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Brian Barnard - One of the best experts on this subject based on the ideXlab platform.
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rating migration and bond valuation decomposing rating migration matrices from market data via Default Probability term structures
2017Co-Authors: Brian BarnardAbstract:The study builds on previous research that decomposes rating category Default Probability term structures from rating category interest rate term structures, and proposes a method to decompose rating migration matrices from market data, via decomposed Default Probability term structures. To investigate the power and accuracy of the proposed method, it was examined to what extent an existing, known rating migration matrix could again be surfaced by the method. Overall, the results are more than satisfactory, and the method promises to be accurate. Although not considered here, the main objective is the application of the method to market data. The outcome should be insightful in itself, and can be used to evaluate historical rating migration matrices commonly devised by rating agencies, and to form a better understanding of the Default Probability term structures embedded in market data.
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rating migration and bond valuation ahistorical interest rate and Default Probability term structures
2017Co-Authors: Brian BarnardAbstract:The study extends the theoretical framework proposed to decompose rating migration matrices from bond market price data. In this context, method to decompose Default Probability term structures for and from interest rate term structures for different rating categories, is also delineated and empirically evaluated. In principle, whenever it is possible to decompose an interest rate term structure for a rating category from ahistorical market data, it should also be possible to decompose a Default Probability term structure for the rating category, and to decompose a Default Probability term structure for the particular interest rate term structure of the rating category. Principal to the method and vantage point is that, when decomposing a Default Probability term structure, purely ahistorical market data is used, as is the case when decomposing interest rate term structures. In addition, greater emphasis is placed on surfacing actual market perceptions regarding Default Probability, which may differ from theoretical modelling of Default Probability. The method naturally allows a mapping and transitioning between interest rate term structures and Default Probability term structures. Consequentially, the study examines the corresponding interest rate term structures of the Default Probability term structures of a typical rating migration matrix, and the corresponding Default Probability term structures of a typical market interest rate term structure set. The sensitivity of the results to the coupon rate used is also examined, particularly in view of using an external (artificial) portfolio to facilitate the process. It is found that the Default Probability term structures decomposed from market interest rate term structures significantly differ from rating migration matrix based Default Probability term structures. This may point to differing views on Default Probability term structures. In addition, it is also shown that even external (artificial) portfolios can illuminate the Default Probability term structures of interest rate term structures with reasonable accuracy. Mapping to and fro interest rate term structures and Default Probability term structures introduces and additional level of triangulation and evaluation. It is expected that interest rate term structures should have valid and sensible Default Probability term structures, and vice versa. Another important implication of decomposing Default Probability term structures from ahistorical market data is that the Default Probability term structures would change at the frequency of the market and market data. This implies that market based Default Probability term structures may include a volatility component. Market based ahistorical Default Probability term structures offer a different and unique perspective on Default Probability term structures. If found to be an accurate representation of market views and perceptions, it may demand a reconsideration of the prominence of Default Probability as a bond valuation factor.
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rating migration and bond valuation ahistorical interest rate and Default Probability term structures
2017Co-Authors: Brian BarnardAbstract:The study extends the theoretical framework proposed to decompose rating migration matrices from bond market price data. Method to decompose Default Probability term structures for and from interest rate term structures for different rating categories, is delineated and empirically evaluated. Emphasis is squarely on using ahistorical (non-historical) market data, and utilizing actual market perceptions regarding Default probabilities. The method naturally allows a mapping and transitioning between interest rate term structures and Default Probability term structures. Mapping to and fro interest rate term structures and Default Probability term structures introduces an additional level of triangulation and evaluation. The study examines the corresponding interest rate term structures of the Default Probability term structures of a typical rating migration matrix, and the corresponding Default Probability term structures of a typical market interest rate term structure set. It is found that the Default Probability term structures decomposed from market interest rate term structures significantly differ from rating migration matrix based Default Probability term structures. This may point to differing views on Default Probability term structures.
Yang Hong - One of the best experts on this subject based on the ideXlab platform.
