Sources Of Credit Risk Research Articles

FACTOR COPULA MODEL FOR PORTFOLIO CREDIT RISK

A critical aspect in the valuation and risk management of multi-name credit derivatives is the modeling of the dependence among sources of credit risk. The dependence modeling poses difficulties in the pricing of a multi-name credit derivatives, in the estimation of the value-at-risk of a portfolio, or in the pricing of some other basket credit derivative as the description not only on the default arrival in an individual reference entity but on the default dependence among entities in the portfolio should be considered. Although the elliptical models have been widely used due to their mathematical tractability, the dependence modeling using the multi-dimensional Lévy process has shown growing interest among researchers despite its complexity. In this paper, we introduce one factor copula model for portfolio credit risk based on Normal Tempered Stable (NTS) distribution and calibrate the model through 5-year synthetic Collateralized Debt Obligation (CDO) tranche spreads under a large homogeneous portfolio approximation. The calibration results show that the one factor copula model based on NTS distribution is more flexible and provides a dependence structure fitting market CDO tranche spreads. As one of the major applications of the dependence modeling in credit risk, this model shares the advantage of the Gaussian one factor model, and all extensions and implementation methods used for it can be utilized.

Integrating Climate Risks into Credit Risk Assessment - Current Methodologies and the Case of Central Banks Corporate Bond Purchases

Climate change and the transition to a low-carbon economy to mitigate it engender significant economic costs. These costs are ultimately borne by households and firms, affecting their cash flows and wealth, which are key determinants of their credit worthiness. Climate-related costs are thus a source of credit risk. An accurate assessment of all credit risks, including climate credit risk, is central for creditors including central banks. If they underestimate it, they are exposed to financial losses and to the risk of holding assets of inadequate credit quality. Assessing climate risks requires methodologies based on forward-looking scenarios, on complex cause-and-effect linkages and on data that has not been observed in the past. Such models are at their infancy, but already offer meaningful insights. This note provides an overview of key components that such models are built on and illustrates them with examples of the analytics that are already available. It also applies one of the available methodologies to assess transition risk to the corporate bond holdings of the European Central Bank.

Comparative Analysis on the Sources of Credit Risk in Nepalese Commercial Banks

The study was conducted to find out the significant differences between state-owned bank and private sector bank in the practice of measuring the sources of credit risk. This paper attempts to ascertain the perceptions of Nepalese bankers about the importance of identification practice of credit risk sources within the specific borrowers. The result of the study indicates that the collaterals, characters, capacity, capital, condition, legality of the business, economy of the country, business environment and industrial relation were consider as a sources of credit risk in Nepalese banking sector. The result shows that level of the credit risk sources were disparity the Nepalese commercial banks. In addition, Collaterals provided as a security by the borrower, characters of the borrower, capacity, legality of the business, economy of the country and industrial relation were found significant predictor for the sources of credit risk. Moreover, there was a positive relationship between identification practice and sources of credit risk.

A Consistent Stochastic Model of the Term Structure of Interest Rates for Multiple Tenors

Explicitly taking into account the risk incurred when borrowing at a shorter tenor versus lending at a longer tenor (roll-over risk), we construct a stochastic model framework for the term structure of interest rates in which a frequency basis (i.e. a spread applied to one leg of a swap to exchange one floating interest rate for another of a different tenor in the same currency) arises endogenously. This roll-over risk consists of two components, a credit risk component due to the possibility of being downgraded and thus facing a higher credit spread when attempting to roll over short-term borrowing, and a component reflecting the (systemic) possibility of being unable to roll over short-term borrowing at the reference rate (e.g., LIBOR) due to an absence of liquidity in the market. The modelling framework is of reduced form in the sense that (similar to the credit risk literature) the source of credit risk is not modelled (nor is the source of liquidity risk). However, the framework has more structure than the literature seeking to simply model a different term structure of interest rates for each tenor frequency, since relationships between rates for all tenor frequencies are established based on the modelled roll-over risk. We proceed to consider a specific case within this framework, where the dynamics of interest rate and roll-over risk are driven by a multifactor Cox/Ingersoll/Ross-type process, show how such model can be calibrated to market data, and used for relative pricing of interest rate derivatives, including bespoke tenor frequencies not liquidly traded in the market.

