scholarly journals Goodness-of-Fit of Logistic Regression of the Default Rate on GDP Growth Rate and on CDX Indices

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1930
Author(s):  
Kuang-Hua Hu ◽  
Shih-Kuei Lin ◽  
Yung-Kang Ching ◽  
Ming-Chin Hung
Keyword(s):  
Logistic Regression ◽  
Goodness Of Fit ◽  
Explanatory Power ◽  
Credit Default Swap ◽  
Estimation Model ◽  
Loss Given Default ◽  
Explanatory Variables ◽  
Gdp Growth Rate ◽  
Credit Default ◽  
Default Rate

Under the Basel II and Basel III agreements, the probability of default (PD) is a key parameter used in calculating expected credit loss (ECL), which is typically defined as: PD × Loss Given Default × Exposure at Default. In practice or in regulatory requirements, gross domestic product (GDP) has been adopted in the PD estimation model. Due to the problem of excessive fluctuation and highly volatile ECL estimation, models that produce satisfactory PD and thus ECL estimations in the context of existing risk management techniques are lacking. In this study, we explore the usage of the credit default swap index (CDX), a market’s expectation of future PD, as a predictor of the default rate (DR). By comparing the goodness-of-fit of logistic regression, several conclusions are drawn. Firstly, in general, GDP has considerable explanatory power for the default rate which is consistent with current models in practice. Secondly, although both GDP and CDX fit the DR well for rating B class, CDX has a significantly better fit of DR for ratings [A, Baa, Ba]. Thirdly, compared with low-rated companies, the relationship between the DR and GDP is relatively weak for rating A. This phenomenon implies that, in addition to using macroeconomic variables and firm-specific explanatory variables in the PD estimation model, high-rated companies exhibit a greater need to use market supplemental information, such as CDX, to capture the changes in the DR.

scholarly journals Component Structure of Credit Default Swap Spreads and Their Determinants

2018 ◽  
Vol 8 (4) ◽  
pp. 248
Author(s):  
Ramaprasad Bhar ◽  
David Colwell ◽  
Peipei Wang
Keyword(s):  
Yield Curve ◽  
Credit Default Swap ◽  
Component Structure ◽  
Explanatory Variables ◽  
Component Framework ◽  
Credit Default ◽  
Default Swap ◽  
Swap Spreads ◽  
Transitory Component

In this paper, we decompose credit default swap (CDS) spreads into a transitory component and a persistent component and test how these components are affected by the theoretical explanatory variables. We find significant but differing impacts of these explanatory variables on the extracted components. For example, equity volatility seems to have a larger influence on the transitory component, suggesting that its effect may be mostly short-lived, while our proxy for illiquidity has a greater impact on the persistent component indicating its more enduring effect. Also, the slope of the yield curve has impacts with opposite signs on the two components and so our analysis thus helps address the conflicting results reported in earlier studies without such a component framework. These results indicate that a two-factor formulation may be needed to model CDS options.

scholarly journals The Determinants of Credit Default Swap Premia

2009 ◽  
Vol 44 (1) ◽  
pp. 109-132 ◽  
Author(s):  
Jan Ericsson ◽  
Kris Jacobs ◽  
Rodolfo Oviedo
Keyword(s):  
Default Risk ◽  
Common Factor ◽  
Explanatory Power ◽  
Empirical Work ◽  
Credit Default Swap ◽  
Principal Component ◽  
Corporate Bond ◽  
Credit Default ◽  
Default Swap ◽  
Swap Spreads

AbstractVariables that in theory determine credit spreads have limited explanatory power in existing empirical work on corporate bond data. We investigate the linear relationship between theoretical determinants of default risk and default swap spreads. We find that estimated coefficients for a minimal set of theoretical determinants of default risk are consistent with theory and are significant statistically and economically. Volatility and leverage have substantial explanatory power in univariate and multivariate regressions. A principal component analysis of residuals and spreads indicates limited evidence for a residual common factor, confirming that the theoretical variables explain a significant amount of the variation in the data.

scholarly journals Splitting Credit Risk into Systemic, Sectorial and Idiosyncratic Components

2019 ◽  
Vol 12 (3) ◽  
pp. 129
Author(s):  
Alfonso Novales ◽  
Alvaro Chamizo
Keyword(s):  
Risk Factor ◽  
Credit Risk ◽  
Interest Rates ◽  
Explanatory Power ◽  
Credit Default Swap ◽  
Global Risk ◽  
Additional Information ◽  
The North ◽  
Credit Default ◽  
Highly Correlated

