68 papers
This paper proposes a dynamic market-making framework for spot FX that jointly optimizes size-dependent quotes and trade rejection decisions to manage latency-driven adverse selection, while incorporating an endogenous feedback mechanism where past rejections modulate client order flow, ultimately solved via dynamic programming and approximated by a tractable adiabatic-quadratic method.
This paper investigates the relationship between long-only global minimum variance portfolios and asset factor exposures, providing explicit analytical solutions for single-factor models and geometric descriptions for multi-factor scenarios, validated through empirical US stock data.
This paper provides a comprehensive definition and complete implementation guide for a time-dependent SABR-type Libor Market Model (SABR/LMM) designed to flexibly match the volatility skew and smile of interest rate derivatives, addressing the practical limitations of existing models in global banking.
This paper introduces a differential machine learning framework for pricing 0DTE options under stochastic volatility and jumps, which utilizes a specialized network architecture and a three-stage training procedure to simultaneously compute accurate prices and Greeks while significantly outperforming traditional Fourier-based benchmarks in speed and stability.
This paper presents a stochastic macro-financial model integrating the Keen framework with a jump-diffusion asset market to formalize the feedback loops between credit expansion and crash risk, demonstrating how endogenous financial fragility can generate regimes ranging from stable growth to recurrent boom-bust cycles.
This paper introduces two shortfall-aware reinforcement learning frameworks, RLOP and QLBS, which outperform traditional parametric models in reducing tail risk and hedging shortfalls for SPY and XOP options, thereby offering a more robust approach to autonomous derivatives risk management.
While theoretical models suggest Condorcet voting methods produce more moderate winners than Instant Runoff Voting, this study demonstrates that incorporating realistic voter behaviors, such as partial ballots, significantly diminishes the moderating advantage of Condorcet methods.
This paper introduces a generalized Bayesian framework using the Gibbs posterior to derive utility-consistent parametric portfolio policies without modeling the return generating process, and proposes a KNEEDLE algorithm to optimally select the data-weighting parameter in-sample, revealing that characteristic-based gains in U.S. equities (1955–2024) are concentrated pre-2000 and that the optimal weighting depends on risk aversion and higher-order moments.
This paper extends Kyle's (1985) seminal insider trading model to an infinite-dimensional, many-asset setting, deriving a parsimonious equilibrium with closed-form solutions for trading strategies, cross-market price impacts, and information efficiency.
This paper extends the restrictiveness measure to functional and structural econometric settings using Gaussian process priors, demonstrating that models appear more restrictive over continuum domains than finite observations, establishing restrictiveness as the limit of noise-free learning curves, and revealing how endogeneity and discrepancy function choices fundamentally alter model rankings.
This paper proposes that the negative skew and positive mean observed in stock returns stem from the broken symmetry of stochastic volatility between gains and losses, a phenomenon effectively modeled by a modified Jones-Faddy skew t-distribution that is validated using daily S&P 500 return data.
This paper proposes an enhanced indexation framework that treats index options as artificial assets through defined trading rules, demonstrating via S&P 500 data (2017–2025) and second-order stochastic dominance that integrating these option strategies significantly improves out-of-sample portfolio performance.
This paper addresses the challenge of measuring the "temperature" parameter in agent-based econophysics models beyond capital markets by establishing a measurement equation for a two-option decision system in a news environment and demonstrating its application to influence average opinion in competing subsystems.
This paper presents a no-arbitrage-based data-driven ensemble approach for option pricing that establishes a common representation space for domain adaptation and validates its effectiveness through real-world data and experimental results.
This paper analyzes an optimal stopping problem with a discontinuous reward function motivated by variable annuity pricing, establishing conditions under which surrender occurs only at maturity and deriving new analytical representations for the value function and surrender region based on fee and surrender charge structures.
This paper presents a unified equilibrium model combining Arrow-Debreu and Kyle frameworks to explain how informed agents with arbitrary private information about state probabilities trade state-contingent claims, thereby accounting for key option market phenomena such as common trading strategies and the volatility smile.
This paper provides a detailed exposition of various single-name CDS options, including recovery options and swaps, while presenting a new index option formula that prioritizes the use of the Black-76 model for consistency across asset classes and treats "armageddon events" as non-special cases.
This paper analyzes Italian manufacturing firms to demonstrate that while external recruitment is consistently necessary for all types of innovation, internal training strategies are particularly effective in driving the adoption of circular innovations.
This paper analyzes AI and robotics patenting trends from 1980 to 2019, revealing that while AI-enhanced robotics experienced a sharp post-2010 acceleration, the long-run integration of these technologies and the role of innovation systems vary significantly across countries, with China showing strong public-sector-driven integration and the US exhibiting a more market-oriented, less integrated structure.
This paper proposes a tractable stochastic correlation extension of the Vasicek credit risk model using circular diffusion processes to capture time-varying dependence, thereby deriving closed-form expressions for joint default probabilities and demonstrating how correlation risk significantly impacts tail-event assessments through empirical analysis of U.S. bank charge-off rates.