Introduces SOCK (SOft Competing Kernels), a differentiable random convolutional feature map, to train generative models of financial time series via feature matching and shows outperformance over signature and diffusion baselines on small-sample datasets.
Mandelbrot and John W
2 Pith papers cite this work. Polarity classification is still indexing.
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Bayesian joint estimation of Hurst parameter and volatility in fractional SDE models is developed to propagate parameter uncertainty into fractional Black-Scholes option prices.
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Generating Financial Time Series by Matching Random Convolutional Features
Introduces SOCK (SOft Competing Kernels), a differentiable random convolutional feature map, to train generative models of financial time series via feature matching and shows outperformance over signature and diffusion baselines on small-sample datasets.