Participation in Leveraged Loans: It's Not Just About Credit Risk

Even as banks have decreased their exposure to residential mortgage loans since 2008, bank exposure to leveraged lending has risen dramatically. The $1 trillion total asset leveraged loan market poses a significant and growing source of credit risk to U.S. depository institutions and investors. Further, the full extent of the risk posed by leveraged loans as an asset class is not adequately understood by investors, regulators, and the markets as a whole. In addition to the well-documented credit risk posed by leverage loans, KBRA believes that the widespread practice of participating shares of leveraged loans represents a significant additional risk to financial institutions and other investors from this asset class.

Stochastic Optimization for Portfolios Consisting of Corporate Bonds in an Integrated Market and Credit Risk Framework

High credit spreads in 2009 have attracted a lot of interest from investors seeking higher yields outside of government bonds. Investment grade corporate bonds have indeed been a popular investment in 2009. The corporate bond market today is one of the largest and most liquid of the financial markets. Corporate bonds markets can be appealing as they are generally considered safer than stocks and they often provide higher yields and more liquidity than government bonds markets. After the World Financial Crisis central banks set interest rates at their historically low level affecting positively bonds’ prices. The question for a corporate bond portfolio manager is which decisions to take in order to hedge portfolio against volatility of interest rates. Additionally managers have to analyse the factors that affect bonds’ prices volatility and is due to credit risk. For this reasons managers have to implement a risk management framework in order to identify the factor that affect whole portfolio value and finally optimize its value. In an integrated market and risk perspective the future value of a corporate bond portfolio is decomposed into the risk that a bond issued by a firm downgraded by rating agencies and possible drives to default, the correlation between credit events, the risk that changes occur on the average spread of exposures with the same final rating as the firm and the effect of interest rate uncertainty.In chapter 1 we introducing the main types of bonds, analyzing how to valuate non-callable bonds and calculating their yielding and we present factors that affect bond process and price volatility when interest rates changes a bit in a parallel way.In chapter 2 we make an introduction to risk management by referring the risks that affect corporate bond prices. Then we explain the term structure of interest rates and how we can immunize a portfolio of bonds when interest rates changes in a parallel way. Then we introduce a multi-factor model for corporate bonds and present a portfolio immunization model based on factors to protect portfolio value against non-parallel changes of interest rates, introducing the appearance of shape risk.In chapter 3 we added an introduction of credit risk referring the main sources of credit risk in a portfolio of corporate bonds. Then we model credit risk based on a reduced-form approach which defines the default as the instantaneous likelihood of default, called the hazard rate. In chapter 4 we present an integrated market and credit risk framework using scenario generation in order later to optimize a corporate bond portfolio using stochastic programming models. Scenario generation is a tool to model the uncertainty. We develop a model for stochastic interest rates and credit spreads based on the Hull-White algorithm. Interest rates and credit spreads stochasticity based on extended Vasicek model and we produce scenarios of interest rates and credit spreads using trinomial lattice structures which capture the correlation between interest rates and default intensities. Finally we model the correlation between default and migration.In chapter 5 we present the necessity for Conditional Value-at-Risk as a coherent risk measure for corporate bond portfolios. The right risk measure is of paramount importance when we optimize corporate bond portfolios because of tail effects (heavy tails) in the distribution of returns that could result in significant losses and destroy portfolio efficiency. Then we present a linear program for minimizing portfolio risk using CVaR.In chapter 6 we implement portfolio optimization models using stochastic programming in a dynamic aspect. Initially we present an anticipative model where we only take into account stochasticity in future prices without taking recourse actions. Then we present a multistage recourse model where we can take recourse decisions at the preceding time periods based on information we take at discrete time in the future. We assume that there are transaction costs when we buy or sell bonds in order to make models more sophisticated. Finally we analyze one by one the equations of multistage recourse model.

