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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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
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Bayesian Joint Estimation of the Hurst Parameter and Volatility with Applications to Fractional Option Pricing
Bayesian joint estimation of Hurst parameter and volatility in fractional SDE models is developed to propagate parameter uncertainty into fractional Black-Scholes option prices.