140 papers
This paper utilizes stochastic reflected diffusion processes to analyze how trading fees and continuous-time arbitrage impact liquidity provider profitability in Geometric Mean Market Makers, extending previous findings on Uniswap v2 to broader models like Balancer to derive the long-term expected logarithmic growth of LP wealth.
This paper formulates and solves a singular control problem for optimal trade execution under general price impact and stochastic liquidity regimes, proving that the value function is the unique viscosity solution to a system of Hamilton-Jacobi-Bellman inequalities and characterizing the resulting optimal strategy through its free boundary.
This paper derives semi-closed Fourier-based pricing formulas for European options on forward contracts within function-valued infinite-dimensional affine stochastic volatility models, specifically analyzing Gaussian models driven by finite-rank Wishart processes and pure-jump extensions of the Barndorff-Nielsen-Shephard framework to capture term structure risks.
This paper provides an explicit solution and computable characterization for the long-only minimum variance portfolio under a one-factor model with mixed-sign betas, resolving a prior open question and establishing that in high-dimensional regimes, the proportion of active assets converges to a value determined by the distribution of asset betas, notably vanishing when all betas are positive.
This paper theoretically demonstrates and empirically validates that mandatory disclosure of informed trades reduces trading costs more significantly in markets with weaker competition among market makers, a finding supported by a difference-in-differences analysis of the 2002 Sarbanes-Oxley Act reform.
This paper introduces a novel concept of Aharonov-Bohm-type arbitrage in financial markets, demonstrating how global topological inconsistencies (non-trivial holonomy) in filtered systems can generate profitable, predictable trading strategies despite the absence of local price discrepancies.
This paper proposes a regulator-leader-follower trilateral game framework based on optimal control theory to design quantitative investment regulation mechanisms that effectively mitigate herding behavior, minimize regulatory costs, and maximize social welfare.
This paper derives explicit closed-form solutions for the optimal timing of annuitization under a sudden, permanent mortality shock, demonstrating how the interplay between annuity attractiveness, financial returns, and bequest motives across health states dictates the strategy compared to a constant mortality benchmark.
This paper develops and evaluates pricing and hedging strategies for cross-currency equity protection swaps, comparing separate versus aggregated hedging paradigms and proposing superhedging techniques for OTC basket options to address the risks of international investor diversification.
This paper introduces the Anticipatory Neural Jump-Diffusion (ANJD) framework, a novel generative model that synthesizes forward-looking, discontinuous stochastic trajectories by performing sequential Maximum Mean Discrepancy gradient flows on a whitened Marcus-signature RKHS, thereby capturing complex regime shifts and heavy-tailed dynamics while providing rigorous generalization bounds.
This paper proposes the Deep Penalty Method (DPM), a deep learning algorithm inspired by penalty methods for free boundary PDEs to solve high-dimensional optimal stopping problems, providing theoretical error bounds and demonstrating its accuracy and efficiency through numerical tests on American option pricing.
This paper proposes an intertemporal, cost-efficient consumption model that extends Sharpe's Distribution Builder framework by utilizing copulas to capture dependencies between consumption periods, thereby recovering investor risk preferences without relying on difficult-to-measure utility functions, and demonstrates its application under both Black-Scholes and CEV market models.
This paper establishes a general framework for risk-indifference pricing of American-style contingent claims under asymmetric information, demonstrating its consistency with no-arbitrage principles and characterizing these prices via reflected backward stochastic differential equations to enable deep learning-based numerical implementation in stochastic volatility models.
This paper introduces "Financial Relativity," an information-geometric framework that unifies asset pricing by treating physical and risk-neutral measures as equivalent probabilistic reference frames, thereby reinterpreting prices as geometric projections of terminal payoffs and deriving volatility as an endogenous manifestation of evolving posterior uncertainty.
This paper establishes the existence and uniqueness of a strong solution to the Hamilton-Jacobi-Bellman equation for an optimal dividend ratcheting problem with costly capital injections under the Cramér-Lundberg model, thereby deriving an explicit optimal feedback strategy that advances the field beyond standard viscosity solution frameworks.
This paper addresses -robust utility maximization in the presence of intractable claims by transforming the dynamic stochastic control problem into a static quantile optimization task, deriving optimal payoffs as solutions to a system of first-order ordinary differential equations that accommodate various risk constraints and ambiguity attitudes.
This paper introduces Anticipatory Reinforcement Learning (ARL), a novel framework that bridges non-Markovian decision processes and classical reinforcement learning by embedding trajectory history into a signature-augmented manifold, enabling agents to achieve stable, proactive risk management and superior policy stability in volatile, continuous-time environments through a deterministic, single-pass evaluation of expected returns.
This paper establishes a compact embedding result for spaces of forward rate curves, demonstrating that any forward rate evolution can be approximated by a sequence of finite-dimensional processes within a larger state space.
This paper characterizes the set of equivalent martingale measures in two-period finite markets as convex combinations of a finite number of measures via a new algorithm, applying these results to analyze the Korn-Kreer-Lenssen model and demonstrate the limitations of discrete-time approximations for continuous-time markets.
This paper introduces amortizing perpetual options (AmPOs), a fungible variant of installment options that replaces explicit payments with notional decay, enabling their valuation to be reduced to equivalent vanilla perpetual American options with derived analytical formulas for exercise boundaries, Greeks, and risk-neutral pricing.