Develops basis-expansion reductions for stochastic hedge ratios with residual-minimization and projected-moment (Galerkin/Petrov-Galerkin) coefficient criteria to accelerate pathwise sensitivity-to-hedge conversion in Monte Carlo engines.
Turbocharging Monte Carlo pricing for the rough Bergomi model
6 Pith papers cite this work. Polarity classification is still indexing.
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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.
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.
Proposes using timing synchronicity and statistical surprise to detect per-action price impact, assuming fast adverse events indicate causation.
Discounting sensitivities align liquidity forecasting with self-financing replication, while a liquidity valuation adjustment captures settlement lags.
A coupled reaction-diffusion model of order books yields the LMF trade-sign long memory and square-root meta-order impact, reinterpreted as event-time versus physical-time statements with subordination effects.
citing papers explorer
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Faster Forward Sensitivities: Reduced stochastic hedge ratios from pathwise algorithmic differentiation
Develops basis-expansion reductions for stochastic hedge ratios with residual-minimization and projected-moment (Galerkin/Petrov-Galerkin) coefficient criteria to accelerate pathwise sensitivity-to-hedge conversion in Monte Carlo engines.
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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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Realtime price impact detection
Proposes using timing synchronicity and statistical surprise to detect per-action price impact, assuming fast adverse events indicate causation.
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Replication-Consistent Liquidity Forecasting for Derivatives -- Forward Funding Sensitivities and a Liquidity Valuation Adjustment for Settlement Lags
Discounting sensitivities align liquidity forecasting with self-financing replication, while a liquidity valuation adjustment captures settlement lags.
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Revisiting Trade-sign Long-memory and Square-root Law price impact
A coupled reaction-diffusion model of order books yields the LMF trade-sign long memory and square-root meta-order impact, reinterpreted as event-time versus physical-time statements with subordination effects.