Simple GAN-based generators (RCGAN, GMMN) beat multivariate GARCH and factor stochastic volatility models on synthetic benchmarks, and their generated return paths improved HAR volatility forecasts in a simulated straddle trading task.
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Systematic comparison of deep generative models applied to multivariate financial time series
Simple GAN-based generators (RCGAN, GMMN) beat multivariate GARCH and factor stochastic volatility models on synthetic benchmarks, and their generated return paths improved HAR volatility forecasts in a simulated straddle trading task.