping Chinese economy. This book makes crucial theoretical contributions to the study of local political economy of China. This book engages with Chinese state responses to challenges China faces in the processes of reform, transition and development of both commercial and non-commercial banks. This book explores Chinese economic growth and development policy processes and its uniqueness in the wider world economy. The book examines Chinese financial policy praxis and offers an insightful account of its successes for the wider resurgence of alternative political economy of local development. Additionally, this book also showcases state led entrepreneurship in China. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

Do the macro and global economic factors drive the nonperforming loans in GCC economies?

PurposeThe current study aims to investigate the determinants of nonperforming loans (NPLs) in the GCC economies during the period spanning 2000 to 2018. It also examines whether the worldwide financial crisis of 2007–2008, which brought the issue of non–performing loans to the greater attention of academics and policymakers, had a substantial impact on NPLs in this region.Design/methodology/approachThe sample consists of 53 conventional banks from GCC countries, and the basic data for the study is obtained from various sources such as Bankscope, IMF World Economic Outlook, World Bank and Chicago Board of Options Exchange Market Volatility Index. The estimations were done by dynamic panel data regression modeling using system generalized methods of moments.FindingsThe findings reveal that both, the non-oil real GDP growth rate and inflation have favorable effects on NPLs. On the other hand, domestic credit to the private sector and the volatility index have an adverse effect on NPLs. Furthermore, the period-wise analysis shows that the relevance and significance of the determinants of NPLs vary between the precrisis and postcrisis periods. It is also reflected through the intercept dummy, which is found to be significant, indicating that the financial crisis, as a global economic factor, had a significant impact on NPLs. A number of robustness tests are applied, which indicate that the results are mostly robust and consistent in terms of the significance of the explanatory variables and the direction of their relationship with the dependent variable.Practical implicationsPolicymakers and bank authorities must strive to maintain a healthy economy and implement macroprudential policies to improve the financial stability of banks and reduce credit risk.Originality/valueTo the best of the authors' knowledge, this is likely the first study that empirically investigates the influence of the financial crisis on NPLs in the context of GCC economies. In addition, the research spans 19 years to produce more conclusive results.

Bond Portfolio Optimization with Long-Range Dependent Credits

Consider the optimal allocation between money market account and corporate bond fund. While the money market account is free of credit risk, corporate bonds are defaultable and exhibit long-range dependence (LRD) in credit risk. We propose a Volterra default intensity model to capture the LRD in credit risk. Using utility maximization, we derive the novel optimal investment strategy for a corporate bond fund. As empirical study shows that the COVID-19 pandemic has lowered the level of LRD in credit risk, we conduct sensitivity analysis and empirically investigate the changes in demand for corporate bonds before and during the pandemic period.

THE MICROPRUDENTIAL STRESS TESTING FOR BANKING SYSTEM. A STUDY CASE ON ALGERIAN PRIVATE BANK, USING ACCOUNTING APPROACH

The aim of this article is to highlight the importance and effectiveness of stress testing as part of microprudential policy. We focus on microprudential stress testing to assess financial stability, the resilience and solvency of one important private bank in Algeria in the face of liquidity risk. Our empirical analysis adopts a bottom-up approach based on an accounting method. It studies the relationship between the bank solvency ratio (ratio cook) and bank portfolios, such as loans to the construction, trade, industry, and automotive sectors. Microeconomic stress tests assess the credit risk of a bank's loan portfolio by bottom-up accounting approach, applying eleven pessimistic and plausible multi-variable scenarios with potential risks. The tests introduce several types of microeconomic shocks into the scenarios, which are designed to replicate those that occurred during the global financial crisis. The tests results show that this private bank is highly resistant to liquidity risk, despite significant losses on its investment portfolio. The stress tests prove once again, and especially after the 2008 financial crisis, that they are indispensable tools in the management of banking risks and against systemic risks.

