Applications of Bayesian Inference in Financial Econometrics: A Review
DOI:
https://doi.org/10.71222/mz71ts21Keywords:
Bayesian Inference, financial econometrics, risk management, time series analysisAbstract
This review provides a comprehensive review of the applications of Bayesian inference in financial econometrics. It explores fundamental Bayesian methods, such as Bayes' Theorem, Markov Chain Monte Carlo (MCMC), and Variational Inference, and discusses their use in financial modeling, including asset pricing, risk management, and portfolio optimization. The paper also highlights recent advancements such as Hamiltonian Monte Carlo and Bayesian Neural Networks, which have enhanced the computational efficiency of Bayesian techniques. Despite these advancements, challenges related to computational complexity, prior selection, and high-dimensional data persist. The paper concludes by suggesting future research directions, focusing on improving algorithms and developing more data-driven approaches.
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