Advanced Generative Adversarial Networks for Financial Time Series: Incorporating Stochastic Masking, Multi-Frequency Data, Hidden Markov Models and Vector Quantization 

Abstract

In quantitative finance, the creation of extensive and diverse synthetic time series data holds practical significance. Not only does it bolster the stability of training data-intensive models like neural networks, but it also fortifies model resilience against noise through the introduction of synthetic data idiosyncrasies. This research introduces novel structures that integrate Regime classification, Conditional structures, and Vector Quantization with generative adversarial networks (GANs) to generate diverse financial market data, specifically designed for simulating benchmarks and futures data time series. The primary objective is to produce time series datasets that reliably replicate stylized facts, encompassing crucial distributional features and autocorrelation functions, mirroring the input data. We have proposed stochastic attention mask with temporal attention GAN (TAGAN) and temporal transformer GAN (TTGAN) to capture difference markets characteristics, high-low freqency GAN (HLF-GAN) to capture varied seasonal patterns, RegimeGAN for mimicking the complex dynamics of regime shifts in financial markets and be able to do scenario simulations, CGAN and VQC-VAEGAN to model volatility clustering effectively, forging a promising avenue in the synthesis of financial time series data with remarkable fidelity.

Keywords: Generative Adversarial Network, stochastic attention mask, RegimeGAN, HLF-GAN, CGAN, VQCVAEGAN