Total Value Adjustment of Multi-Asset Derivatives under Multivariate CGMY Processes

Counterparty credit risk (CCR) is a significant risk factor that financial institutions have to consider in today's context, and the COVID-19 pandemic and military conflicts worldwide have heightened concerns about potential default risk. In this work, we investigate the changes in the value of financial derivatives due to counterparty default risk, i.e., total value adjustment (XVA). We perform the XVA for multi-asset option based on the multivariate Carr–Geman–Madan–Yor (CGMY) processes, which can be applied to a wider range of financial derivatives, such as basket options, rainbow options, and index options. For the numerical methods, we use the Monte Carlo method in combination with the alternating direction implicit method (MC-ADI) and the two-dimensional Fourier cosine expansion method (MC-CC) to find the risk exposure and make value adjustments for multi-asset derivatives.

Impact of Geographical Diversification on Credit Risk of Microfinance Organizations in Armenia

This paper analyzes the existence of relationship between credit risk and the geographical diversification of financial institutions, originating from emerging countries. Due to economic unstable situation in the world caused by Covid-19, credit portfolios of banks and MFIs caused negatively which in some situations can lead to default. In the current situation, it became necessary to discover new approaches to credit risk management and new researches to be done. For this purpose, financial indicators of MFIs operating in Armenia were evaluated and Pearson analysis of MFIs data, risks & profitability efficiency calculation was made to take out impact of diversification of MFIs on credit risk reduction. Both international literature and practical data of MFIs operating in Armenia were identified. Another research was made for taking out the number of branches and credit risk correlation. Our findings show that geographic diversification is statistically significant with the expansion of gross loans. In contrast, empirical results suggest that the geographical diversification of MFIs does not have a significant correlation with the size of the credit risk reserve, which means that the representation of MFIs in different regions in the form of branches will not always lead to credit risk reduction, and in some cases may lead to operational risks and additional costs. We adopt cost funding and assets size variables impact assessment evaluation through instrumental variables method. Our results confirm the endogenous nature of those variables with risk level of MFIs. © 2023 Publishing House of the Higher School of Economics. All rights reserved.

Permanent layoff and consumer credit card loss forecasting

PurposeThe unemployment rate (UR) is the leading macroeconomic indicator used in the credit card loss forecasting. COVID-19 pandemic has caused an unprecedented level of volatility in the labor market variables, leading to new challenges to use UR in the credit risk modeling framework. This paper examines the dynamic relationship between the credit card charge-off rate and the unemployment rate over time.Design/methodology/approachThis study uses quarterly observations of charge-off rates on credit card loans of all commercial banks from Q1 1990 to Q4 2020. Univariate, multivariable, machine learning, and regime-switching time series modeling are employed in this research.FindingsThe authors decompose UR into two components – temporary and permanent UR. The authors find the spike in UR during COVID-19 is mainly attributed to the surge in temporary layoffs. More importantly, the authors find that the credit card charge-off rate is primarily driven by permanent UR while temporary UR has little predictive power. During recessions, permanent UR seems to be a stronger indicator than total UR. This research highlights the importance of using permanent UR for credit risk modeling.Originality/valueThe findings in the research can be applied to the credit card loss forecasting and CECL reserve models. In addition, this research also has implications for banks, macroeconomic data vendors, regulators, and policymakers.

Assessment of Banking Conditions on Financial Distress During the Period of COVID-19 in Indonesia

This study was conducted to analyze the effect of liquidity ratios (LDR), profitability (ROA), and leverage (CAR) on the financial difficulties of banking companies listed on the Indonesia Stock Exchange (IDX) for the 2020-2021 period, moderated by credit risk during the COVID-19 pandemic. 19. Financial Distress was measured using the Altman Z-Score model, and compared with conditions before the 2016-2019 pandemic, so that the research data was taken from the annual report for the 2016-2021 period. The samples collected were 27 companies. This study uses quantitative analysis techniques with linear regression and processed using SPSS 22. The results of this study indicate that during the pandemic period and before COVID-19 in Indonesia, liquidity, profitability and leverage ratios have a significant effect on financial difficulties. Credit risk as a moderating variable can only strengthen the influence of the liquidity ratio and profitability ratio. Meanwhile, the leverage ratio cannot be moderated by credit risk. In the pre-pandemic period of 2016-2019, it showed that the ratio of liquidity, profitability, and leverage could not be moderated by credit risk. The findings in this study explain that banking conditions are not in financial difficulty during the pandemic, but profits for companies are low. This anomaly is caused by over-liquidity from credit that is not widely distributed to the business sector. © 2023, World Scientific and Engineering Academy and Society. All rights reserved.

