Non-spanning expiries identify the no-event volatility surface while event-spanning quotes calibrate deterministic-time jumps, yielding better held-out pricing for SPX options around macro events than surface-absorbing or amortized alternatives.
Quantitative Finance , volume =
9 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Develops a stochastic control model for option market making with hedging-induced impact on the underlying, analyzes manipulation and arbitrage risks, proves well-posedness of the mixed control problem, and numerically approximates optimal strategies via policy optimization.
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.
A deep learning dynamic programming scheme prices path-dependent convertible bonds under GBM, CEV and Heston dynamics, showing that reset and call clauses dominate the underlying process in determining value and that downward resets can paradoxically lower bond prices.
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.
PTMC is a proposed Monte Carlo estimator that generates market-outcome distributions by simulating continuous double-auction interactions among persona-conditioned neural-policy bots whose heterogeneity is drawn from a learned distribution.
A matrix approximation technique computes Bachelier option prices and Greeks under stochastic volatility models for infinitely many strikes from finite expectations.
Constructs silting t-structures in the Q-shaped derived category from admissible partitions of Q, with explicit cotorsion pairs, homological descriptions, and examples of when none exist.
Hybrid CoMeTS-GAN plus diffusion model generates multivariate financial time series claimed to better reproduce stylized facts and inter-asset correlations than prior generative methods.
citing papers explorer
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Non-Spanning Identification of Scheduled Event Risk in Option Pricing
Non-spanning expiries identify the no-event volatility surface while event-spanning quotes calibrate deterministic-time jumps, yielding better held-out pricing for SPX options around macro events than surface-absorbing or amortized alternatives.
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Option market making with hedging-induced market impact
Develops a stochastic control model for option market making with hedging-induced impact on the underlying, analyzes manipulation and arbitrage risks, proves well-posedness of the mixed control problem, and numerically approximates optimal strategies via policy optimization.
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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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A deep learning approach for pricing convertible bonds with path-dependent reset and call provisions
A deep learning dynamic programming scheme prices path-dependent convertible bonds under GBM, CEV and Heston dynamics, showing that reset and call clauses dominate the underlying process in determining value and that downward resets can paradoxically lower bond prices.
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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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Persona-Trained Monte Carlo: Estimating Market-Outcome Distributions via Swarms of Persona-Conditioned Neural Policy Bots in a Limit Order Book
PTMC is a proposed Monte Carlo estimator that generates market-outcome distributions by simulating continuous double-auction interactions among persona-conditioned neural-policy bots whose heterogeneity is drawn from a learned distribution.
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Matrix Approximation of Bachelier Option Prices and Greeks under Stochastic Volatility models
A matrix approximation technique computes Bachelier option prices and Greeks under stochastic volatility models for infinitely many strikes from finite expectations.
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Silting t-structures in $Q$-shaped derived categories
Constructs silting t-structures in the Q-shaped derived category from admissible partitions of Q, with explicit cotorsion pairs, homological descriptions, and examples of when none exist.
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High-Quality Synthetic Financial Time-Series using a GAN-Diffusion Framework
Hybrid CoMeTS-GAN plus diffusion model generates multivariate financial time series claimed to better reproduce stylized facts and inter-asset correlations than prior generative methods.