Bayesian Machine Learning in Quantitative Finance

Theory and Practical Applications

Wilson Tsakane Mongwe, Rendani Mbuvha, Tshilidzi Marwala

• Publisher: Palgrave Macmillan
• Year: 2025
• Print ISBN: 9783031884306
• eBook ISBN: 9783031884313
• Topic: Business
• Subtopic: Corporate Finance
• Index: Business

This book offers a comprehensive discussion of the Bayesian inference framework and demonstrates why this probabilistic approach is ideal for tackling the various modelling problems within quantitative finance. It demonstrates how advanced Bayesian machine learning techniques can be applied within financial engineering, investment portfolio management, insurance, municipal finance management as well as banking.

The book covers a broad range of modelling approaches, including Bayesian neural networks, Gaussian processes and Markov Chain Monte Carlo methods. It also discusses the utility of Bayesian inference in quantitative finance and discusses future research goals in the applications of Bayesian machine learning in quantitative finance. Chapters are rooted in the theory of quantitative finance and machine learning while also outlining a range of practical considerations for implementing Bayesian techniques into real-world quantitative finance problems. This book is ideal for graduate researchers and practitioners at the intersection of machine learning and quantitative finance, as well as those working in computational statistics and computer science more broadly.

Table of contents

• Cover
• Front Matter
• 1. Introduction to Bayesian Machine Learning in Quantitative Finance
• 2. Background to Bayesian Machine Learning in Quantitative Finance
• 3. On the Stochastic Alpha Beta Rho Model and Hamiltonian Monte Carlo Techniques
• 4. Learning Equity Volatility Surfaces Using Sparse Gaussian Processes
• 5. Analyzing South African Equity Option Prices Using Normalizing Flows
• 6. Sparse and Distributed Gaussian Processes for Modeling Corporate Credit Ratings
• 7. Bayesian Detection of Recovery on Charged-Off Loan Accounts
• 8. Bayesian Audit Outcome Model Selection Using Normalizing Flows
• 9. Bayesian Detection of Unauthorized Expenditure Using Langevin and Hamiltonian Monte Carlo
• 10. Bayesian Neural Network Inference of Motor Insurance Claims
• 11. Shadow and Adaptive Hamiltonian Monte Carlo Methods for Calibrating the Nelson and Siegel Model
• 12. Static and Dynamic Nested Sampling for Yield Curve Model Selection
• 13. A Bayesian Investment Analyst on the Johannesburg Stock Exchange
• 14. Conclusions to Bayesian Machine Learning in Quantitative Finance
• Back Matter