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Interest rate risk, i.e. the risk of changes in the interest rate term structure, is of high relevance in insurers' risk management. Due to large capital investments in interest rate sensitive assets such as bonds, interest rate risk plays a considerable role for deriving the solvency capital requirement (SCR) in the context of Solvency II. This paper seeks to address these issues.

In addition to the Solvency II standard model, the author applies the model of Gatzert and Martin for introducing a partial internal model for the market risk of bond exposures. After introducing calibration methods for short rate models, the author quantifies interest rate and credit risk for corporate and government bonds and demonstrates that the type of process can have a considerable impact despite comparable underlying input data.

The results show that, in general, the SCR for interest rate risk derived from the standard model of Solvency II tends to the SCR achieved by the short rate model from Vasicek, while the application of the Cox, Ingersoll, and Ross model leads to a lower SCR. For low‐rated bonds, the internal models approximate each other and, moreover, show a considerable underestimation of credit risk in the Solvency II model.

The aim of this paper is to assess model risk with focus on bonds in the market risk module of Solvency II regarding the underlying interest rate process and input parameters.

The purpose of this study is to investigate the effectiveness of qualitative information extracted from firm’s annual report in predicting corporate credit rating. Qualitative information represented by published reports or management interview has been known as an important source in addition to quantitative information represented by financial values in assigning corporate credit rating in practice. Nevertheless, prior studies have room for further research in that they rarely employed qualitative information in developing prediction model of corporate credit rating.

This study adopted three document vectorization methods, Bag-Of-Words (BOW), Word to Vector (Word2Vec) and Document to Vector (Doc2Vec), to transform an unstructured textual data into a numeric vector, so that Machine Learning (ML) algorithms accept it as an input. For the experiments, we used the corpus of Management’s Discussion and Analysis (MD&A) section in 10-K financial reports as well as financial variables and corporate credit rating data.

Experimental results from a series of multi-class classification experiments show the predictive models trained by both financial variables and vectors extracted from MD&A data outperform the benchmark models trained only by traditional financial variables.

This study proposed a new approach for corporate credit rating prediction by using qualitative information extracted from MD&A documents as an input to ML-based prediction models. Also, this research adopted and compared three textual vectorization methods in the domain of corporate credit rating prediction and showed that BOW mostly outperformed Word2Vec and Doc2Vec.

This study aims to shed more light on the relationship between probability of default, investment horizons and rating classes to make decision-making processes more efficient.

Based on credit default swaps (CDS) spreads, a methodology is implemented to determine the implied default probability and the implied rating, and then to estimate the term structure of the market-implied default probability and the transition matrix of implied rating. The term structure estimation in discrete time is conducted with the Nelson and Siegel model and in continuous time with the Vasicek model. The assessment of the transition matrix is performed using the homogeneous Markov model.

The results show that the CDS-based implied ratings are lower than those based on Thomson Reuters approach, which can partially be explained by the fact that the real-world probabilities are smaller than those founded on a risk-neutral framework. Moreover, investment and sub-investment grade companies exhibit different risk profiles with respect of the investment horizons.

The originality of this study consists in determining the implied rating based on CDS spreads and to detect the difference between implied market rating and the Thomson Reuters StarMine rating. The results can be used to analyze credit risk assessments and examine issues related to the Thomson Reuters StarMine credit risk model.

This paper has used account-level data of corporate and retail borrowers, assessed their credit risk through the risk-neutral principle and examined its implication on loan pricing.

It derives the capital charge and credit risk-premium for expected and unexpected losses through a risk-neutral approach. It estimates the risk-adjusted return on capital as the pricing principle for loans. Using GMM regression, the article has assessed the determinants of risk-based pricing.

It has been found that risk-premium is not reflected in the current loan pricing policy as per Basel II norms. However, the GMM estimation on RAROC can price risk premium and probability of default, LGD, risk weight, bank beta and capital adequacy, which are the prime determinants of loan pricing. The average RAROC for retail loans is more than that of corporate loans despite the same level of risk capital requirement for both categories of loans. The robustness tests indicate that the RAROC method of loan pricing and its determinants are consistent against the time and type of borrowers.

The RAROC method of pricing effectively assesses the inherent risk associated with loans. Though the empirical findings are confined to the sample bank, the model can be used for any bank implementing the Basel principle of risk and capital assessments.

