{"title":"The survival analysis approach in Basel II credit risk management: modeling danger rates in the loss given default parameter","authors":"S. Bonini, Giulia Caivano","doi":"10.21314/JCR.2013.155","DOIUrl":"https://doi.org/10.21314/JCR.2013.155","url":null,"abstract":"","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"40 1","pages":"101-118"},"PeriodicalIF":0.3,"publicationDate":"2013-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86922768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper examines the determinants of creditor recoveries from defaulted debt instruments, an important yet under-studied area in investment and risk management. First, we argue that to properly measure a debt instrument’s relative position in a firm’s debt structure, debt pari passu to the instrument must be taken into account. We propose a new measure of seniority and find that it is the most important determinant of recovery rates, explaining more recovery variations than the combination of all commonly used instrument-level variables, including seniority class, collateral type, and percentage above. Second, we find that firm-level variables, especially the trailing 12-month stock returns, are more critical than industryor macroeconomic-level variables, although the latter can also help, for private firms because stock price information is not available for such firms. In contrast with earlier studies, we find that the relative contribution of the industry and macroeconomic variables varies with the sample, model specification, and especially the modeling technique used.
{"title":"Debt Structure, Market Value of Firm, and Recovery Rate","authors":"M. Qi, Xinlei Zhao","doi":"10.21314/JCR.2013.157","DOIUrl":"https://doi.org/10.21314/JCR.2013.157","url":null,"abstract":"This paper examines the determinants of creditor recoveries from defaulted debt instruments, an important yet under-studied area in investment and risk management. First, we argue that to properly measure a debt instrument’s relative position in a firm’s debt structure, debt pari passu to the instrument must be taken into account. We propose a new measure of seniority and find that it is the most important determinant of recovery rates, explaining more recovery variations than the combination of all commonly used instrument-level variables, including seniority class, collateral type, and percentage above. Second, we find that firm-level variables, especially the trailing 12-month stock returns, are more critical than industryor macroeconomic-level variables, although the latter can also help, for private firms because stock price information is not available for such firms. In contrast with earlier studies, we find that the relative contribution of the industry and macroeconomic variables varies with the sample, model specification, and especially the modeling technique used.","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"49 1","pages":"3-37"},"PeriodicalIF":0.3,"publicationDate":"2013-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84630441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. M. Corcuera, Jan De Spiegeleer, Albert Ferreiro-Castilla, A. Kyprianou, D. Madan, W. Schoutens
We look at the problem of pricing CoCo bonds where the underlying risky asset dynamics are given by a smile conform model, more precisely an exponential Levy process incorporating jumps and heavy tails. A core mathematical quantity that is needed in closed form in order to produce an exact analytical expression for the price of a CoCo is the law of the infimum of the underlying equity price process at a fixed time. With the exception of Brownian motion with drift, no such closed analytical form is available within the class of Levy process that are suitable for financial modeling. Very recently however there has been some remarkable progress made with the theory of a large family of Levy processes, known as β-processes, cf. Kuznetsov [12] and Kuznetsov et al. [14]. Indeed for this class of Levy processes, the law of the infimum at an independent and exponentially distributed random time can be written down in terms of the roots and poles of its characteristic exponent; all of which are easily found within regularly spaced intervals along one of the axes of the complex plane. Combining these results together with a recently suggested Monte-Carlo technique, due to Kuznetsov et al. [13], which capitalises on the randomised law of the infimum we show the efficient and effective numerical pricing of CoCos. We perform our analysis using a special class of β-processes, known as β-VG, which have similar characteristics to the classical Variance-Gamma model. The theory is put to work by performing two case studies. After calibrating our model to market data, we price and analyze one of the Lloyds CoCos as well as the first Rabo CoCo.