We provide a methodology to estimate a global credit risk factor from credit default swap (CDS) spreads that can be very useful for risk management. The global risk factor (GRF) reproduces quite well the different episodes that have affected the credit market over the sample period. It is highly correlated with standard credit indices, but it contains much higher explanatory power for fluctuations in CDS spreads across sectors than the credit indices themselves. The additional information content over iTraxx seems to be related to some financial interest rates. We first use the estimated GRF to analyze the extent to which the eleven sectors we consider are systemic. After that, we use it to split the credit risk of individual firms into systemic, sectorial, and idiosyncratic components, and we perform some analyses to test that the estimated idiosyncratic components are actually firm-specific. The systemic and sectorial components explain around 65% of credit risk in the European industrial and financial sectors and 50% in the North American sectors, while 35% and 50% of risk, respectively, is of an idiosyncratic nature. Thus, there is a significant margin for portfolio diversification. We also show that our decomposition allows us to identify those firms whose credit would be harder to hedge. We end up analyzing the relationship between the estimated components of risk and some synthetic risk factors, in order to learn about the different nature of the credit risk components.

An Explicit, Multi-Factor Credit Default Swap Pricing Model with Correlated Factors

2008 ◽  
Vol 43 (1) ◽  
pp. 123-160 ◽  
Author(s):  
Ren-Raw Chen ◽  
Xiaolin Cheng ◽  
Frank J. Fabozzi ◽  
Bo Liu
Keyword(s):  
Goodness Of Fit ◽  
Credit Default Swap ◽  
Change Of Measure ◽  
Computationally Efficient ◽  
Period 2 ◽  
Credit Default ◽  
Risky Bonds ◽  
Default Swap ◽  
The Interest Rate ◽  
Correlated Factors

AbstractWith the recent significant growth in the single-name credit default swap (CDS) market has come the need for accurate and computationally efficient models to value these instruments. While the model developed by Duffie, Pan, and Singleton (2000) can be used, the solution is numerical (solving a series of ordinary differential equations) rather than explicit. In this paper, we provide an explicit solution to the valuation of a credit default swap when the interest rate and the hazard rate are correlated by using the “change of measure” approach and solving a bivariate Riccati equation. CDS transaction data for the period 2/15/2000 through 4/8/2003 for 60 firms are used to test both the goodness of fit of the model and provide estimates of the influence of economic variables in the market for credit-risky bonds.

scholarly journals EXAMINING WHAT BEST EXPLAINS CORPORATE CREDIT RISK: ACCOUNTING-BASED VERSUS MARKET-BASED MODELS

2013 ◽  
Vol 15 (2) ◽  
pp. 253-176 ◽  
Author(s):  
Antonio Trujillo-Ponce ◽  
Reyes Samaniego-Medina ◽  
Clara Cardone-Riportella
Keyword(s):  
Credit Risk ◽  
Credit Rating ◽  
Explanatory Power ◽  
Credit Default Swap ◽  
Comprehensive Model ◽  
Corporate Credit ◽  
Credit Default ◽  
Recent Financial Crisis ◽  
Market Variables ◽  
Swap Spreads

This paper uses a sample of 2,186 credit default swap spreads quoted in the European market during the period 2002–2009 to empirically analyze which model – accounting- or market-based – better explains corporate credit risk. We find little difference in the explanatory power of these two approaches. Our results indicate that a comprehensive model that combines accounting- and market-based variables is the best option to explain the credit risk, suggesting that both types of data are complementary. We also demonstrate that the explanatory power of credit risk models is particularly strong during periods of high uncertainty, such as those experienced in the recent financial crisis. Finally, the comprehensive model continues to produce the best results if the credit rating is used as the proxy for credit risk; however, accounting variables currently appear to have a more important role than market variables in determining corporate credit ratings.

scholarly journals Separating the Components of Default Risk: A Derivative-Based Approach

2015 ◽  
Vol 05 (01) ◽  
pp. 1550005 ◽  
Author(s):  
Anh Le
Keyword(s):  
Default Risk ◽  
Credit Default Swap ◽  
Loss Given Default ◽  
Positive Correlation ◽  
Credit Default ◽  
Risk Neutral ◽  
Default Swap ◽  
Using Data ◽  
Pricing Framework

