SME Credit Access After Basel III. Does Size (and Quality) Matter?

PurposeThis analysis asks whether regulatory capital requirements capture differences in systematic risk for large firms and micro-, small- and medium-sized enterprises (MSMEs). The authors explore whether bank capital regulations intended to support SMEs’ access to borrowing are effective. The purpose of this paper is to find out whether the regulatory design (particularly the estimate of asset correlations) positively affects the lending process to small and medium enterprises, compared to large corporates.Design/methodology/approachThe authors investigate the appropriateness of bank capital requirements considering default risk of loans to MSMEs and distortions in capital charges between MSMEs and large firms under the Basel III framework. The authors compiled firm-level data to capture the proportions of MSMEs and large firms in Italy during 2000–2014. The data set is drawn from financial reports of 708,041 firms over 15 years. Unlike most empirical studies that correlate assets and defaults, this study assesses a firm’s creditworthiness not by agency ratings or by sampling banks but by a specific model to estimate one-year probabilities of default.FindingsThe authors found that asset correlations increase with firms’ size and that large firms face considerably greater systematic risk than MSMEs. However, the empirical values are much lower than regulatory values. Moreover, when the authors focused on the MSME segment, systematic risk is rather stable and varies significantly with turnover. This analysis showed that the regulatory supporting factor represents a valuable attempt to treat MSME loans more fairly with respect to banks’ capital requirements. Basel III-internal ratings-based approach results show that when the supporting factor is applied, the Risk-Weighted-Assets (RWA) differences between MSMEs and large firms increase.Research limitations/implicationsThe implications of this research is that banking regulators to make MSMEs support more effective should review asset correlation estimation criteria, refining the fitting with empirical evidence.Practical implicationsThe asset correlation parameter stipulated by the Basel framework is invariant with economic cycles, decreases with borrowers’ probability of default and increases with borrowers’ assets. The authors found that those relations do not hold. This way, asset correlations fall below parameters defined by regulatory formula, and SMEs’ credit risk could be overstated, resulting in a capital crunch.Originality/valueThe original contribution of this paper is to demonstrate that the gap between empirical and regulatory capital charge remains high. When the authors examined the Basel III-IRBA, results showed that when the supporting factor is applied, the RWA differences between MSMEs and large firms increase. This is particularly strong for loans to small- and medium-sized companies. Correctly calibrating asset correlations associated with the supporting factor eliminates regulatory distortions, reducing the gap in capital charges between loans to large corporate and MSMEs.

The three drivers in modeling CDOs (collateralized debt obligations) are the probability of default, the recovery rate, and correlation of the underlying pool of credits. In general, the probability of default is by far the most relevant factor and correlation, the least. Unfortunately, an unwarranted amount of attention is currently given to correlation. Worse yet, most models are driven by (or based on) asset correlation assumptions when what is really relevant is the default correlation. There is no assurance that controlling the asset correlation can produce the desired default correlation.

The use of stress testing for risk monitoring has increased considerably over the last decade. Stress testing – a simulation technique used to assess the strength of a portfolio or a financial institution under unusual economic conditions – emerged as a powerful tool that was originally used in market risk. Its use has subsequently been extended into credit risk. To stress test a credit risk portfolio, practitioners focus on the key parameters that allow the risk of a credit portfolio to be assessed. These parameters, also known as Basel II parameters, are probability of default, loss given default, exposure at default and asset correlation. In this paper, using a time series approach (specifically, an error correction model), we focus on the probability of default parameter that is related to macroeconomic factors. Such an approach involves dealing with the nonstationarity of economic time series and cointegration issues. Hence, when the model is estimated, the probability of default can be simulated by measuring the effects of macroeconomic shocks applied to the model. In turn, these probabilities of default can be used to measure the impact on the probability of default under a given macroeconomic scenario, and then to improve the credit risk monitoring. The results of our study suggest that error correction models are well-suited to macroeconomic stress testing. Indeed, the fitting of the historical probability of default and the results under the stress scenarios considered here are satisfactory.