{"title":"Pricing of contingent convertibles under smile conform models","authors":"J. M. Corcuera, Jan De Spiegeleer, Albert Ferreiro-Castilla, A. Kyprianou, D. Madan, W. Schoutens","doi":"10.21314/JCR.2013.163","DOIUrl":"https://doi.org/10.21314/JCR.2013.163","url":null,"abstract":"We look at the problem of pricing CoCo bonds where the underlying risky asset dynamics are given by a smile conform model, more precisely an exponential Levy process incorporating jumps and heavy tails. A core mathematical quantity that is needed in closed form in order to produce an exact analytical expression for the price of a CoCo is the law of the infimum of the underlying equity price process at a fixed time. With the exception of Brownian motion with drift, no such closed analytical form is available within the class of Levy process that are suitable for financial modeling. Very recently however there has been some remarkable progress made with the theory of a large family of Levy processes, known as β-processes, cf. Kuznetsov [12] and Kuznetsov et al. [14]. Indeed for this class of Levy processes, the law of the infimum at an independent and exponentially distributed random time can be written down in terms of the roots and poles of its characteristic exponent; all of which are easily found within regularly spaced intervals along one of the axes of the complex plane. Combining these results together with a recently suggested Monte-Carlo technique, due to Kuznetsov et al. [13], which capitalises on the randomised law of the infimum we show the efficient and effective numerical pricing of CoCos. We perform our analysis using a special class of β-processes, known as β-VG, which have similar characteristics to the classical Variance-Gamma model. The theory is put to work by performing two case studies. After calibrating our model to market data, we price and analyze one of the Lloyds CoCos as well as the first Rabo CoCo.","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"66 1","pages":"121-140"},"PeriodicalIF":0.3,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86565238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The objective of this paper is to help a bank originator of a collateralized debt obligation (CDO) to build a maximally profitable CDO. We consider an optimization framework for structuring CDOs. The objective is to select attachment/ detachment points and underlying instruments in the CDO pool. In addition to “standard” CDOs we study so-called “step-up” CDOs. In a standard CDO contract the attachment/detachment points are constant over the life of a CDO. In a step-up CDO the attachment/detachment points may change over time. We show that step-up CDOs can save about 25–35% of tranche spread payments (ie, profitability of CDOs can be boosted by about 25–35%). Several optimization models are developed from the bank originator perspective. We consider a synthetic CDO where the goal is to minimize payments for the credit risk protection (premium leg), while maintaining a specific credit rating (assuring the credit spread) of each tranche and maintaining the total incoming credit default swap spread payments. The case study is based on the time-to-default scenarios for obligors (instruments) generated by the Standard & Poor’s CDO Evaluator. The Portfolio Safeguard package by AORDA was used to optimize the performance of several CDOs based on example data.
{"title":"Optimal structuring of collateralized debt obligation contracts: an optimization approach","authors":"Alexander Veremyev, Peter Tsyurmasto, S. Uryasev","doi":"10.21314/JCR.2012.153","DOIUrl":"https://doi.org/10.21314/JCR.2012.153","url":null,"abstract":"The objective of this paper is to help a bank originator of a collateralized debt obligation (CDO) to build a maximally profitable CDO. We consider an optimization framework for structuring CDOs. The objective is to select attachment/ detachment points and underlying instruments in the CDO pool. In addition to “standard” CDOs we study so-called “step-up” CDOs. In a standard CDO contract the attachment/detachment points are constant over the life of a CDO. In a step-up CDO the attachment/detachment points may change over time. We show that step-up CDOs can save about 25–35% of tranche spread payments (ie, profitability of CDOs can be boosted by about 25–35%). Several optimization models are developed from the bank originator perspective. We consider a synthetic CDO where the goal is to minimize payments for the credit risk protection (premium leg), while maintaining a specific credit rating (assuring the credit spread) of each tranche and maintaining the total incoming credit default swap spread payments. The case study is based on the time-to-default scenarios for obligors (instruments) generated by the Standard & Poor’s CDO Evaluator. The Portfolio Safeguard package by AORDA was used to optimize the performance of several CDOs based on example data.","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"42 1","pages":"133-155"},"PeriodicalIF":0.3,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82526630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Collateralized credit default swaps and default dependence: implications for the central counterparties","authors":"M. Fujii, Akihiko Takahashi","doi":"10.21314/JCR.2012.143","DOIUrl":"https://doi.org/10.21314/JCR.2012.143","url":null,"abstract":"","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"9 6 1","pages":"97-113"},"PeriodicalIF":0.3,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78464237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A survey of loan credit default swap pricing models","authors":"M. Ong, Dan Li, David Lu","doi":"10.21314/JCR.2012.144","DOIUrl":"https://doi.org/10.21314/JCR.2012.144","url":null,"abstract":"","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"32 1","pages":"67-96"},"PeriodicalIF":0.3,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87939455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An