Bayesian Structural Credit Risk Model with Microstructure Noise in Nigeria

Abstract

Financial markets rely on asset prices, which are often distorted by market frictions, liquidity constraints, and transaction costs, all of which influence a country’s structural credit risk. Traditional Markov Chain Monte Carlo (MCMC) estimation converges slowly and may not reliably capture rare, high-impact risks. To address this, the study develops a Bayesian structural credit risk model using Markov Chain Quasi-Monte Carlo (MCQMC) techniques, explicitly accounting for microstructure noise to improve the accuracy of asset value and default risk estimates in Nigeria. Comparative analysis shows that MCQMC achieves faster convergence, lower variance, and greater computational efficiency than MCMC, highlighting the benefits of noise-adjusted modeling for reliable credit risk assessment. The findings suggest that financial institutions should adopt MCQMC methods, while policymakers may consider incorporating noise-aware credit risk models into regulatory frameworks, offering a more robust and efficient approach to credit risk management in Nigerian financial practice.