Credit Derivatives Research Articles

Credit derivatives and corporate default prediction

There have been 128 defaults among U.S. CDS reference entities between 2001 and 2020. Within this sample, the five-year CDS spread is a significant predictor of corporate default in models with equity market covariates and firm attributes. This finding holds for forecast horizons up to 12 months, among financial and non-financial firms, within and without the great financial crisis, and is robust to the inclusion of corporate bond and equity options market information. A decomposition of the CDS spread into liquidity, physical default, and risk premium components shows that most of its predictive power for corporate default comes from the physical default component, both in- and out-of-sample. These results confirm the relevance of information contained in single-name CDS pricing to corporate default prediction.

Application of Credit Derivatives in Portfolio Management

The use of credit derivatives has grown considerably over the past decade, with participation from a diverse set of institutional investors. Specifically, investors increasingly are using credit default swaps (CDS), credit default swap indices (CDX), and options on CDX to manage their portfolios. In this article, the authors demonstrate how investors apply credit derivatives in the context of portfolio management. The authors show how CDS can be used to create synthetic corporate bonds and how investors structure basis trading opportunities by taking advantage of mispricing between CDS and corporate bonds. Further, the authors illustrate how investors apply options on CDX for the purpose of hedging the tail risks of a fixed income portfolio, and they include a discussion on various methods to reduce the cost of such tail-risk-hedging strategies.

Quantitative Structural Models to Assess Credit Risk on Individuals

Default Probabilities quantitatively measures the credit risk that a borrower will be unable or unwilling to repay its debt. An accurate model to estimate, as a function of time, these default probabilities is of crucial importance in the credit derivatives market. In this work, we adapt Merton’s [1] original works on credit risk, consumption and portfolio rules to model an individual wealth scenario, and apply it to compute this individual default probabilities. Using our model, we also compute the time depending individual default intensities, recovery rates, hazard rate and risk premiums. Hence, as a straight-forward application, our model can be used as novel way to measure the credit risk of individuals.

Enhanced disclosure of credit derivatives, information asymmetry and credit risk

AbstractThis study examines the extent to which enhanced disclosure of credit derivatives [Financial Accounting Standards Board (FASB) issued FASB Staff Position (FSP) Financial Accounting Standards (FAS) No. 133‐1] reduces information asymmetry and credit derivative sellers who experience a decrease in information asymmetry. I find that bid‐ask spread for credit derivative sellers with positive net protection is reduced in the post‐period, whereas that of sellers with negative net protection and the control groups is not. These results are consistent with the conjecture that market participants use enhanced disclosure to confirm or correct previous expectations about sellers’ credit risk. In addition, results in high versus low institutional groups suggest that bid‐ask spreads reduce in both groups but reduce more in the high institutional group in the post‐period, possibly due to information environment differences. Finally, I investigate the information content of two credit derivative exposure measures: maximum payment amount and fair value. Consistent with McAnally (1996), I find that the maximum payment amount is credit risk‐relevant. Thus, enhanced disclosure facilitates monitoring risk concentration and evaluating credit risk, thereby contributing to the stabilization of capital markets prior to regulation of the credit default swap (CDS) market.

Credit derivatives and loan yields

AbstractWe compare the loan yields of credit derivative (CRD) active bank holding companies (BHCs) with the loan yields of CRD inactive peers over the pre‐crisis, crisis (2008–10), and post‐crisis periods. During the post‐crisis period, protection purchasers report lower yields than their peers, while sellers report yields like those reported by their peers. The relation between the yield and commercial and industrial (C&I) loans is positive and significant during the pre‐ and post‐crisis periods, and CRD activities do not affect this relation. CRD activities are changing the relations between the loan yield and consumer loans, real estate loans, and securitization.

Fixed-Income ETFs: A liquidity illusion?

Exchange-Traded Funds (ETFs) have revolutionised the asset-management industry with high liquidity and low bid-asks allowing investors to access a diversified portfolio cheaply. The desirable liquidity characteristics of ETFs are, however, in contrast with their behavior in crisis periods. This paper studies the breakdown in the fixed-income ETFs (FIETFs) market, during the peak of the COVID-19-driven liquidity shock of March 2020. We argue that FIETFs provide an illusion of liquidity and the liquidity mismatch between the ETF and the underlying manifests itself in terms of very significant differences between the price and the Net Asset Value (NAV). We run a further analysis on the dislocation by comparing it with equity ETFs. Comparisons suggest that the cost of liquidity is very high for fixed-income ETFs with significant tracking errors during volatile periods in contrast with the better-behaved equity ETFs. Further analysis on the performance patterns of FIETFs indicates that both the price and NAV might have deviated from fundamentals with an overreaction in the ETF price accompanying an underreaction in the NAV. We also study the phenomena of ‘dealer inventory management’. FIETFs are uniquely different from equity ETFs in that the authorised participant (arbitrageurs) tend to be the large banks that are also market-makers in the underlying securities. Therefore, we show that the incentives of the arbitrageurs may not always be aligned towards arbitraging the price-NAV differential. In a novel empirical study using trading volumes and position changes for the largest corporate bond ETF (LQD), we suggest that inventory management by the larger broker-dealers may have exacerbated the dislocation. We believe that the conflicting objectives of dealers have further increased due to high balance-sheet costs imposed upon them post the Global Financial Crisis. Finally, we propose the use of derivatives, in particular credit derivatives, for the risk management of liquidity shocks. We show that the drawdown from rapid liquidity shocks can be reduced significantly through exposure to credit convexity.

