Extends rough fractional stochastic volatility to a multivariate fOU model with GMM estimation, simulation validation, and empirical analysis of realized volatility series showing correlations and spillover effects.
On deep calibration of (rough) stochastic volatility models
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Bayesian neural SDE calibration produces posterior mixtures that deliver robust bounds on implied volatility by jointly using historical and option data, learning the historical-to-risk-neutral measure change, and sampling via Langevin dynamics.
A hybrid deep learning approach using Price Approximator and Calibration Correction networks improves the efficiency and accuracy of Heston model calibration on S&P 500 option data.
citing papers explorer
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Multivariate Rough Volatility
Extends rough fractional stochastic volatility to a multivariate fOU model with GMM estimation, simulation validation, and empirical analysis of realized volatility series showing correlations and spillover effects.
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Robust financial calibration: a Bayesian approach for neural SDEs
Bayesian neural SDE calibration produces posterior mixtures that deliver robust bounds on implied volatility by jointly using historical and option data, learning the historical-to-risk-neutral measure change, and sampling via Langevin dynamics.
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Deep Learning-Enhanced Calibration of the Heston Model: A Unified Framework
A hybrid deep learning approach using Price Approximator and Calibration Correction networks improves the efficiency and accuracy of Heston model calibration on S&P 500 option data.