Estimating correlation parameters in credit portfolio models under time-varying and nonhomogeneous default probabilities
This paper proposes new maximum likelihood estimation methods that offer greater flexibility than current methods and can account for finite portfolio sizes, scarce default data and time varying, nonhomogeneous default probabilities.
The authors investigate the borrower risk factors, delinquency rates, yield curves, and interest rates of long-term auto loans.
The authors put forward a systemic risk measurement model and measure systemic risk in China's banking sector for the period 2013-18.
Quantification of model risk with an application to probability of default estimation and stress testing for a large corporate portfolio
This paper discusses the building of obligor-level rather than segment-level hazard rate corporate probability of default models for stress testing.
The authors propose a new method to design credit risk rating models for corporate entities using a meta-algorithm which exploits information embedded in expert-assigned credit ratings to rank customers.
By adding a correlated risk driver to Merton's model for corporate bond pricing, the authors model the empirically observed recovery risk premium.
The authors propose a general structural default model combining enhanced economic relevance and affordable computational complexity.
The author presents a new, computationally simple framework for quantifying and detecting changes in established companies' corporate credit quality.
In this paper the authors propose a framework for granular-level stressed net interest income calculation and profit-and-loss risk attribution.
This paper proposes a credit risk model based on purchase order information to address the deficiencies of monitoring methods that use only financial statements.
This study explores banks’ internal credit risk estimates and the associated banksourced transition matrixes.
Credit rating and collateral value's changes have a measurable impact on creditworthiness
This paper surveys the impressively broad range of machine learning methods and application areas for credit risk.
News feeds are factored into models to predict credit events
Review of credit risk and credit scoring models based on computing paradigms in financial institutions
This paper provides an overview of some prominent credit scoring models used in financial institutions and provides an insight into how the use and integration of popular computing paradigms based on NNs, machine learning, game theory and BDA in credit…
In this paper, a structural model for credit rating migration is developed and validated, by which the migration boundary is recovered for the first time.
Small and medium-sized enterprises that borrow from "alternative" lenders in the United Kingdom: who are they?
This study provides a general overview of the external financing landscape for the UK SMEs and an exploratory analysis of the SME portfolio of one of the alternative lenders in the United Kingdom.
The authors introduce a simple numerical algorithm to study banking systems subject to credit risk. The algorithm is based on a model that is completely defined by only two parameters.
HVA is framed consistently with other valuation adjustments
A stochastic time change helps the modelling of rating transition
Research on listed companies’ credit ratings, considering classification performance and interpretability
This study uses the correlation coefficient and F-test to select the initial features of a credit evaluation system, and then a validity index for a second selection to ensure that the feature system has the optimum ability to discriminate in determining…
Beyond the contract: client behavior from origination to default as the new set of the loss given default risk drivers
In this paper, we expand the modeling process by constructing a set of client-behavior-based predictors that can be used to construct more precise models, and we investigate the economic justifications empirically to examine their potential usage.
This paper examines which hybridization strategy is more suitable for credit risk assessment in the dynamic financial world.