Credit risk

About: Credit risk is a research topic. Over the lifetime, 18595 publications have been published within this topic receiving 382866 citations.

Credit Risk Analysis Using Quantum Computers

Abstract: We present and analyze a quantum algorithm to estimate credit risk more efficiently than Monte Carlo simulations can do on classical computers. More precisely, we estimate the economic capital requirement, i.e. the difference between the Value at Risk and the expected value of a given loss distribution. The economic capital requirement is an important risk metric because it summarizes the amount of capital required to remain solvent at a given confidence level. We implement this problem for a realistic loss distribution and analyze its scaling to a realistic problem size. In particular, we provide estimates of the total number of required qubits, the expected circuit depth, and how this translates into an expected runtime under reasonable assumptions on future fault-tolerant quantum hardware.

Multivariate Survival Modelling: A Unified Approach with Copulas

Abstract: In this paper, we review the use of copulas for multivariate survival modelling. In particular, we study properties of survival copulas and discuss the dependence measures associated to this construction. Then, we consider the problem of competing risks. We derive the distribution of the failure time and order statistics. After having presented statistical inference, we finally provide financial applications which concern the life time value (attrition models), the link between default, prepayment and credit life, the measure of risk for a credit portfolio and the pricing of credit derivatives.

Household Need for Liquidity and the Credit Card Debt Puzzle

Abstract: In the 2001 U.S. Survey of Consumer Finances (SCF), 27% of households report simultaneously revolving signicant credit card debt and holding sizeable amounts of low-return liquid assets; this is known as the \credit card debt puzzle". In this paper, I quantitatively evaluate the role of liquidity demand in accounting for this puzzle: households that accumulate credit card debt may not pay it o using their money in the bank, because they anticipate needing that money in situations where credit cards cannot be used. I characterize the puzzle in survey data, and calibrate a dynamic stochastic heterogeneous-agent model of household portfolio choice, where consumer credit and liquidity coexist as means of consumption and saving, where households consume a cash good and a credit good, and where cash consumption is subject to uncertainty. The model accounts for between 44% and 56% of the households in the data who hold consumer debt and liquidity simultaneously, and for 100% of the liquidity held by a median such household. Under reasonable calibration alternatives, the model can capture the entire puzzle group size as well. One-half of money demand in the model is precautionary.

Comparison of individual, ensemble and integrated ensemble machine learning methods to predict China’s SME credit risk in supply chain finance

Abstract: Supply chain finance (SCF) becomes more important for small- and medium-sized enterprises (SMEs) due to global credit crunch, supply chain financing woes and tightening credit criteria for corporate lending. Currently, predicting SME credit risk is significant for guaranteeing SCF in smooth operation. In this paper, we apply six methods, i.e., one individual machine learning (IML, i.e., decision tree) method, three ensemble machine learning methods [EML, i.e., bagging, boosting, and random subspace (RS)], and two integrated ensemble machine learning methods (IEML, i.e., RS–boosting and multi-boosting), to predict SMEs credit risk in SCF and compare the effectiveness and feasibility of six methods. In the experiment, we choose the quarterly financial and non-financial data of 48 listed SMEs from Small and Medium Enterprise Board of Shenzhen Stock Exchange, six listed core enterprises (CEs) from Shanghai Stock Exchange and three listed CEs from Shenzhen Stock Exchange during the period of 2012–2013 as the empirical samples. Experimental results reveal that the IEML methods acquire better performance than IML and EML method. In particular, RS–boosting is the best method to predict SMEs credit risk among six methods.