Credit Default Swaps around the World

Abstract We analyze the impact of the introduction of credit default swaps (CDSs) on real decision-making within the firm. Our structural model predicts that CDS introduction increases debt capacity more when uncertainty about the credit events that trigger CDS payment is lower. Using a sample of more than 56,000 firms across 51 countries, we find that CDSs increase leverage more in legal and market environments where uncertainty about CDS obligations is reduced and when property rights are weaker. Our results highlight the importance of legal uncertainty in the interpretation of the underlying trigger events of global credit derivatives.

The Effect of company's Interest Coverage Ratio on the Structural and Reduced-Form Models, in Predicting Credit Derivatives Price

Derivatives pricing models use either fixed or variable interest rates at the corporate level to compensate for the devaluation, which results in an estimated accounting profit caused by the cash inflation at the maturity date. These models also fail to take into account the lost opportunity costs, which are considered a deficiency. Accordingly, the present study strives to remove this problem by adding the company's Interest Coverage Ratio (ICR) to pricing models; which is the novelty of this study. The research data were extracted from the Bloomberg Terminal for an eight-year period from 2008 to 2015. The statistical population of the research includes the North American and European companies recognized as the reference entities for Credit Default Swaps (CDS) in the given period, and the statistical sample consists of 125 companies. The data were analysed using four Artificial Neural Network (ANN) algorithms, viz., ANFIS, NNARX, AdaBoost, and SVM. . The results of the research indicated the increased predictive accuracy of the pricing models under scrutiny after adding the interest coverage ratio. The findings also shed light on the superiority of the intensity model over the structural model in prognosticating the price of CDS contracts.

Russian stock market: The practice of using credit derivatives

Subject. This article explores the practice of using credit derivatives in the Russian stock market. Objectives. The article aims to identify the problems and advantages of the Russian credit derivatives market, and propose certain improvements concerning this derivatives market segment. Methods. For the study, we used analysis, comparison, and statistical methods. Results. The article describes the problems and advantages of using credit derivatives in the Russian stock market and proposes certain activities to improve this segment of the market. Conclusions. The results obtained can be used by students and university staff to study over-the-counter derivatives. The proposed measures to improve the credit derivatives market can also be applied for practical purposes to improve the legislative framework and develop the market infrastructure.

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.

A measure of Turkey's sovereign and banking sector credit risk: Asset swap spreads

The existence of the credit derivatives written on the eurobonds such as credit default swaps or asset swaps allows policymakers and investors to monitor the evolvement of credit risk. However, these instruments are mostly available in advanced economies, whereas the market for credit derivatives in emerging market countries, including Turkey, is limited in terms of liquidity and maturity. In this regard, this study aims to construct a proxy for the credit risk of the Turkish Treasury and banking sector in international markets by calculating asset swap spread for US dollar-denominated fixed coupon eurobonds, which requires a robust estimation of the relevant yield curves. The study firstly presents the estimation of the sovereign and banking sector yield curves and then constructs a synthetic asset swap structure to obtain embedded credit risk premia in the eurobond curves. Our findings show that the proposed credit risk indicator is vastly correlated with credit default swap premium. In addition to this, estimated eurobond curves are also useful for monitoring borrowing cost dynamics of the Turkish Treasury and banking sector in international markets.

The Economics of Central Clearing

Central clearing counterparties (CCPs) have a variety of economic rationales. The Great Recession of 2007–2009 led regulators to mandate CCPs for most interest-rate and credit derivatives, markets in which large amounts of risks are transferred across agents. This change led to a large increase in CCP studies, which along with classical studies are surveyed in this article. For example, multilateral netting, the insurance against counterparty risk, the effect of CCPs on asset prices and fire sales, margins setting, the default waterfall, and CCP governance are discussed here. We review both CCP theory and empirical work and conclude by discussing regulatory issues.

Credit Derivatives and Bank Systemic Risk: Risk Enhancing or Reducing?

This study investigates the impact of credit derivative usage on U.S. bank holding companies' systemic risk from 2006 to 2018. The results show that an increase in bank holdings of credit derivatives subsequently increases their systemic risk. This is robust to a number of controls. Such findings have policy implications for regulators and market participants, as larger banks are in a higher risk category, potentially causing further disruption to financial markets.