The article has developed and validated the model for estimating RAROC, as per Basel II guidelines, for loan pricing that any bank can use.

It has developed the risk-based loan pricing model for retail and corporate borrowers. It has significant practical utility for banks to manage their risk, reduce their losses and productively utilise the public deposits for societal developments.

The article empirically validated the risk-neutral pricing principle using a unique 1,520 retail and corporate borrowers dataset.

The purpose of this paper is to investigate the effect of board attributes on Sukuk rating in firms listed in Bursa Malaysia (Malaysian Stock Exchange) during the period of 2008 to 2013.

This study uses ordinal logit regression model to examine the influence of board attributes (CEO-chairman duality, board size and board independence) on the dependent variable (RATING).

The findings of this paper generally support the agency theory and stakeholder theory. Results show that after controlling for firm characteristics, the Sukuk rating is positively associated with CEO-chairman duality, board size and board independence; and negatively correlated with leverage while positively related to profitability and size. The findings of this study also provide evidence that having two positions in an organization as CEO and chairman could have added higher responsibility towards making corporate decisions and provide better Sukuk rating performance. In addition, findings show that the larger the board size, the better Sukuk rating. Also, higher board independence enjoys higher rating.

This study was limited to the investigation of the relationship between board attributes (CEO duality, board size and board independence) on Sukuk ratings using aggregate data from 2008 to 2013 among Malaysian Sukuk issuers.

The findings of this paper describe the impact of board attributes on Sukuk rating in Malaysian Sukuk market which in turn gives the useful insights to many of the actors in the markets such as issuers, investors and policymakers which can be relied upon in making strategic decisions to issue and invest in Islamic bonds in Malaysian market. In addition, the findings could prove to be useful also for regulators because they are responsible for the acceptable level of corporate governance standards.

This study contributes to the body of knowledge by focusing heavily on enhancing Sukuk ratings by reducing conflict between managers and Sukuk holders in Malaysia. Additionally, this study benefits from the agency theory and stakeholder theory to provide evidence on the effect of board attributes on Sukuk rating.

The purpose of this study is to conduct an empirical investigation into the impact of supply chain dependence (including customer dependence and supplier dependence) on credit risk through the lens of social network theory (SNT) by focusing on how to manage firm risk using supply chain relationships in the context of Chinese small and medium-sized enterprises (SMEs).

Using data from public databases, this study selects a unique sample from a Chinese SME board and uses an ordered logistic regression model to investigate the relationship between the dependence on major customers or suppliers and both credit risk and credit rating. It is found that the results are robust to the use of different empirical methods.

The main findings of this study are that a firm’s dependence on major customers is positively related to its credit risk but negatively related to its credit rating, while a firm’s dependence on major suppliers is positively related to its credit risk but negatively related to its credit rating.

To broaden the understanding of industrial marketing and purchasing, this study contributes to research on supply chain relationship management and risk management by focusing on SMEs’ dependence on major customers and suppliers and empirically examining the influence of this dependence on both credit risk and credit rating in an emerging market.

The New Basel Capital Accord (Basel II) was published in June 2004. This modification of the regulatory framework for banking institutions raises the question to what extent real estate financing will be impacted and how market participants can be adequately prepared. Aims to examine the impact of Basel II on the future pricing and availability of debt capital and on the cost of capital in real estate financing and to present possible reactions for real estate developers.

This research paper follows a deductive approach. First, the New Basel Capital Accord and the main features of commercial real estate financing are presented. On a normative level, the implications for developers are explained. Since no information regarding the behaviour of market participants in commercial real estate financing was available, the authors have ascertained the relevant questions within the framework of an empirical analysis. A total of 205 banking institutions were asked to fill out a survey pertaining to commercial real estate financing. The results of this survey are partly presented and interpreted.

The availability and the pricing of debt capital will be risk‐adjusted and will depend on the amount of regulatory equity banks will have to hold in reserve for a credit engagement. The cost of debt capital in real estate financing will rise due to systemic reasons of the New Basel Capital Accord. Banks are/will be very restrictive with regard to credit allowances. The use of the positive leverage effect will become more difficult. Structured financing, particularly the use of private equity, is the best way to fill a potential financing gap.

