68 papers
This paper solves Merton's portfolio optimization problem in a multivariate fake stationary affine Volterra setting by employing a martingale optimality principle and a Riccati backward stochastic differential equation to derive semi-closed form optimal strategies, overcoming the challenges posed by non-Markovian and non-semimartingale dynamics.
This paper introduces a machine learning framework that utilizes a directed bipartite graph to model time-ordered predictive linkages between U.S. and Chinese equity markets, revealing a significant directional asymmetry where U.S. returns strongly predict Chinese intraday performance while the reverse effect is limited.
This paper proposes a model-independent framework that utilizes perturbed optimal transport and Fisher information linearization to rapidly generate accurate SPX and VIX risk scenarios and hedging strategies without requiring full recalibration after market shocks.
This paper introduces a risk-aware evaluation framework for Large Language Models in financial services, featuring a domain-specific taxonomy, an automated multi-round red-teaming pipeline, and a Risk-Adjusted Harm Score (RAHS) metric to better capture and quantify severe, operationally actionable security failures that traditional domain-agnostic benchmarks miss.
Using proprietary transaction-level data on gilt-backed repo trades, this paper demonstrates that dealer market power and the transmission of heterogeneous shocks within funding networks are significant drivers of bond mispricing, collectively accounting for up to 4 percentage points of yield deviation.
This paper proposes a two-tier stochastic control model for market making in aggregator-routed RFQ markets that links dealer quoting strategies to slowly varying win-score promotion gates, revealing how the interplay between fast inventory dynamics and slow score evolution can lead to bistability, hysteresis, and endogenous "campaign vs. harvest" quoting patterns.
This paper introduces the Weighted Generalized Risk Measure and its associated Weighted Risk Quadrangle framework to synthesize heterogeneous risk assessments, providing analytical characterizations, tractable optimization methods, and empirical evidence of superior portfolio performance in mitigating losses from single-scenario judgments.
This paper proposes a hybrid Hidden Markov Model that combines Laplace quantile-defined market states with a Poisson-driven jump-duration mechanism to generate synthetic equity excess growth rates that simultaneously preserve heavy-tailed distributions, volatility clustering, and realistic tail-state dwell times, outperforming standard GARCH and HMM models in joint distributional and temporal fidelity.
This paper demonstrates through computer simulations that the entropy of exchange economies can be empirically measured using a calorimetric approach, validating the framework of thermal macroeconomics by confirming that entropy acts as a concave, path-independent state function even in complex systems where traditional microfoundations are infeasible.
This paper introduces an uncertainty-aware deep hedging framework that utilizes a deep ensemble of LSTMs to quantify model confidence, enabling a CVaR-optimized blending strategy with the Black-Scholes delta that significantly outperforms both classical and theoretically optimal hedging approaches, particularly when model agreement is high.
Using interpretable machine learning on 2022 PISA data from ten Latin American countries, this study identifies that student underachievement is primarily driven by a combination of individual socioeconomic factors (such as poverty, minority language status, and child labor) and systemic school-level disadvantages (including poor ICT infrastructure and low teacher certification), with repetition and household wealth emerging as consistent predictors across the region.
This paper employs a global game model to demonstrate how the interplay between public and private information, large sales, and collateral quality influences stablecoin depegging risks, revealing that greater private signal precision can paradoxically enhance stability in strong fundamental regimes and explaining the resilience of opaque stablecoins.
This paper presents a neural network framework for multi-period risk-reward stochastic control with discontinuous, constrained policies, proving that the empirical optimum of the parametrized objective converges in probability to the true optimal value as network capacity and sample size increase, while numerical experiments validate the method's accuracy and out-of-sample robustness.
This study utilizes survey data and regression analyses to demonstrate that both sleep duration and quality significantly influence redistribution preferences, revealing an inverted U-shaped relationship where optimal sleep maximizes allowable tax rates, with stronger positive effects observed among high-income earners and those facing hypothetical scenarios of doubled tax redistribution.
Using a Japanese panel dataset from 2016 to 2024, this study finds that the COVID-19 pandemic reduced redistribution preferences among high-income individuals, a decline that was mitigated by higher trust in government but remained unaffected by generalized trust or reciprocity.
Using Japanese data, this study reveals that the post-pandemic shift from unconditional support for tax-based redistribution to conditional altruism has created a widening gap where income redistribution now critically depends on trust in government, particularly among high-income individuals.
This paper develops a nonlinear theoretical framework to analyze public expenditure reform in Uruguay, demonstrating that structural adjustments involving institutional frictions and transition costs initially increase total spending before converging to a more efficient long-run allocation, thereby producing a J-shaped expenditure trajectory.
This paper develops a dynamic equilibrium model for Automated Market Makers that reveals how the strategic interaction between informed arbitrageurs, noise traders, and liquidity providers—shaped by intrinsic buy-sell asymmetry and execution costs—leads to a non-monotonic, hump-shaped relationship between volatility and optimal liquidity provision.
This paper develops a general framework for dynamic, non-concave portfolio choice under smooth ambiguity and Bayesian learning, demonstrating how a robust representation of ambiguity aversion yields explicit trading rules and, in the context of delegated management, leads to more conservative risk-taking and reduced volatility.
This paper extends existing valuation frameworks for differential swaps by deriving explicit pricing and hedging strategies for contracts with collateral denominated in a currency different from the cash flows, demonstrating that such foreign-currency collateral introduces significant non-trivial valuation adjustments and risk exposures that must be incorporated into modern multi-currency models.