Cross-Border Credit Derivatives Linkages

This paper is a first attempt to include credit derivatives in international macro-financial analysis. We document that gross credit derivatives holdings map to bilateral portfolio investment linkages. On a net basis, our results suggest an asymmetry between sectors and between net buyers and net sellers of CDSs. When a banking system is a net buyer of protection, the protection purchased is proportional to the debt securities held. Conversely, when a banking system is a net seller, the protection sold is proportional to the securities held. For investment funds, we find no aggregate relation between net CDSs and the debt securities held. JEL Classification: F34, F21

Mathematical Estimation Methods and Models for Industrial Companies

The collateralized debt obligations and credit default swaps applications are shown in this paper. The industry obligations secondary market risk estimation methods are considered in this work. The new methods taking into account statistically significant parameters for industrial credit derivatives portfolio are offered for single-name investment risks numerical experiments realization. The mathematical estimation of tranche were shown. The single and multiple name default obligations necessary mathematical modeling methods and formulae for the industrial materials manufacturers derivative credit tools market are shown. It is determined that the portfolio of synthetic debt tools is made of the given parameters. The task of a loss derivative tranches mathematical estimation is solved. Late defaults raise the equity tranches payment required sums with high spreads, early defaults reduce. Also the functional characteristics required for an estimation huge debts problem solving are partly considered in this paper. The problem of the default modeling for market tools and numerical simulation of the obligations influence on conditions of current bistability mode are shown here. Some credit derivatives of industrial manufacturers are demonstrated in the modeling process of default as an example. It is found that the model is an additional factor help us to estimate the default opportunity.

The Financial Crisis – Risk Transfer, Insurance Layers, and (No?) Reinsurance Culture

The financial crisis of 2007 has triggered various debates, ranging from the stability of the banking system to subtle technical issues regarding the Gaussian and other copulas. All these debates are important, and it might be good to start even a further one: Credit derivatives have much in common with treaty reinsurance, e.g. risk transfer via pooling and layering, scarce data, skewed distributions, and a limited number of specialized players in the market. All this leads to a very particular mixture of mathematical/statistical and behavioral challenges. Reinsurers have been struggling to cope with these, not always successfully, but they have learnt many lessons during their more-than-one century in business. This has led to certain rules adopted by the reinsurance market and to a certain mindset adopted by the individuals working in the industry. Some of the traditions established in the insurance and reinsurance world could possibly inspire markets like the credit derivatives market.

ANALYZING THE RELATIONSHIP BETWEEN DERIVATIVE USAGE AND SYSTEMIC RISK IN SOUTH AFRICA

This paper analyzes the relationship between derivative usage and systemic risk in South Africa. We employ expected shortfall as a measure of systemic risk for the banking sector. The more flexible Toda-Yamamoto Granger non-causality test is used to find the direction of causality between the variables, and the ARDL estimation technique is employed to estimate the specified model. We also test for the existence of a long-run relationship amongst the variables using the Bounds test approach. We find that credit derivatives and bank credit extensions increase systemic risk in the long run. Moreover, the systemic risk decreases in bank liquidity and usage of equity derivatives. However, in the short run, we find that increases in bank liquidity tend to increase systemic risk. We interpret this to imply that improvements in liquidity cause banks to undertake riskier transactions. Furthermore, the market can also perceive central bank interventions to increase liquidity as a sign of worsening financial conditions. On the backdrop of these results, we recommend continuous monitoring of derivatives markets to avoid risk excesses that could pose threat to the whole financial system.

Financial engineering to optimize risk management in banks based on Interest Rate Swaps to better hedge the exposure to interest rate fluctuations the case of banks in Syria

The banking system is affected by uncertainties related to the evolution of pandemic. One of the identified risks is that of a fluctuation of rates. Volatility of Interest rates is one of the major risks for the banking system. Therefore, financial engineering can be used as a very important hedging practice for banks against such a risk. The aim of this study is to develop a risk hedging mechanism to better overcome market volatility by hedging position against the exposure to interest rate risk based on credit derivatives. Therefore, this study uses Interest Rate Swaps (IRS)s to better hedge the exposure of banks to interest rate fluctuations in stress conditions giving consideration to the case study of banks in Syria in optimizing hedging practices based on Interest Rate Swaps. The aim is to use financial engineering to provide banks with a hedging technique to better absorb shocks in times of stress conditions. This has been discussed and illustrated with visual model diagrams. The case study of banks in Syria is not just the story of individual banks but a window into how to hedge the exposure of banks in stress conditions. In the end, most banking crises are quite similar. The recommendations set out in this study provide banks with an optimized hedging practice which is not part of current financial engineering at banks in Syria.

Parsimonious Credit Risk Pricing Model for Corporate Bonds and Credit Derivatives Markets

Parsimonious Credit Risk Pricing Model for Corporate Bonds and Credit Derivatives Markets

Corporate Credit Derivatives

Corporate Credit Derivatives