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The purpose of this paper is to investigate the relationship between liquidity and credit risk, and employ the findings to estimate the Incremental Risk Charge (IRC), the new credit risk capital add‐on introduced by the Basel Committee for banks' trading books. The IRC estimates are compared with stressed market risk measures, derived from a sample of corporate bond indices encompassing the recent financial crisis. This can determine the extent to which trading book capital would change in stress conditions, under newly proposed rules.

The Basel II and the proposed Basel III capital requirements for banks' trading books, with a sample of bond portfolios, are implemented.

The findings show that, although the (incremental) credit risk in the trading book may be considerable, the capital needed to absorb market risk‐related losses in stressed scenarios can be more than ten times larger.

The data, methodology and purpose are all original.

The primary objective of the paper is to demonstrate the importance of borrower‐specific characteristics as well as local situation factors in determining the demand prospect as well as the risk of credit loss on residential housing loan repayment behavior in India.

Using 13,487 housing loan accounts (sanctioned from 1993‐2007) data from Banks and Housing Finance Cos (HFCs) in India, this paper attempts to find out the crucial factors that drive demand for housing and its correlation with borrower characteristics using a panel regression method. Next, using logistic regression, housing loan defaults and the major causative factors of the same are examined.

In analyzing the housing demand pattern, some special characteristics of the Indian residential housing market (demographic and social features) and the housing loan facility structure (loan process, loan margin, loan rate, collateral structure etc.), that have contributed to the safety and soundness of the Indian housing market have been deciphered. The empirical results suggest that borrower defaults on housing loan payments is mainly driven by change in the market value of the property vis‐à‐vis the loan amount and EMI to income ratio. A 10 percent decrease in the market value of the property vis‐à‐vis the loan amount raises the odds of default by 1.55 percent. Similarly, a 10 percent increase in EMI to income ratio raises the delinquency chance by 4.50 percent. However, one cannot ignore borrower characteristics like marital status, employment situation, regional locations, city locations, age profile and house preference which otherwise may inhibit the lender to properly assess credit risk in home loan business, as the results show that these parameters also act as default triggers.

This study contributes on the micro side of the housing market in India, since it uses unique and robust loan information data from banks and HFCs. The empirical results obtained in this paper are useful to regulators, policy makers, market players as well as the researchers to understand housing market demand and risk characteristics in an emerging market economy such as India.

The purpose of this paper is to aid understanding of the changes in Basel Committee on Banking Supervision (BCBS) regulatory strategies after the global financial crisis.

The author uses the credit valuation adjustment (CVA) charge reform as a test case for inquiring whether BCBS has departed from its pre-crisis facilitative regulatory strategy path. The regulatory strategy of the CVA charge is discussed.

The charge exhibits a new regulatory strategy that BCBS has adopted. It seeks to manipulate market structures by imposing risk-insensitive capital charge methodologies.

The paper offers a new heuristic to analyse regulatory initiatives and their significance. The CVA charge has not been subject to a regulatory theory-based analysis in prior literature.

I. INTRODUCTION A typical shortcoming of most current credit portfolio models is the lack of a stochastic modeling of risk factors, such as interest rates or credit spreads, during the revaluation process at the risk horizon. For example, fixed income instruments, such as bonds or loans, are revalued at the risk horizon using the current forward rates and (rating class specific) forward credit spreads for discounting future cash flows. Hence, the stochastic nature of the instrument's value in the future which results from changes in factors other than credit quality is ignored, and the riskiness of the credit portfolio at the risk horizon is underestimated. A further consequence is that correlations between changes of the debtor's default probability and changes of market risk factors and, hence, the exposure at default cannot be integrated into the credit portfolio model. This drawback is especially relevant for portfolios of defaultable market‐driven derivatives. One reason why risk factors not directly related to credit risk are neglected in most current credit portfolio models is that there is still no commonly accepted approach for modeling the credit quality of a debtor and the dependencies between the credit quality changes of different debtors. Hence, it might be over‐ambitious to incorporate correlations between market risk factors and credit quality changes. Even empirical evidence on the sign of the correlation remains inconclusive. Additionally, introducing stochastic market risk factors and modeling the correlation between these risk factors and credit quality changes would significantly increase the computational burden for calculating robust risk measures of credit portfolios.

The purpose of this paper is to provide a method that can better evaluate the credit risk (CR) under PPP project finance.

The principle to evaluate the CR of PPP projects is to calculate three critical indicators: the default probability (DP), the recovery rate (RR) and the exposure at default (EAD). The RR is determined by qualitative analysis according to Standard & Poor’s Recovery Scale, and the EAD is estimated by NPV analysis. The estimation of the DP is the focus of CR assessment because the future cash flow is not certain, and there are no trading records and market data that can be used to evaluate the credit condition of PPP projects before financial close. The modified CreditMetrics model and Monte Carlo simulation are applied to evaluate the DP, and the application is illustrated by a PPP project finance case.

First, the proposed method can evaluate the influence of the project’s cash flow uncertainty on the potential loss of the bank. Second, instead of outputting a certain default loss value, the method can derive an interval of the potential loss for the bank. Third, the method can effectively analyze how different repayment schedules and risk preference of banks influence the evaluating result.