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predication of Default Probability of clients electricity charges arrears based on logistic regression model
2007Co-Authors: Yang HongAbstract:On the basis of analyzing the reason bringing on electricity charge arrears and utilizing the gettable data, the key variables impacting the charge arrears, which are needed in arrear risk recognition model, are designed; by means of logistic regression theory and method that can deal with bi-category problem, a model that can recognize the possibility extent of electric power clients’ charge arrears is established. Thereby, it is possible to predict the Default Probability according to the latest client data that the electric power enterprises could acquire, and the previous post-mortem management of charge arrears can be turned into a priori one, therefore the propose of preventing client arrears could be achieved.
Mungo Wilson - One of the best experts on this subject based on the ideXlab platform.
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credit ratings and credit risk is one measure enough
2017Co-Authors: Jens Hilscher, Mungo WilsonAbstract:This paper investigates the information in corporate credit ratings. If ratings are to be informative indicators of credit risk, they must reflect what a risk-averse investor cares about: both raw Default Probability and systematic risk. We find that ratings are relatively inaccurate measures of raw Default Probability—they are dominated as predictors of failure by a simple model based on publicly available financial information. However, ratings do contain relevant information since they are related to a measure of exposure to common (and undiversifiable) variation in Default Probability (“failure beta”). Systematic risk is shown to be related to joint Default probabilities in the context of the Merton [Merton RC (1974) On the pricing of corporate debt: The risk structure of interest rates. J. Finance 29(2):449–470] model. Empirically, it is related to credit Default swap spreads and risk premia. Given the multidimensional nature of credit risk, it is not possible for one measure to capture all the relev...
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credit ratings and credit risk is one measure enough
2015Co-Authors: Jens Hilscher, Mungo WilsonAbstract:This paper investigates the information in corporate credit ratings. If ratings are to be informative indicators of credit risk they must reflect what a risk-averse investor cares about: both raw Default Probability and systematic risk. We find that ratings are relatively inaccurate measures of raw Default Probability - they are dominated as predictors of failure by a simple model based on publicly available financial information. However, ratings do contain relevant information since they are related to a measure of exposure to common (and undiversifiable) variation in Default Probability ('failure beta'). Systematic risk is shown to be related to joint Default probabilities in the context of the Merton (1974) model. Empirically, it is related to CDS spreads and risk premia. Given the multidimensional nature of credit risk, it is not possible for one measure to capture all the relevant information.
Pocheng Chen - One of the best experts on this subject based on the ideXlab platform.
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a grey system theory based Default prediction model for construction firms
2015Co-Authors: Hui Ping Tserng, Pocheng Chen, Le Quyen TranAbstract:As the prediction of construction firm failure is of great importance for owners, contractors, investors, banks, insurance firms, and creditors, previous studies have developed several models for predicting the Probability of construction firm Default based on financial ratio analysis. However, to be applied, these models require a considerable quantity of data, including normally distributed data, and the models cannot tolerate too many changing factors. Furthermore, most of the approaches produce sample selection biases. To avoid these disadvantages, this study is the first to integrate the grey system theory with all available firm-year samples during the sample period to provide a new method for predicting the Probability of construction firm Default. This method not only offers an improved rate of prediction accuracy, but it also offers simpler and clearer procedures as a reference for examining firm Default Probability and ranks all financial ratios in terms of their level of importance. The research collects and analyzes the financial reports of 92 construction firms in the United States. The proposed model includes only eight ranked variables (financial ratios), and it achieves an 84.8% level of accuracy for predicting construction firm Default Probability. As a result, practitioners may directly use the model as a means of quickly and conveniently examining their firm Default Probability with the simple procedures.
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prediction of Default Probability for construction firms using the logit model
2014Co-Authors: Ping H Tserng, Pocheng Chen, Wenhaw Huang, Quang Hung TranAbstract:AbstractRecently, the high incidence of construction firm bankruptcies has underlined the importance of forecasting Defaults in the construction industry. Early warning systems need to be developed to prevent or avert contractor Default; additionally, this evaluation result could facilitate the selection of firms as collaboration or investment partners. Financial statements are considered one of the key basic evaluation tools for demonstrating firm strength. This investigation provides a framework for assessing the Probability of construction contractor Default based on financial ratios by using the Logit model. A total of 21 ratios, gathered into five financial groups, are utilized to perform univariate logit analysis and multivariate logit analysis for assessing contractor Default Probability. The empirical results indicate that using multivariate analysis by adding market factor to the liquidity, leverage, activity and profitability factors can increase the accuracy of Default prediction more than usin...