The accounting treatment of expected credit losses by South African banks during COVID-19

Orientation: The coronavirus disease 2019 (COVID-19) pandemic negatively affected borrowers' ability to repay debt, which is expected to influence banks' calculation of their expected credit loss (ECL) allowance. Comprehensive disclosure regarding the application of managerial judgement in calculating ECLs would produce decision-useful financial information. Research purpose: This study explored the effects of the COVID-19 pandemic on the measurement and disclosure of ECL allowances by South African listed banks. Motivation for the study: It is unknown whether decision-useful financial information regarding South African banks' ECLs was produced during the pandemic, mirroring developed countries. Research approach/design and method: Content analysis of quantitative and qualitative data from annual financial statements was employed for a sample of listed banks. Main findings: Banks employed a variety of relief measures to accommodate borrowers, but these relief measures did not automatically trigger a significant increase in credit risk. More loans were subject to lifetime ECLs, causing the ECL allowance to increase substantially during the first year of the pandemic. Forward-looking information as well as postmodel adjustments were employed to measure the ECL allowance. Practical/managerial implications: The ECL allowances of South African listed banks increased during the pandemic. Disclosure in the annual financial statements and identifying ECLs as a key audit matter provided evidence of adequate consideration of the credit risk and forward-looking information influencing ECLs by banks and their auditors. Improved disclosure regarding postmodel adjustments is required. Contribution/value-add: During the COVID-19 pandemic, decision-useful financial information regarding the calculation of banks' ECL allowances was available for South African banks, mirroring developed countries.

Reexamine the Role of the Financial Institution Management During Card Debt Crisis or the COVID-19

Due to the increasing popularity of financial technology and the lifting of financial regulations, various financial institutions have become increasingly competitive and actively expand their consumer finance business. Changes in generational consumption behavior have led to excessive credit expansion, excessive debt or bad credit records. All of these result in the emergence of adverse selection and moral hazard problems of information asymmetry, and finally cause the card debt crisis in 2005. This article focuses on variables such as the number of cards in circulation, retail sales volume, revolving balance, and overdue ratios of credit cards in public and private banks, and examine whether the information asymmetry in the credit card market has been improved ,with the financial institution management. Furthermore, due to the COVID-19 exploring whether the information asymmetry has been worsened or improved deserves the attention of the financial authority again. The results reveal that continuous financial institution management is very important and effective during the card debt period or the pandemic.

NEXUS OF RISK AND STABILITY IN ISLAMIC BANKS DURING THE PANDEMIC: EVIDENCE FROM INDONESIA

This paper analyzes the effect of liquidity risk and credit risk on Islamic bank stability and whether the risk-stability nexus changes during the Covid-19 pandemic. Using a panel quarterly dataset of 14 Islamic banks from 2017 to 2020, a total of 224 quarterly-bank observations in total and the system generalized method of moment, we find that credit risk and liquidity risk are negatively associated with bank stability. Moreover, the COVID-19 does not alter the negative relationship between liquidity risk and stability. To validate the results, we also estimate the model using the LSDVC. The LSDVC results remain consistent. These results provide new insight into understanding risk management implementation for minimizing these risks. © Bank Indonesia Institute. All rights reserved.

The effects of IFRS 9 valuation model on cost of risk in commercial banks – the impact of COVID-19

The aim of this paper is to analyse the variables determining the cost of risk in banks after the implementation of IFRS 9 with a particular focus on the COVID-19 pandemic in terms of the quality of credit portfolio. To achieve this we propose a panel research model with quarterly variables determining the cost of risk in commercial banks. The research data was taken from the domestic and European banking sector in 2018–2020 during the initial phase of the COVID-19 pandemic. We show that contrary to regulatory assumptions, procyclical tendencies with a cliff effect have not been eliminated in commercial banks under the IFRS 9 framework. In addition, we observe significant differences in the recognition of loan impairment in the domestic banks versus the EU ones under IFRS 9. However, we demonstrate that IFRS 9 did allow banks to recognise loan impairment reasonably fast in the most acute phase of the COVID-19 pandemic. © 2022 Narodowy Bank Polski. All rights reserved.