Fast Simulation of Multifactor Portfolio Credit Risk

This paper develops rare-event simulation methods for the estimation of portfolio credit risk—the risk of losses to a portfolio resulting from defaults of assets in the portfolio. Portfolio credit risk is measured through probabilities of large losses, which are typically due to defaults of many obligors (sources of credit risk) to which a portfolio is exposed. An essential element of a portfolio view of credit risk is a model of dependence between these sources of credit risk: large losses occur rarely and are most likely to result from systematic risk factors that affect multiple obligors. As a consequence, estimating portfolio credit risk poses a challenge both because of the rare-event property of large losses and the dependence between defaults. To address this problem, we develop an importance sampling technique within the widely used Gaussian copula model of dependence. We focus on difficulties arising in multifactor models—that is, models in which multiple factors may be common to multiple obligors, resulting in complex dependence between defaults. Our importance sampling procedure shifts the mean of the common factor to increase the frequency of large losses. In multifactor models, different combinations of factor outcomes and defaults can produce large losses, so our method combines multiple importance sampling distributions, each associated with a shift in the mean of common factors. We characterize “optimal” mean shifts. Finding these points is both a combinatorial problem and a convex optimization problem, so we address computational aspects of this step as well. We establish asymptotic optimality results for our method, showing that—unlike standard simulation—it remains efficient as the event of interest becomes rarer.

The Basic Analytics of Access to Financial Services

Access to financial services, or rather the lack thereof, is often indiscriminately decried as problem in many developing countries. This paper argues that the “problem of access” should rather be analyzed by identifying different demand and supply constraints. We use the concept of an access possibilities frontier, drawn for a given set of state variables, to distinguish between cases where a financial system settles below the constrained optimum, cases where this constrained optimum is too low, and—in credit services—cases where the observed outcome is excessively high. We distinguish between payment and savings services and fixed intermediation costs, on the one hand, and lending services and different sources of credit risk, on the other hand. We include both supply and demand side frictions that can lead to lower access. The analysis helps identify bankable and banked population, the binding constraint to close the gap between the two, and policies to prudently expand the bankable population. This new conceptual framework can inform the debate on adequate policies to expand access to financial services and can serve as basis for an informed measurement of access.

Off-balance-sheet credit risk of the top 20 Japanese banks

The study of off-balance-sheet (OBS) credit risk across the top 20 Japanese banks indicated that financial derivatives were heavily used by the top four banks and that loan commitments are the largest source of credit risk among traditional OBS instruments. The notional amounts of derivative contracts make up 92 per cent of total OBS activities. There is a wide difference across the banks in the use of derivative leverage. As compared to the US and European banks, Japanese banks use fewer OBS instruments as a percentage of their assets. This implies that Japanese banks are more conservative and risk averse in general than their US or European counterparts, especially given the bad financial condition of the banks and Japan.

Off-balance-sheet credit risk of the top 20 European commercial banks

The comparison of off-balance-sheet (OBS) credit risk of the top 20 European commercial banks indicated that loan commitments are the largest source of credit risk among traditional OBS instruments. However, the notional amounts of derivative activities make up around 95 per cent of the total OBS exposure. Here, a concentration of exposure with a small group of banks can be found. An analysis by country of origin points to national differences in the use of derivative leverage. In comparison with US commercial banks, European banks use fewer OBS activities as a multiple of on-balance-sheet assets. The disclosure and reporting by European banks regarding OBS activities varies widely and is less comparable than US activities.

Credit Spread Arbitrage in Emerging Eurobond Markets

In the corporate emerging Eurobond fixed-income market there are two main sources of credit risk: sovereign risk and the relative credit quality of issuers of the eurobonds. This article presents a model to estimate, in a one-step procedure, both the term structure of interest rates and the credit spread function of a diversified international portfolio of Eurobonds with different credit ratings. The estimated term structures can be used to analyze credit spread arbitrage opportunities in Eurobond markets. Numerical examples in the Argentinean, Brazilian, and Mexican Eurobond markets illustrate the practical use of the methodology.