The proposed method offers an approach for the bank to value the CR under PPP project finance. The method took into consideration of the uncertainty and other characteristics of PPP project finance, adopted and improved the CreditMetrics model, and provided a possible loss range under different project cash flow volatilities through interval estimation under certain confident level. In addition, the bank’s risk preference is considered in the CR evaluating method proposed in this study where the bank’s risk preference is first investigated in the CR evaluating process of PPP project finance.

Catastrophe (CAT) events associated with natural catastrophes and man-made disasters cause profound impacts on the insurance industry. This research thus reviews the impact of CAT risk on the insurance industry and how traditional reinsurance and securitized risk-transfer instruments are used for managing CAT risk.

This research reviews the impact of CAT risk on the insurance industry and how traditional reinsurance and securitized risk-transfer instruments are used for managing CAT risk. Apart from many negative influences, CAT events can increase the net revenue of the insurance industry around CAT events and improve insurance demand over the post-CAT periods. The underwriting cycle of reinsurance causes inefficiencies in transferring CAT risks. Securitized risk-transfer instruments resolve some inefficiencies of the reinsurance market, but are subject to moral hazard, basis risk, credit risk, regulatory uncertainty, etc. The authors introduce some popular securitized solutions and use Merton's structural framework to demonstrate how to value these CAT-linked securities. The hybrid solutions by combining reinsurance with securitized CAT instruments are expected to offer promising applications for CAT risk management.

The authors introduce some popular securitized solutions and use Merton's structural framework to demonstrate how to value these CAT-linked securities. The hybrid solutions by combining reinsurance with securitized CAT instruments are expected to offer promising applications for CAT risk management.

This research reviews a broad array of impacts of CAT risks on the (re)insurance industry. CAT events challenge (re)insurance capacity and influence insurers' supply decisions and reconstruction costs in the aftermath of catastrophes. While losses from natural catastrophes are the primary threat to property–casualty insurers, the mortality risk posed by influenza pandemics is a leading CAT risk for life insurers. At the same time, natural catastrophes and man-made disasters cause distinct impacts on (re)insures. Man-made disasters can increase the correlation between insurance stocks and the overall market, and natural catastrophes reduce the above correlation. It should be noted that huge CAT losses can also improve (re)insurance demand during the postevent period and thus bring long-term effects to the (re)insurance industry.

The purpose of this paper is to verify recovery risk management capabilities by lessors. It tests several hypotheses and finds out interesting specific results for lessors.

The approach is empirical: two different database of leasing contracts are analysed with econometric methodologies.

There is clear evidence that: lessors are ex ante able to balance the probability of default and the loss given default case by case, using proper contract structures; and they carefully manage recovery procedures and strategies according to operations' characteristics.

The data used are large enough, but come from institutions concentrated in Italy. Future research could be extended to other relevant countries.

Results presented are verified in leasing companies which made a limited use of rating systems and credit risk model: they have been achieved by the continuous improvements of traditional lending practices. The development of modern reliable systems can enhance risk management capabilities; our findings can help building more structured and advanced credit risk management tools.

The paper adds to the literature in the sense that gives clear evidence of a neglected but important fact of real world credit markets: financial intermediaries have the capability of properly assessing risk components and manage loss given default (LGD) in order to control overall credit risk.

Probability of Default (PD) is a crucial credit risk parameter. International accords have motivated banks and credit institutions to adopt objective systems of evaluating and monitoring the PD. This study examines retail unsecured loans of a major Greek bank during the period of the financial crisis. It focusses on the stochastic behaviour of the financial states of the loans. It is tested whether a first-order Markov chain (MC) model describes sufficiently the transitions from one state to another. Moreover, Poisson regression models are estimated in order to calculate the limiting transition matrix, the limiting state probabilities and the PD. It is proved that the MC of the financial states of loans is non-homogeneous suggesting that the transition probabilities from one financial state to another are not constant across time. From the Poisson regression models, the transition probability matrix is estimated from one state to another in alternative time periods. From the limiting transition matrix, it is shown that if a loan is delayed then it is very likely to move towards the next worst case. The findings of this research could be useful for bank management.

Introducing radical changes to the methodologies for the determination of capital requirements, the final stage of the Basel III standards, which is referred to as “Basel IV” by the industry, will be a significant challenge for the global banking sector. This article reviews the main components of the new framework, analyses its ongoing implementation in the European Union and discusses its potential impact on banks, putting forward policy recommendations.

This article uses primary sources such as the publications by the Basel Committee for Banking Supervision and the European Commission. It also reviews the secondary sources, including both academic articles and analyses by various stakeholders. However, this article does not undertake any empirical analysis.

This article discusses that Basel IV will introduce strategic, operational and regulatory challenges for banks in scope. It also identifies a number of areas which are subject to further debate in the European Union such as the enhanced due diligence requirements under the new credit risk framework; governance, reporting and control rules under the operational risk framework; exemptions for certain derivative transactions under the credit valuation adjustment framework and the level of application of the capital floors within banking groups. This article concludes that the global implementation of the reforms by all jurisdictions and transposition into national banking laws concurrently with the European Union in line with the Basel Committee's implementation timeline is important from a financial stability standpoint.

The article presents an up-to-date and comprehensive review of the practical implications of Basel IV standards. It analyses the implementation of the standards in the case of the European Union, reviews the potential policy implications and presents recommendations for risk management practitioners.