In this paper, I propose a general pricing framework that allows the risk neutral dynamics of loss given default (Lℚ) and default probabilities (λℚ) to be separately and sequentially discovered. The key is to exploit the differentials in Lℚ exhibited by different securities on the same underlying firm. By using equity and option data, I show that one can efficiently extract pure measures of λℚ that are not contaminated by recovery information. Equipped with this knowledge of pure default dynamics, prices of any defaultable security on the same firm with non-zero recovery can be inverted to compute the associated Lℚ corresponding to that particular security. Using data on credit default swap premiums, I show that, cross-sectionally, λℚ and Lℚ are positively correlated. In particular, this positive correlation is strongly driven by firms' characteristics, including leverage, volatility, profitability and q-ratio. For example, 1% increase in leverage leads to 0.14% increase in λℚ and 0.60% increase in Lℚ.

Anchoring Credit Default Swap Spreads to Firm Fundamentals

2016 ◽  
Vol 51 (5) ◽  
pp. 1521-1543 ◽  
Author(s):  
Jennie Bai ◽  
Liuren Wu
Keyword(s):  
Explanatory Power ◽  
Credit Default Swap ◽  
Future Market ◽  
Cross Sectional ◽  
Credit Default ◽  
Out Of Sample ◽  
Shrinkage Method ◽  
Distance To Default ◽  
Bayesian Shrinkage ◽  
Default Swap

In this article, we examine the extent to which firm fundamentals can explain the cross-sectional variation in credit default swap (CDS) spreads. We construct a fundamental CDS valuation by combining the Merton distance-to-default measure with a long list of firm fundamentals via a Bayesian shrinkage method. Regressing CDS quotes against the fundamental valuation cross-sectionally generates an averageR2of 77%. The explanatory power is stable over time and robust in out-of-sample tests. Deviations between market quotes and the valuation predict future market movements. The results highlight the important role played by firm fundamentals in differentiating the credit spreads of different firms.

Issuers’ credit risk and pricing of warrants in the recent financial crisis

2015 ◽  
Vol 16 (4) ◽  
pp. 444-462 ◽  
Author(s):  
Andrea Schertler ◽  
Saskia Stoerch
Keyword(s):  
Credit Risk ◽  
Design Methodology ◽  
Explanatory Power ◽  
Credit Default Swap ◽  
Price Setting ◽  
New Approach ◽  
Content Type ◽  
Credit Default ◽  
Recent Financial Crisis

Purpose – The purpose of this paper is to investigate whether factor sensitivities of margins of bank-issued warrants depend on issuers’ credit risk during the period of economic turmoil between January 2008 and June 2010. Design/methodology/approach – Therefore, first, Fama–MacBeth estimations were applied and it was demonstrate that the sensitivities of margins in terms of time to maturity and moneyness vary substantially over time; the average outcomes are similar to the results of classical pooled estimations. Findings – Then, time-series tests were used and it was found that the steepness of the issuers’ credit default swap (CDS) spread curves correlates negatively with the time-to-maturity sensitivities as well as with the explanatory power of Fama–MacBeth estimations. Research limitations/implications – These findings indicate that the life-cycle hypothesis is weakened when the issuers’ CDS spread curves become steeper. Originality/value – Thus, this study offers a new approach to gain insights into the role of issuers’ credit risk on price setting behavior.

Estimating credit default swap spreads using accounting data, market quotes and credit ratings: the European Banks Case

2015 ◽  
pp. 59-81
Author(s):  
Enrico Laghi ◽  
Michele Di Marcantonio ◽  
Eugenio D'Amico
Keyword(s):  
Stock Price ◽  
Statistical Significance ◽  
Explanatory Power ◽  
Credit Default Swap ◽  
Accounting Data ◽  
Credit Default ◽  
Data Market ◽  
European Banks ◽  
Default Swap ◽  
Swap Spreads

The aim of this paper is to define a model for estimating the theoretical Credit Default Swap spread of European banks considering firms' accounting data, market quotes, official ratings and macroeconomic variables. We detect a significant log-linear relation between Credit Default Swaps spreads and four explanatory variables determined on the basis of the stock price, the financial structure, the equity composition, the incidence of the reserve for loan losses on total loans, the official ratings and macroeconomic indicators of the country of domicile of each company. The empirical results show that for the period from 2008 to 2013 the model has a high statistical significance and a remarkable explanatory power. Our main contribution to the existing literature is the exploration of new determinants of banks' credit risk and the provision of new evidence on the determinants of banks' default risk in the crisis and post-crisis European context. Furthermore, we define a practical model for estimating Credit Default Swap spreads of banks suitable for professional use.

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