asset drop model as an alternative to the treatment of double defaults within the Basel framework","authors":"S. Ebert, E. Lütkebohmert","doi":"10.21314/JCR.2012.145","DOIUrl":"https://doi.org/10.21314/JCR.2012.145","url":null,"abstract":"","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"43 1","pages":"41-63"},"PeriodicalIF":0.3,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75106600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Recursive formulas for the default probability distribution with applications in Markov chain-based intensity models","authors":"D. Miao, B. Hambly","doi":"10.21314/JCR.2012.147","DOIUrl":"https://doi.org/10.21314/JCR.2012.147","url":null,"abstract":"","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"50 1","pages":"3-40"},"PeriodicalIF":0.3,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73817227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper examines the empirical relationship between credit risk and interest rate risk. We use the credit default swap (CDS) spread as our measure of credit risk. Also, we control for the variation in the fair-value spread that combines multiple sources of default risk, including the market price of risk (Sharpe ratio), the loss given default (LGD), and the expected default frequency (EDF). After taking into account the fair-value spread, a liquidity risk factor, and several proxies for the general state of the macroeconomy, we find that the interest rate surprise factor serves as a robust determinant of CDS spread gyrations in both the full sample and most subsamples organized by industry type and credit rating status. Furthermore, we empirically find that the swap interest rate variables convey material information about CDS spread movements above and beyond the Treasury interest rate variables in the vast majority of 2SLS regressions. These empirical results have important implications for the parameterization of interest rate dynamics in the Monte Carlo simulation of economic capital for a typical bank's credit portfolio.
{"title":"Credit Default Swap Spreads, Fair Value Spreads, and Interest Rate Dynamics","authors":"A. Yeh","doi":"10.21314/JCR.2012.154","DOIUrl":"https://doi.org/10.21314/JCR.2012.154","url":null,"abstract":"This paper examines the empirical relationship between credit risk and interest rate risk. We use the credit default swap (CDS) spread as our measure of credit risk. Also, we control for the variation in the fair-value spread that combines multiple sources of default risk, including the market price of risk (Sharpe ratio), the loss given default (LGD), and the expected default frequency (EDF). After taking into account the fair-value spread, a liquidity risk factor, and several proxies for the general state of the macroeconomy, we find that the interest rate surprise factor serves as a robust determinant of CDS spread gyrations in both the full sample and most subsamples organized by industry type and credit rating status. Furthermore, we empirically find that the swap interest rate variables convey material information about CDS spread movements above and beyond the Treasury interest rate variables in the vast majority of 2SLS regressions. These empirical results have important implications for the parameterization of interest rate dynamics in the Monte Carlo simulation of economic capital for a typical bank's credit portfolio.","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"77 1","pages":"53-129"},"PeriodicalIF":0.3,"publicationDate":"2012-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79772222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paperanalyzes the impact of several popular factor models on the calculation of value-at-risk (VaR) for the loss of a credit portfolio with many obligors. The study covers linear and nonlinear factor models focusing on the importance of tail dependence. The financial crisis, which was an example of an extreme tail event, showed the need for models other than the Gaussian model. We show that, even when controlling for correlation and fat marginal tails among models, the tail dependence has an important impact on VaR and asset allocation. We use the central limit theorem to approximate the loss distribution conditional on the common factors. The efficient frontier and portfolio allocation are provided by optimizing a portfolio of corporate loans. We give evidence that the Gaussian factor model can lead to portfolios with a misleading optimal risk–return trade- off because it does not capture extreme events adequately.
{"title":"Impact of Factor Models on Portfolio Risk Measures: A Structural Approach","authors":"M. Escobar, Tobias Frielingsdorf, R. Zagst","doi":"10.21314/JCR.2012.142","DOIUrl":"https://doi.org/10.21314/JCR.2012.142","url":null,"abstract":"This paperanalyzes the impact of several popular factor models on the calculation of value-at-risk (VaR) for the loss of a credit portfolio with many obligors. The study covers linear and nonlinear factor models focusing on the importance of tail dependence. The financial crisis, which was an example of an extreme tail event, showed the need for models other than the Gaussian model. We show that, even when controlling for correlation and fat marginal tails among models, the tail dependence has an important impact on VaR and asset allocation. We use the central limit theorem to approximate the loss distribution conditional on the common factors. The efficient frontier and portfolio allocation are provided by optimizing a portfolio of corporate loans. We give evidence that the Gaussian factor model can lead to portfolios with a misleading optimal risk–return trade- off because it does not capture extreme events adequately.","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"1 1","pages":"47-79"},"PeriodicalIF":0.3,"publicationDate":"2012-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82401565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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