This paper, inspired by the efforts of a Canadian bank, discusses Basel preparation and validation issues. A comprehensive outcomes analysis (eg, back-testing) framework is presented, including a simulation-based calibration test. A consistent risk rating philosophy - point-in-time (PIT) or through-the-cycle (TTC) - encompasses both the probability of default (PD) and the default correlations, and the validation needs to be consistent with both. Related arguments are made that not only PD itself, but the correlation of PD used in economic capital models should be rating system specific. We need to use a larger PD correlation under a TTC rating system than under a PIT rating system. Furthermore, Basel loss given default may not be appropriate for commonly used internal models, and accordingly adjustments are proposed.

The main challenge of forecasting credit default risk in loan portfolios may be seen in forecasting the default probabilities and the default correlations. We derive a Merton-style threshold value model for the default probability which treats the asset value of a firm as unknown and uses a factor model instead. In addition, we demonstrate how default correlations can be easily modeled. The empirical analysis is based on a large data set of German firms provided by Deutsche Bundesbank. We find that default probabilities can be forecast given the values of risk drivers known at the point of time at which the forecast is made. In addition, correlations depend on the fit of the estimated default probabilities to the realized default rate for given points in time.

Capital Allocation for Portfolio Credit Risk

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Abstract. The objective of this article is theoretical and methodological justifying of determining algorithm of the cost of debt capital for enterprises functioning in emerging markets (EM). The methods of research: analysis and synthesis, system analysis, comparative analysis, empirical and statistical methods, factor analysis. Results. In this article key determinants of interest rates on debt capital for enterprises and their impact on the procedure of discount rate calculation are determined. The issue of the cost of debt calculation of enterprises in condition of absence of complete information concerning systematic and non-systematic crediting risks is studied. Differences between interest rate on the loan fixed in credit agreement and expected by creditors the cost of debt are identified. It is determined that the key factor impacting the deviation level of market value of debt capital from the nominal, and respectively, deviation of the cost of debt from the cost of capital is probability of default (PD). At the minimum values of PD, the contract interest rate corresponds to the rate of cost of debt and it is advisable to use it for discount rate calculation. Critical analysis of alternative methodological approaches of the cost of debt calculation is made. Ways of integrating of market information concerning credit default swaps into the process of expected cost of debt calculation are justified. Factors of shadowing of rates of the cost of debt and ways of reducing of shadow transactions’ level in the credit market are identified. Conclusions. At high PD values, expected by market premium for default risk may exceed the contract interest rate, which necessitates constant monitoring of credit risks and appropriate adaptation of interest rates. In the paper the algorithm of such adaptation are proposed. It is shown that in the case of non-use of interest rates adjustment taking into account changes in PD, CDS and LGD, premium for creditors’ systematic risk can differ significantly from market values of similar enterprises (peer-group), and estimated value of the cost of debt can acquire negative values. Contract (promised) interest rate should be set in such way that the premium for systematic risk of providing debt capital will be at the level of similar companies and does not change significantly as a result of probability of default changes. If in practice the opposite situation occurs, it is the evidence of contract interest rate shadowing, absence of effective system of assessment and management of credit risks. For solving the problem of interest rate transparency and filling of information gaps concerning PD borrowers in EM countries, should intensify CDS market. Keywords: debt capital, default probability, non-performing loans, credit default swap, credit spread, debt capital premium, shadow economy. JEL Classification E47 Formulas: 16; fig.: 0; tabl.: 3; bibl.: 15.

