A spin model is used for simulations of financial markets. To determine return volatility in the spin financial market we use the GARCH model often used for volatility estimation in empirical finance. We apply the Bayesian inference performed by the Markov Chain Monte Carlo method to the parameter estimation of the GARCH model. It is found that volatility determined by the GARCH model exhibits "volatility clustering" also observed in the real financial markets. Using volatility determined by the GARCH model we examine the mixture-of-distribution hypothesis (MDH) suggested for the asset return dynamics. We find that the returns standardized by volatility are approximately standard normal random variables. Moreover we find that the absolute standardized returns show no significant autocorrelation. These findings are consistent with the view of the MDH for the return dynamics.
Bridging Econometrics and AI: VaR Estimation via Reinforcement Learning and GARCH Models
Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Univ. of Lille（里尔大学）；Centrale Lille（里尔中央理工学院）；UMR 9189 - CRIStAL（法国国家科学研究中心信号与自动控制、计算机科学实验室）；MRE UR 209（蒙彼利埃经济研究中心）
Synthetic Financial Data Generation for Enhanced Financial Modelling
AIMS Rwanda（非洲数学科学研究所（Rwanda））；African Institute for Mathematical Sciences（非洲数学科学研究所）；College of Engineering, Carnegie Mellon University（卡内基梅隆大学工程学院）；Sefako Makgatho Health Sciences University (SMU)（塞法科·马加索健康科学大学）；AI Research and Innovation Nexus for Africa (AIRINA Labs)（非洲人工智能研究与创新 nexus（AIRINA 实验室））；AI.Technipreneurs, Bénin（AI.Technipreneurs（贝宁））
