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Research Paper | Risk Management | United States of America | Volume 12 Issue 12, December 2023 | Rating: 4.4 / 10
Advanced Credit Risk Assessment Using Markov Chain Monte Carlo Techniques
Abstract: This paper explores the application of Markov Chain Monte Carlo (MCMC) methods in credit risk assessment, highlighting how MCMC enhances the predictive accuracy of default probabilities (PDs) and evaluation of systemic risk in interconnected financial networks. We discuss the integration of Bayesian inference with MCMC techniques to estimate posterior distributions for PDs, focusing on correlated defaults and systemic risks. The paper also investigates the fusion of network theory with credit risk analysis to provide a holistic view of financial stability. Empirical case studies are used to validate the effectiveness of MCMC in real-world scenarios, followed by best practices for implementation and an introduction to advanced MCMC algorithms. We conclude with a comparative analysis of MCMC against other credit risk methodologies and outline future research directions.
Keywords: Markov Chain Monte Carlo (MCMC), Credit Risk, Bayesian Inference, Systemic Risk, Probability of Default (PD)
Edition: Volume 12 Issue 12, December 2023,
Pages: 2160 - 2163