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integration of accounting based and option based models to predict construction contractor Default
2012Co-Authors: Lungken Tsai, Pocheng Chen, Hui Ping Tserng, Hsienhsing Liao, Wenpei WangAbstract:This paper aims to predict construction contractor Default, which is excluded by most extant studies, due to the distinct characteristics of construction industry. Default predicting models developed in past literatures are mostly built by accounting information, yet accounting sheets have innate flaws. To calculate Default Probability, several recent studies applied the option pricing theory, which presumes that the stock market is efficient. This presumption isn't always true in real life. In this paper, a hybrid model is proposed. It combines information from both models by inputting the Default Probability from the option-based model into the accounting-based model. As the measure of models' predicting performance, the Area Under the receiver operating characteristic Curve (AUC) is used. Empirical results show that the hybrid model (AUC: 0.8732) outperforms both the accounting-based model (AUC: 0.7519) and the option-based model (AUC: 0.8581). This result shows that accounting or stock market information alone is not sufficient to explain real-world behavior. It is suggested that the hybrid model be used as an alternative prediction model of construction contractor Default.
Jens Hilscher - One of the best experts on this subject based on the ideXlab platform.
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credit ratings and credit risk is one measure enough
2017Co-Authors: Jens Hilscher, Mungo WilsonAbstract:This paper investigates the information in corporate credit ratings. If ratings are to be informative indicators of credit risk, they must reflect what a risk-averse investor cares about: both raw Default Probability and systematic risk. We find that ratings are relatively inaccurate measures of raw Default Probability—they are dominated as predictors of failure by a simple model based on publicly available financial information. However, ratings do contain relevant information since they are related to a measure of exposure to common (and undiversifiable) variation in Default Probability (“failure beta”). Systematic risk is shown to be related to joint Default probabilities in the context of the Merton [Merton RC (1974) On the pricing of corporate debt: The risk structure of interest rates. J. Finance 29(2):449–470] model. Empirically, it is related to credit Default swap spreads and risk premia. Given the multidimensional nature of credit risk, it is not possible for one measure to capture all the relev...
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credit ratings and credit risk is one measure enough
2015Co-Authors: Jens Hilscher, Mungo WilsonAbstract:This paper investigates the information in corporate credit ratings. If ratings are to be informative indicators of credit risk they must reflect what a risk-averse investor cares about: both raw Default Probability and systematic risk. We find that ratings are relatively inaccurate measures of raw Default Probability - they are dominated as predictors of failure by a simple model based on publicly available financial information. However, ratings do contain relevant information since they are related to a measure of exposure to common (and undiversifiable) variation in Default Probability ('failure beta'). Systematic risk is shown to be related to joint Default probabilities in the context of the Merton (1974) model. Empirically, it is related to CDS spreads and risk premia. Given the multidimensional nature of credit risk, it is not possible for one measure to capture all the relevant information.
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bank stability and market discipline the effect of contingent capital on risk taking and Default Probability
2014Co-Authors: Jens Hilscher, Alon RavivAbstract:Abstract This paper investigates the effects of financial institutions issuing contingent capital, a debt security that automatically converts into equity if assets fall below a predetermined threshold. We decompose bank liabilities into sets of barrier options and present closed-form solutions for their prices. We quantify the reduction in Default Probability associated with issuing contingent capital instead of subordinated debt. We then show that appropriate choice of contingent capital terms (in particular the conversion ratio) can virtually eliminate stockholders' incentives to risk-shift, a motivation that is present when bank liabilities instead include either subordinated debt or additional equity. Importantly, risk-taking incentives continue to be weak during times of financial distress. Our findings imply that contingent capital may be an effective tool for stabilizing financial institutions.
