New Definition of Default—Recalibration of Credit Risk Models Using Bayesian Approach

After the financial crisis, the European Banking Authority (EBA) has established tighter standards around the definition of default (Capital Requirements Regulation CRR Article 178, EBA/GL/2017/16) to increase the degree of comparability and consistency in credit risk measurement and capital frameworks across banks and financial institutions. Requirements of the new definition of default (DoD) concern how banks recognize credit defaults for prudential purposes and include quantitative impact analysis and new rules of materiality. In this approach, the number and timing of defaults affect the validity of currently used risk models and processes. The recommendation presented in this paper is to address current gaps by considering a Bayesian approach for PD recalibration based on insights derived from both simulated and empirical data (e.g., a priori and a posteriori distributions). A Bayesian approach was used in two steps: to calculate the Long Run Average (LRA) on both simulated and empirical data and for the final model calibration to the posterior LRA. The Bayesian approach result for the PD LRA was slightly lower than the one calculated based on classical logistic regression. It also decreased for the historically observed LRA that included the most recent empirical data. The Bayesian methodology was used to make the LRA more objective, but it also helps to better align the LRA not only with the empirical data but also with the most recent ones.

Shortfall Risk Models When Information on Loss Function Is Incomplete

Utility-based shortfall risk measures effectively captures a decision maker's risk attitude on tail losses. In this paper, we consider a situation where the decision maker's risk attitude toward tail losses is ambiguous and introduce a robust version of shortfall risk, which mitigates the risk arising from such ambiguity. Specifically, we use some available partial information or subjective judgement to construct a set of plausible utility-based shortfall risk measures and define a so-called preference robust shortfall risk as through the worst risk that can be measured in this (ambiguity) set. We then apply the robust shortfall risk paradigm to optimal decision-making problems and demonstrate how the latter can be reformulated as tractable convex programs when the underlying exogenous uncertainty is discretely distributed.

Cardiac device implantation and device usage in Fabry and hypertrophic cardiomyopathy

Background Fabry disease (FD) is a treatable X-linked condition leading to progressive cardiac disease, arrhythmia and premature death. We aimed to increase awareness of the arrhythmogenicity of Fabry cardiomyopathy, by comparing device usage in patients with Fabry cardiomyopathy and sarcomeric HCM. All Fabry patients with an implantable cardioverter defibrillator (ICD) implanted in the UK over a 17 year period were included. A comparator group of HCM patients, with primary prevention ICD implantation, were captured from a regional registry database. Results Indications for ICD in FD varied with 72% implanted for primary prevention based on multiple potential risk factors. In FD and HCM primary prevention devices, arrhythmia occurred more frequently in FD over shorter follow-up (HR 4.2, p < 0.001). VT requiring therapy was more common in FD (HR 4.5, p = 0.002). Immediate shock therapy for sustained VT was also more common (HR 2.5, p < 0.001). There was a greater burden of AF needing anticoagulation and NSVT in FD (AF: HR 6.2, p = 0.004, NSVT: HR 3.1, p < 0.001). Conclusion This study demonstrates arrhythmia burden and ICD usage in FD is high, suggesting that Fabry cardiomyopathy may be more ‘arrhythmogenic’ than previously thought. Existing risk models cannot be mutually applicable and further research is needed to provide clarity in managing Fabry patients with cardiac involvement.

A Smoothed Version of the Lassosum Penalty for Fitting Integrated Risk Models Using Summary Statistics or Individual-Level Data

Polygenic risk scores are a popular means to predict the disease risk or disease susceptibility of an individual based on its genotype information. When adding other important epidemiological covariates such as age or sex, we speak of an integrated risk model. Methodological advances for fitting more accurate integrated risk models are of immediate importance to improve the precision of risk prediction, thereby potentially identifying patients at high risk early on when they are still able to benefit from preventive steps/interventions targeted at increasing their odds of survival, or at reducing their chance of getting a disease in the first place. This article proposes a smoothed version of the “Lassosum” penalty used to fit polygenic risk scores and integrated risk models using either summary statistics or raw data. The smoothing allows one to obtain explicit gradients everywhere for efficient minimization of the Lassosum objective function while guaranteeing bounds on the accuracy of the fit. An experimental section on both Alzheimer’s disease and COPD (chronic obstructive pulmonary disease) demonstrates the increased accuracy of the proposed smoothed Lassosum penalty compared to the original Lassosum algorithm (for the datasets under consideration), allowing it to draw equal with state-of-the-art methodology such as LDpred2 when evaluated via the AUC (area under the ROC curve) metric.

Applying appropriate models to predict bankruptcy for Vietnamese listed construction companies

This study focuses on assessing the suitability and condition of various bankruptcy risk models applied to construction companies listed on the Vietnam Stock Market. In this study, the panel data were collected from the disclosed financial statements of the companies from 2012 to 2017. Through the assessment, bankruptcy risks are predicted for the companies that are experiencing initial signals such as delisting, compulsory supervision. In the next step, interviews were conducted to justify which of the following factors may indicate the companies at the risk of being bankrupted: asset management, capital structure, business size, and/or state management.