The role of web browsing in credit risk prediction

Online mail order and online retail purchases have increased rapidly in recent years worldwide, with Covid-19 forcing almost all non-grocery shopping to move online. These practices have facilitated the availability of new data sources, such as web behavioural variables providing scope for innovation in credit risk analysis and decision practices. This paper examines new web browsing variables and incorporates them into survival analysis as predictors of probability of default (PD). Using a large sample of purchase and repayment credit accounts from a major digital retailer and financial services provider, we show that these new variables enhance the predictive accuracy of probability of default (PD) models at account level. This also holds in the absence of credit bureau data, therefore, the new information can help people who may not have a credit history (thin file) who cannot be assessed using traditional variables. Moreover, we leverage on the dynamic nature of these new web variables and explore their predictive value in short and long- term horizons. By adding macroeconomic variables, the possibility for stress-testing is provided. Our empirical findings provide insights into web browsing behaviour, highlight how the inclusion of non-standard variables can improve credit risk scoring models and lending decisions and may provide a solution to the thin files problem. Our results also suggest a direct value added to the online retail credit industry as firms should leverage the increasing trend of consumers embracing the digital environment. © 2022 The Authors

Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach.

The economic onslaught of the COVID-19 pandemic has compromised the risk management of financial institutions. The consequences related to such an unprecedented situation are difficult to foresee with certainty using traditional methods. The regulatory credit loss attached to defaulted mortgages, so-called expected loss best estimate (ELBE), is forecasted using a machine learning technique. The projection of two ELBEs for 2022 and their comparison are presented. One accounts for the outbreak's impact, and the other presumes the nonexistence of the pandemic. Then, it is concluded that the referred crisis surely adversely affects said high-risk portfolios. The proposed method has excellent performance and may serve to estimate future expected and unexpected losses amidst any event of extraordinary magnitude.

Covid-19, Credit Risk Management Modeling, and Government Support.

We investigate rating and default risk dynamics over the covid-19 crisis from a credit risk modeling perspective. We find that growth dynamics remain a stable and sufficient predictor of credit risk incidence over the pandemic period, despite its large, short-lived swings due to government intervention and lockdown. Unobserved component models as used in the recent credit risk literature appear mainly helpful for explaining the high-default wave in the early 2000s, but less so for default prediction above and beyond growth dynamics during the 2008 financial crisis or the early 2020 covid default peak. Government support variables do not reduce the impact of either growth proxies or unobserved components. Correlations between government support and credit risk are different, however, during the financial and the covid crisis. Using the empirical models in this paper as credit risk management tools, we show that growth factors also suffice to predict credit risk quantiles out-of-sample during covid times.

Global assessment of the COVID-19 impact on IFRS 9 loan loss provisions

PurposeThis article aims to analyze the impact of COVID-19 measures by governments and central banks on International Financial Reporting Standards (IFRS) 9 loan loss provisions (LLPs). Changes in the total amount of LLPs, distribution of outstanding loan balance among IFRS 9 stages and credit risk parameters used for calculation are investigated for each world region where banks report under IFRS.Design/methodology/approachData for a global selection of 105 banks reporting under IFRS were collected from 2019 to 2020 annual reports, financial statements, and Pillar III reports. These data provide the basis to empirically analyze the impact of COVID-19 on LLPs.FindingsIn most world regions Stage 2 balances increase while Stage 3 balances remain comparatively stable. The credit risk parameters used for computing LLPs remained stable in 2020. However, in China, the impact of COVID-19 on banks was not detected. Mean Stage 1 balances for Chinese banks increased slightly during the pandemic. Aside from the COVID-19 impact, we find that LLPs, credit risk parameters, and loss absorption capacities are significantly lower for banks in Canada, Oceania and Western Europe compared to those in the rest of the world.Originality/valueThere exists previous research examining the COVID-19 impact on financial stability, implementation of emergency rules and country-wide analyses to anticipate default rates depending on recovery scenarios. However, this is the first global study on the immediate impact of COVID-19 on LLPs. It reveals the significant differences between world regions and provides implications about their resilience against future credit shocks.