The paper is a timely investigation of a significant regulatory framework that is of world‐wide significance. The New Basel Capital Accord is introduced in its fundamental structure and the two relevant rating approaches are described and put into context. The paper reduces the complexity of the comprehensive and sophisticated Basel Capital Accord. Based on the facts that have been analysed, recommendations of how real estate developers can react to the changes in financing that lie ahead are given.

This study aims to develop a novel approach for predicting default risk in bancassurance, which plays a crucial role in the relationship between interest rates in banks and premium rates in insurance companies. The proposed method aims to improve default risk predictions and assist with client segmentation in the banking system.

This research introduces the group method of data handling (GMDH) technique and a diversified classifier ensemble based on GMDH (dce-GMDH) for predicting default risk. The data set comprises information from 30,000 credit card clients of a large bank in Taiwan, with the output variable being a dummy variable distinguishing between default risk (0) and non-default risk (1), whereas the input variables comprise 23 distinct features characterizing each customer.

The results of this study show promising outcomes, highlighting the usefulness of the proposed technique for bancassurance and client segmentation. Remarkably, the dce-GMDH model consistently outperforms the conventional GMDH model, demonstrating its superiority in predicting default risk based on various error criteria.

This study presents a unique approach to predicting default risk in bancassurance by using the GMDH and dce-GMDH neural network models. The proposed method offers a valuable contribution to the field by showcasing improved accuracy and enhanced applicability within the banking sector, offering valuable insights and potential avenues for further exploration.

Bank creditworthiness refers to the evaluation of a bank’s ability to meet its financial obligations. It is an assessment of the bank’s financial health, stability and capacity to manage risks. This paper aims to investigate the credit rating patterns that are crucial for assessing creditworthiness of the Islamic banks, thereby evaluating the stability of their industry.

Three distinct machine learning algorithms are exploited and evaluated for the desired objective. This research initially uses the decision tree machine learning algorithm as a base learner conducting an in-depth comparison with the ensemble decision tree and Random Forest. Subsequently, the Apriori algorithm is deployed to uncover the most significant attributes impacting a bank’s credit rating. To appraise the previously elucidated models, a ten-fold cross-validation method is applied. This method involves segmenting the data sets into ten folds, with nine used for training and one for testing alternatively ten times changeable. This approach aims to mitigate any potential biases that could arise during the learning and training phases. Following this process, the accuracy is assessed and depicted in a confusion matrix as outlined in the methodology section.

The findings of this investigation reveal that the Random Forest machine learning algorithm superperforms others, achieving an impressive 90.5% accuracy in predicting credit ratings. Notably, our research sheds light on the significance of the loan-to-deposit ratio as a primary attribute affecting credit rating predictions. Moreover, this study uncovers additional pivotal banking features that intensely impact the measurements under study. This paper’s findings provide evidence that the loan-to-deposit ratio looks to be the purest bank attribute that affects credit rating prediction. In addition, deposit-to-assets ratio and profit sharing investment account ratio criteria are found to be effective in credit rating prediction and the ownership structure criterion came to be viewed as one of the essential bank attributes in credit rating prediction.

These findings contribute significant evidence to the understanding of attributes that strongly influence credit rating predictions within the banking sector. This study uniquely contributes by uncovering patterns that have not been previously documented in the literature, broadening our understanding in this field.

This chapter reassesses the economics of interest rate risk management in light of the global financial crisis by developing a derivative-based integrated treatment of interest rate and credit risk interrelation. The decade-long historical data on credit default swap spreads and interest rate swap rates are used as proxy measures for credit risk and interest rate risk, respectively. An elasticity of interest rate risk and credit risk, considered a function of the business cycle phases, maturity of instruments, economic sector, creditworthiness, and other macroeconomic parameters, is investigated for optimizing economic capital. This chapter sheds light on how financial institutions may address hedge strategies against downside risks implementing the proposed derivative-based integrated treatment of interest rate and credit risk assessment allowing for optimization of interest rate swap contracts. The developed framework of integrated interest rate and credit risk management is of special importance for emerging markets heavily dependent on foreign capital as it potentially allows emerging market banks to improve risk management practices in terms of capital adequacy and Basel III rules. Analyzing diversification versus compounding effects, it allows enhancing financial stability through jointly optimizing Pillar 1 and Pillar 2 economic capital.