BackgroundBecause "laws of nature" do not exist in ecology, much of the foundations of community ecology rely on broad statistical generalisations. One of the strongest generalisations is the positive relationship between density and distribution within a given taxonomic assemblage; that is, locally abundant species are more widespread than locally sparse species. Several mechanisms have been proposed to create this positive relationship, and the testing of these mechanisms is attracting increasing attention.ResultsWe report a strong, but counterintuitive, negative relationship between density and distribution in the butterfly fauna of Finland. With an exceptionally comprehensive data set (data includes all 95 resident species in Finland and over 1.5 million individuals), we have been able to submit several of the mechanisms to powerful direct empirical testing. Without exception, we failed to find evidence for the proposed mechanisms creating a positive density-distribution relationship. On the contrary, we found that many of the mechanisms are equally able to generate a negative relationship.ConclusionWe suggest that one important determinant of density-distribution relationships is the geographical location of the study: on the edge of a distribution range, suitable habitat patches are likely to be more isolated than in the core of the range. In such a situation, only the largest and best quality patches are likely to be occupied, and these by definition can support a relatively dense population leading to a negative density-distribution relationship. Finally, we conclude that generalizations about the positive density-distribution relationship should be made more cautiously.

In credit risk, debtors with different creditworthiness are divided into rating classes. One problem is to define the borders of the rating classes. A natural way to estimate these breakpoints from default observations comes out of the field of change point analysis. In order to account for dependency between the debtors, the literature proposes a combination of a breakpoint model with a one-factor model. One finds strongly consistent estimators for the threshold of the rating classes and the corresponding default probabilities, also called risk levels. But an investigation of the inherent model properties is as yet missing. For this reason we derive the default correlation and study its relationship to the model parameters, i.e., the breakpoint, the risk levels, and a new correlation term, named score correlation, appearing in a simulation study. Eventually, we check the magnitude of the score correlation used in the simulation study.

Chapter 11 - Estimating Parameters Required for Credit Risk Modeling

This paper examines consistency in the estimates of probability of default (PD) and loss given default (LGD) that nine large U.S. banks assign to syndicated loans for regulatory capital purposes. Using internal bank data on loans that had PDs and LGDs assigned by more than one bank, we find substantial dispersion in these parameters. Banks differ substantially in PDs, but only a few set PDs systematically higher or lower than the median bank. However, many banks differ from the median bank systematically in LGDs, and these differences affect their Basel II minimum regulatory capital significantly. The differences in LGDs imply that, for an identical loan portfolio, the bank that sets the highest LGDs would have Basel II minimum regulatory capital twice as large as the bank that sets the lowest LGDs. We argue that these differences in risk parameters across banks can be at least partially explained by bank behavior that complies with the Basel rules. We also find a negative relation between banks' LGDs and their shares in loan syndicates, suggesting that differences in risk parameters have implications beyond bank capital.

Much of the research on networks using patent data focuses on citations and the collaboration networks of inventors, hence regarding patents as a positive sign of invention. However, patenting is, most importantly, a strategic action used by companies to compete with each other. This study sheds light on inter-organisational adversarial relationships in patenting for the first time. We constructed and analysed the network of companies connected via patent opposition relationships that occurred between 1980 and 2018. A majority of the companies are directly or indirectly connected to each other and hence form the largest connected component. We found that, in the network, many companies disapprove patents in various industrial sectors as well as those owned by foreign companies. The network exhibits heavy-tailed, power-law-like degree distribution, and assortative mixing. We further investigated the dynamics of the formation of this network by conducting a temporal network motif analysis, with patent co-ownership among the companies being considered. By regarding opposition as a negative relationship and patent co-ownership as a positive relationship, we analysed where collaboration may occur in the opposition network and how such positive relationships would interact with negative relationships. The results identified the structurally imbalanced triadic motifs and the temporal patterns of the occurrence of triads formed by a mixture of positive and negative relationships. Our findings suggest that the mechanisms of the emergence of the inter-organisational adversarial relationships may differ from those of other types of negative relationships, hence necessitating further research.

