Copula-based factor model for credit risk analysis

Lu, Meng-Jou; Chen, Cathy Yi‐Hsuan; Härdle, Wolfgang Karl

doi:10.1007/s11156-016-0613-x

Cited by 8 publications

(4 citation statements)

References 48 publications

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“…Finally, future research may include extending the proposed methodology to include dependent attributes using copula models. An example of the use of copulas in the context of credit risk can be found in [19].…”

Section: Discussionmentioning

confidence: 99%

A Proposed Simulation Technique for Population Stability Testing in Credit Risk Scorecards

2023

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Credit risk scorecards are logistic regression models, fitted to large and complex data sets, employed by the financial industry to model the probability of default of potential customers. In order to ensure that a scorecard remains a representative model of the population, one tests the hypothesis of population stability; specifying that the distribution of customers’ attributes remains constant over time. Simulating realistic data sets for this purpose is nontrivial, as these data sets are multivariate and contain intricate dependencies. The simulation of these data sets are of practical interest for both practitioners and for researchers; practitioners may wish to consider the effect that a specified change in the properties of the data has on the scorecard and its usefulness from a business perspective, while researchers may wish to test a newly developed technique in credit scoring. We propose a simulation technique based on the specification of bad ratios, this is explained below. Practitioners can generally not be expected to provide realistic parameter values for a scorecard; these models are simply too complex and contain too many parameters to make such a specification viable. However, practitioners can often confidently specify the bad ratio associated with two different levels of a specific attribute. That is, practitioners are often comfortable with making statements such as “on average a new customer is 1.5 times as likely to default as an existing customer with similar attributes”. We propose a method which can be used to obtain parameter values for a scorecard based on specified bad ratios. The proposed technique is demonstrated using a realistic example, and we show that the simulated data sets adhere closely to the specified bad ratios. The paper provides a link to a Github project with the R code used to generate the results.

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Section: Discussionmentioning

confidence: 99%

A Proposed Simulation Technique for Population Stability Testing in Credit Risk Scorecards

2023

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“…The copula functions are very useful, especially when the normality assumption does not hold. Modeling the dependence structure between random variables using the copula method in finance is relatively new (Breymann et al, 2003;Silva et al, 2014;Lu et al, 2017;Siami-Namini et al, 2019;and Zhang and Jiang, 2019). Doman and Doman (2012) studied the dependence structure of stock markets during the pre-and post-crisis periods by applying Markov-switching copula model.…”

Section: Literature Reviewmentioning

confidence: 99%

Stock Returns and Roughness Extreme Variations: A New Model for Monitoring 2008 Market Crash and 2015 Flash Crash

Shirvani¹

2020

AEF

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We use Student’s t-copula to study the extreme variations in the bivariate kinematic time series of log–return and log–roughness of the S&P 500 index during two market crashes, the financial crisis in 2008 and the flash crash on Monday August 24, 2015. The stable and small values of the tail dependence index observed for some months preceding the market crash of 2008 indicate that the joint distribution of daily return and roughness was close to a normal one. The volatility of the tail and degree of freedom indices as determined by Student’s t-copula falls down substantially after the stock market crash of 2008. The number of degrees of freedom in the empirically observed distributions falls while the tail coefficient of the copula increases, indicating the long memory effect of the market crash of 2008. A significant change in the tail and degree of freedom indices associated with the intraday price of S&P 500 index is observed before, during, and after the flash crash on August 24, 2015. The long memory effect of the stock market flash crash of August 2015 is indicated by the number of degrees of freedom in the empirically observed distributions fall while the tail coefficient of the joint distribution increases after the flash crash. The small and stable value of degrees of freedom preceding the flash crash provides evidence that the joint distribution for intraday data of return and roughness is heavy-tailed. Time-varying long-range dependence in mean and volatility as well as the Chow and Bai-Perron tests indicate non-stability of the stock market in this period.

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“…In this context, the market size of quantitative investment funds has been increasing, and the total share of the overall financial market has been expanding [5]. At the same time, with the resurgence of AI technology in recent years, concepts such as "deep learning," "big data," and "data mining" have emerged, and various AI-based data processing technologies, financial forecasting models, and high-performance computers have also become more and more closely integrated with quantitative investment [6][7][8]. The concepts and body of knowledge related to it have gradually become the focus of the investment industry and scholars [9][10].…”

Section: Introductionmentioning

confidence: 99%

Fusion of multifactor modeling and supervised learning algorithms in quantitative finance: a comparative analysis of predictive and explanatory power

2024

Applied Mathematics and Nonlinear Sciences

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Finance serves as the lifeblood of the real economy’s development, fundamentally aimed at bolstering the real economy and mitigating financial risks. This paper investigates the application of machine learning technologies in finance, organizing a detailed classification and analysis of associated risks within the sector. Specifically, the study employs the Conditional Value at Risk (CoVaR) to quantify systemic financial risks. It introduces both the Fama-French three-factor and five-factor models for constructing multifactor financial models. It assesses the significance and regression coefficients of risk factors within these models to provide a comparative analysis. Moreover, this research develops an early warning model for financial risk by incorporating financial risk measurement indices into the XGBoost machine learning framework. It utilizes the SHAP (Shapley Additive exPlanations) explanatory framework to elucidate the key features influencing financial risk. This comprehensive approach not only enhances the understanding of financial risk dynamics but also advances the predictive capabilities of financial risk management. It was found that the modified R² increased from 36.52% to 56.25% after adding the IMU factor to the three-factor model, and the coefficient of SMB decreased from 0.135 to −0.215 the larger the size of the enterprise. The accuracy of the financial risk early warning model was 96.35%, and the probability of being predicted by the model to be a high-risk sample was lower when the value of the characteristic INDI was taken as [0,0.125]. Financial institutions can enhance their risk prevention ability by combining the financial risk factors obtained from the multifactor model with the supervised learning algorithm.

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Copula-based factor model for credit risk analysis

Cited by 8 publications

References 48 publications

A Proposed Simulation Technique for Population Stability Testing in Credit Risk Scorecards

A Proposed Simulation Technique for Population Stability Testing in Credit Risk Scorecards

Stock Returns and Roughness Extreme Variations: A New Model for Monitoring 2008 Market Crash and 2015 Flash Crash

Fusion of multifactor modeling and supervised learning algorithms in quantitative finance: a comparative analysis of predictive and explanatory power

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