A General Framework for Incorporating Stochastic Recovery in Structural Models of Credit Risk PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download A General Framework for Incorporating Stochastic Recovery in Structural Models of Credit Risk PDF full book. Access full book title A General Framework for Incorporating Stochastic Recovery in Structural Models of Credit Risk by Albert Cohen. Download full books in PDF and EPUB format.
Author: Albert Cohen
Publisher:
ISBN:
Category :
Languages : en
Pages : 19
Get Book Here
Book Description
In this work, we introduce a general framework for incorporating stochastic recovery into structural models. The framework extends the approach to recovery modeling developed in Cohen and Costanzino (2015, 2017) and provides for a systematic way to include different recovery processes into a structural credit model. The key observation is a connection between the partial information gap between firm manager and the market that is captured via a distortion of the probability of default. This last feature is computed by what is essentially a Girsanov transformation and reflects untangling of the recovery process from the default probability. Our framework can be thought of as an extension of Ishizaka and Takaoka (2003) and, in the same spirit of their work, we provide several examples of the framework including bounded recovery and a jump-to-zero model. One of the nice features of our framework is that, given prices from any one-factor structural model, we provide a systematic way to compute corresponding prices with stochastic recovery. The framework also provides a way to analyze correlation between Probability of Default (PD) and Loss Given Default (LGD), and term structure of recovery rates.
Author: Albert Cohen
Publisher:
ISBN:
Category :
Languages : en
Pages : 19
Get Book Here
Book Description
In this work, we introduce a general framework for incorporating stochastic recovery into structural models. The framework extends the approach to recovery modeling developed in Cohen and Costanzino (2015, 2017) and provides for a systematic way to include different recovery processes into a structural credit model. The key observation is a connection between the partial information gap between firm manager and the market that is captured via a distortion of the probability of default. This last feature is computed by what is essentially a Girsanov transformation and reflects untangling of the recovery process from the default probability. Our framework can be thought of as an extension of Ishizaka and Takaoka (2003) and, in the same spirit of their work, we provide several examples of the framework including bounded recovery and a jump-to-zero model. One of the nice features of our framework is that, given prices from any one-factor structural model, we provide a systematic way to compute corresponding prices with stochastic recovery. The framework also provides a way to analyze correlation between Probability of Default (PD) and Loss Given Default (LGD), and term structure of recovery rates.
Author: Jorge L. Hernández
Publisher:
ISBN:
Category :
Languages : en
Pages : 166
Get Book Here
Book Description
Author: Mads Gjedsted Nielsen
Publisher: LAP Lambert Academic Publishing
ISBN: 9783844306118
Category :
Languages : en
Pages : 120
Get Book Here
Book Description
Three different credit risk models are presented, implemented, and calibrated to real data. Each of which presents a different way to model the dynamics of a firm. To better examine their differences, the models are benchmarked against the much celebrated Merton's model. Generally it is shown that structural credit risk models have empirical validity. However, all is not perfect. Since structural credit risk models may have two objectives. One being to accurately predict credit spreads, and another to determine the optimal capital structure. It is argued that if the goal is the former, then future structural models need to incorporate a more exible framework that can price the many di erent types of bonds that make up a company s debt simultaneously. However, if the objective is the latter, then the future models need to better account for the high costs linked with capital restructures in times of nancial distress.
Author: Michael C. Fu
Publisher: Springer Science & Business Media
ISBN: 0817645454
Category : Business & Economics
Languages : en
Pages : 345
Get Book Here
Book Description
This self-contained volume brings together a collection of chapters by some of the most distinguished researchers and practitioners in the field of mathematical finance and financial engineering. Presenting state-of-the-art developments in theory and practice, the book has real-world applications to fixed income models, credit risk models, CDO pricing, tax rebates, tax arbitrage, and tax equilibrium. It is a valuable resource for graduate students, researchers, and practitioners in mathematical finance and financial engineering.
Author: Raquel M. Gaspar
Publisher:
ISBN:
Category :
Languages : en
Pages : 44
Get Book Here
Book Description
There has been increasing support in the empirical literature that both the probability of default (PD) and the loss given default (LGD) are correlated and driven by macroeconomic variables. Paradoxically, there has been very little effort from the theoretical literature to develop credit risk models that would include this possibility.The goals of this paper are: first, to develop the theoretical reduced-form framework needed to handle stochastic correlation of recovery and intensity, proposing a new class of models; second, to understand under what conditions would our class of models reflect empirically observed features and, finally, to use concrete model from our class to study the impact of this correlation in credit risk term structures.We show that, in our class of models, it is possible to model directly empirically observed features. For instance, we can define default intensity and losses given default to be higher during economic depression periods - the well-know credit risk business cycle effect. Using the concrete model we show that in reduced-form models different assumptions - concerning default intensities, distribution of losses given default, and specifically their correlation - have a significant impact on the shape of credit spread term structures, and consequently on pricing of credit products as well as credit risk assessment in general.Finally, we propose a way to calibrate this class of models to market data, and illustrate the technique using our concrete example using US market data on corporate yields.