Aims: The purpose of this study is to analyze the impact of Gross Domestic Product (GDP), Industry Value Added (IVA), Urban Population (UP), Trade, and Foreign Direct Investment (FDI) on Energy Intensity in G20 countries.
 Study Design: This research used a quantitative descriptive method using panel data analysis.
 Place and Duration of Study: The scope of this research extends to G20 member countries such as Argentina, Brazil, Canada, China, Germany, European Union, France, United Kingdom, Indonesia, India, Italy, Japan, Korea, Mexico, Rusia, Saudi Arabia, Turki, United States, and South Africa, between 1990-2021.
 Methodology: This research uses descriptive method combined with panel data analysis, analyze determine of GDP, IVA, UP, Trade, and FDI on Energy Intensity in G20 countries. Furthermore, the data uses is secondary data that has a regression model on panel data from 1990-2021.
 Results: The result of this research show that IVA has a positive relationship and has a significant effect on increasing energy intensity in G20 countries. GDP, Trade and UP variables have a negative relationship and have a significant effect on Energy Intensity in G20 countries. Meanwhile, the FDI variable has no significant effect on Energy Intensity in G20 countries.
 Conclusion: Based on research result, Energy Intensity in G20 countries is influenced by various factors, The IVA factor has a positive and significant relationship with energy intensity, can be utilized to increase productivity and economic growth, but need to be balanced with effort to increase energy efficiency.
 While the GDP, Trade and Urban Population factors have a negative and significant relationship to energy intensity. However, FDI does not have a significant effect on energy intensity in G20 countries. The government should consider policies to reduce dependence on intensive energy, especially in sector that have a negative relation with energy intensity such as GDP, trade and urban population.

The purpose of this study was to empirically investigate how E-Marketing Orientation relates with Social Media Implementation in the Banking Industry in Nigeria. Three hundred and fifty five (355) copies of the questionnaire were administered to respondents. Upon retrieval and data cleaning, 338 copies were subjected to Data analysis. Data analyses were aided by the use of SPSS version 20.0 and hypotheses were tested using the Pearson Correlation Coefficient. After the data analysis, it was revealed that: Philosophical component has a weak and positive relationship with Social Media Implementation (Business Use and Private Use). The study also revealed a moderate and positive relationship between initiation components and Business Use as a measure of Social Media Implementation but a weak and positive relationship exists between Initiation components and Private Use. Adaptation component was found to have a weak and negative relationship with Social Media Implementation measures (Business Use and Private Use). The study also revealed a strong and positive relationship between Implementation components and Business Use and a moderate and positive relationship between Implementation components as a dimension of E-Marketing Orientation and Private Use as a measure of social media implementation. We therefore concluded that there is a weak and positive relationship between Philosophical components as a dimension of E-Marketing Orientation and Social Media Implementation measures (Business Use and Private Use). Moderate and positive relationship exists between Initiation components and Business Use but a weak and positive relationship exists between Initiation component and Private Use as a measure of Social Media Implementation. Adaptation component has a weak and negative relationship with Social Media Implementation (Business Use and Private Use). Also, strong and positive relationship exists between Implementation component and Business Use as a measure of social media implementation but a moderate and positive relationship exists between Implementation components as a dimension of E-Marketing Orientation and Private Use as a measure of social media implementation. Based on the findings and conclusions we recommended that since Philosophical component of EMO has a weak and positive relationship with Social Media Implementation (Business Use and Private Use), Banking firms in Nigeria should make it a strategic necessity to be involved in e-marketing, develop e-marketing initiatives and inform employees that success depends on the adoption of advanced e-marketing resources. Since there is a moderate and positive relationship between Initiation components and Business Use and a weak and positive relationship between Initiation components and Private Use, Banking firms in Nigeria should monitor competitors’ adoption of e-marketing, carry out in-house research about e-marketing and meetings should be held regularly to prepare e-marketing plans and follow the developments in e-marketing. Also, since Adaptation component has a weak and negative relationship with Social Media Implementation, Banking firms in Nigeria should use e-marketing resources such as web site and e-mail to communicate with their customers and also conduct commercial transactions, have a computerized customer database that they can use to perform marketing activities and use e-marketing in the normal course of carrying out day-to-day business. Finally, since a strong and positive relationship exists between Implementation components and Business Use as a measure of Social Media Implementation and a moderate and positive relationship exists between Implementation components and Private Use, Banking firms in Nigeria should see that the activities of the different departments which are responsible for e-marketing implementation are well coordinated, ensuring that there is adequate technical support and also ensuring that the latest e-marketing technologies are installed in the bank.