Long-Range Dependence in Financial Markets: Empirical Evidence and Generative Modeling Challenges

Abstract

This study provides an empirical investigation of long-range dependence (LRD) in financial markets and evaluates the ability of deep generative models to reproduce such temporal structures. Using daily data from three sectors--equity (S&P 500, DAX, Nikkei 225), commodities (Wheat, Corn, Soybeans), and energy (UNG, USO, XLE)--we examine LRD through rescaled range (R/S) analysis, detrended fluctuation analysis (DFA), segmented multifractal analysis around the COVID-19 period, and an ARFIMA--FIGARCH model with Student's $t$-distributed innovations. The evidence suggests that while mean returns exhibit limited persistence, pronounced long memory is observed in conditional volatility across most assets, and equity-market scaling properties change non-negligibly after 2020. Building on these findings, we assess whether Quant Generative Adversarial Networks (Quant GANs) can learn and reproduce these stylized temporal dependencies against econometric and resampling benchmarks. Although the generated series reproduce heavy-tailed return distributions and aspects of volatility clustering, they do not consistently capture the magnitude and persistence structure of LRD observed in real data. These results highlight an important limitation of deep generative architectures in modeling slow-decaying dependence structures and underscore the need for explicit long-memory mechanisms when synthetic financial data are intended for risk management or long-horizon forecasting.