This paper introduces MarketGAN, a factor-based generative framework for high-dimensional asset return generation under severe data scarcity. We embed an explicit asset-pricing factor structure as an economic inductive bias and generate returns as a single joint vector, thereby preserving cross-sectional dependence and tail co-movement alongside inter-temporal dynamics. MarketGAN employs generative adversarial learning with a temporal convolutional network (TCN) backbone, which models stochastic, time-varying factor loadings and volatilities and captures long-range temporal dependence. Using daily returns of large U.S. equities, we find that MarketGAN more closely matches empirical stylized facts of asset returns, including heavy-tailed marginal distributions, volatility clustering, leverage effects, and, most notably, high-dimensional cross-sectional correlation structures and tail co-movement across assets, than conventional factor-model-based bootstrap approaches. In portfolio applications, covariance estimates derived from MarketGAN-generated samples outperform those derived from other methods when factor information is at least weakly informative, demonstrating tangible economic value.

翻译：本文提出MarketGAN，一种基于因子的生成框架，用于在数据极度稀缺条件下生成高维资产收益率。我们嵌入显式的资产定价因子结构作为经济归纳偏置，并以单一联合向量的形式生成收益率，从而在保持截面依赖性和尾部联动性的同时保留跨期动态特征。MarketGAN采用以时序卷积网络（TCN）为主干的生成对抗学习框架，该网络能够建模随机时变的因子载荷与波动率，并捕捉长程时序依赖性。基于美国大型股票的日收益率数据，我们发现相较于传统的基于因子模型的bootstrap方法，MarketGAN生成的收益率更贴近资产收益率的经验典型事实，包括厚尾边际分布、波动率聚类、杠杆效应，以及最为显著的高维截面相关结构和跨资产尾部联动特征。在投资组合应用中，当因子信息至少具有弱信息性时，基于MarketGAN生成样本所得的协方差估计优于其他方法所得结果，展现出切实的经济价值。