Author: Frédéric Vrins
Publisher: MDPI
ISBN: 3039287605
Category : Business & Economics
Languages : en
Pages : 190
Get Book Here
Book Description
Credit risk remains one of the major risks faced by most financial and credit institutions. It is deeply connected to the real economy due to the systemic nature of some banks, but also because well-managed lending facilities are key for wealth creation and technological innovation. This book is a collection of innovative papers in the field of credit risk management. Besides the probability of default (PD), the major driver of credit risk is the loss given default (LGD). In spite of its central importance, LGD modeling remains largely unexplored in the academic literature. This book proposes three contributions in the field. Ye & Bellotti exploit a large private dataset featuring non-performing loans to design a beta mixture model. Their model can be used to improve recovery rate forecasts and, therefore, to enhance capital requirement mechanisms. François uses instead the price of defaultable instruments to infer the determinants of market-implied recovery rates and finds that macroeconomic and long-term issuer specific factors are the main determinants of market-implied LGDs. Cheng & Cirillo address the problem of modeling the dependency between PD and LGD using an original, urn-based statistical model. Fadina & Schmidt propose an improvement of intensity-based default models by accounting for ambiguity around both the intensity process and the recovery rate. Another topic deserving more attention is trade credit, which consists of the supplier providing credit facilities to his customers. Whereas this is likely to stimulate exchanges in general, it also magnifies credit risk. This is a difficult problem that remains largely unexplored. Kanapickiene & Spicas propose a simple but yet practical model to assess trade credit risk associated with SMEs and microenterprises operating in Lithuania. Another topical area in credit risk is counterparty risk and all other adjustments (such as liquidity and capital adjustments), known as XVA. Chataignier & Crépey propose a genetic algorithm to compress CVA and to obtain affordable incremental figures. Anagnostou & Kandhai introduce a hidden Markov model to simulate exchange rate scenarios for counterparty risk. Eventually, Boursicot et al. analyzes CoCo bonds, and find that they reduce the total cost of debt, which is positive for shareholders. In a nutshell, all the featured papers contribute to shedding light on various aspects of credit risk management that have, so far, largely remained unexplored.
Author: Rajiv Guha
Publisher:
ISBN:
Category :
Languages : en
Pages : 75
Get Book Here
Book Description
Receiving the same fractional recovery of par at default for bonds issued by a particular company regardless of maturity has been labelled in the academic literature as a Recovery of Face Value at Default (RFV). Such a recovery form results from language found in typical bond indentures and is supported by empirical evidence from defaulted bond values. We incorporate RFV into a exogenous boundary structural credit risk model and compare the pricing and hedging implications versus otherrecovery forms more often seen in such models. We find that the recovery form can significantly affect the pricing and sensitivities produced by these models. This has important implications for any practical use of these models as well as for empirical studies attempting to validate structural credit risk models. In general, we provide convincing evidence that choosing the recovery form is not a trivial assumption tomake within this class of models. Some of our results complement those found in the literature which examines the endogeneity of the default boundary. We show that some features that may have been solely attributed to modelling the boundary as anoptimal decision by the firm can obtain in an exogenous boundary framework which assumes an RFV recovery form. We extend our results to incorporate a multifactor default-free term structure model and examine the impact of the recovery form in estimating the cost of debt capital within a structural model framework.
Author: Sandrine Gümbel
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Abstract: This thesis is devoted to the study of term structure modeling in interest rate markets and defaultable term structure modeling in credit risk markets. Post-crisis interest rate markets possess two main characteristics: multiple curves and discontinuities. While a lot of effort has been put in the study of the former, there is one crucial feature of discontinuities, which we will call stochastic. discontinuities, whose investigation seems to be lacking in the interest rate literature so far. This concept of discontinuities has recently been studied in a credit risk framework in Fontana and Schmidt (2018) and Gehmlich and Schmidt (2018). Stochastic discontinuities describe jumps in the underlying interest rates or processes depicting events occurring at announced dates but with a possibly unanticipated outcome. This type of events is clearly present in interest rates, as can be evidenced by jumps in the underlying rates in correspondence with meetings of the European Central Bank. We provide a general analysis of the term structure modeling of multiple curves with the presence of stochastic discontinuities and derive conditions to ensure absence of arbitrage. In particular, we provide an extended Heath-Jarrow-Morton formulation with semimartingales as driving processes. Beyond that, a general market model approach is investigated and some insightful examples in an affine framework are presented in order to show the potential of this approach. Bond prices are calibrated in a Vasi cek framework by means of machine learning techniques adapted to Gaussian processes. In credit risk we are concerned with securities that are subject to default risk. We present a general analysis of the term structure modeling of defaultable bonds allowing for discontinuities. In particular, we derive conditions to ensure absence of arbitrage in the credit risky financial market in an extended Heath-Jarrow-Morton framework with semimartingales as driving processes. We provide a similar characterization for defaultable bonds with recovery.
Author: Jing-zhi Huang
Publisher:
ISBN:
Category :
Languages : en
Pages : 56
Get Book Here
Book Description
Author: Navneet Arora
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
In this paper, we empirically compare two structural models (basic Merton and Vasicek-Kealhofer (VK)) and one reduced-form model (Hull-White (HW)) of credit risk. We propose here that two useful purposes for credit models are default discrimination and relative value analysis. We test the ability of the Merton and VK models to discriminate defaulters from non-defaulters based on default probabilities generated from information in the equity market. We test the ability of the HW model to discriminate defaulters from non-defaulters based on default probabilities generated from information in the bond market. We find the VK and the HW models exhibit comparable accuracy ratios as well as substantially outperform the simple Merton model. We also test the ability of each model to predict spreads in the credit default swap (CDS) market as an indication of each model's strength as a relative value analysis tool. We find the VK model tends to do the best across the full sample and relative sub-samples except for cases where an issuer has many bonds in the market. In this case, the HW model tends to do the best. The empirical evidence will assist market participants in determining which model is most useful based on their purpose in hand. On the structural side, a basic Merton model is not good enough; appropriate modifications to the framework make a difference. On the reduced-form side, the quality and quantity of data make a difference; many traded issuers will not be well modeled in this way unless they issue more traded debt. In addition, bond spreads at shorter tenors (less than two years) tend to be less correlated with CDS spreads. This makes accurate calibration of the term-structure of credit risk difficult from bond data.
