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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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60119179238 · May 202619922001200920172026
48 results for terminal law constraints

Framework learns stochastic dynamics from endpoint and intermediate distributions using soft energy constraints.

problem Learning stochastic dynamics from endpoint and intermediate distributional observations.
method Formulates generation as a McKean-Vlasov control problem with soft energy constraints, solving it through FBSDE.
result Model learns coherent stochastic trajectories matching prescribed marginal laws.

New framework finds periodic policies in reset-free MDPs with sublinear regret.

problem Reset-free reinforcement learning with unknown dynamics and terminal law constraints.
method Periodic framework, periodic policies, periodic regret.
result First non-asymptotic guarantees for reset-free learning in multi-agent settings.

Proposes a new model to optimize investment plans with varying terminal times.

problem Improving the classical mean-variance model for continuous time investments.
method Uses stochastic optimal control and varying terminal time to determine optimal strategies.
result Optimal strategies and terminal times can be determined to minimize portfolio variance.

GH-PID uses guided harmonic paths for efficient SOT with interpretable diagnostics.

problem Efficiently solving Stochastic Optimal Transport with hard terminal distributions and soft costs.
method Guided Harmonic Path-Integral Diffusion (GH-PID) framework with low-dimensional guidance.
result GH-PID generates geometry-aware, cost-reducing trajectories that match terminal distributions.

The paper optimizes insurance dividend payments and reinsurance strategies under specific distribution constraints.

problem Optimizing insurance dividend payments and reinsurance strategies with terminal distribution constraints.
method Explicit expressions for optimal strategies found in both discrete and continuous time settings.
result Explicit expressions for optimal dividend strategies and reinsurance strategies found.

ETCNN uses neural networks to price American options accurately.

problem Accurately pricing American options with inequality constraints.
method ETCNN framework solving BSM equations with exact terminal condition.
result ETCNN achieves high accuracy and robustness across various scenarios.

Social Security and other public policies can be viewed as a series of cash in and outflows that depend on parameters such as the age distribution of the population and the retirement age. Given forecasts of these parameters, policies can be designed to be financially stable, i.e., to terminate with a zero balance. If …

2012-01-30abs ↗pdf ↗

Given an initial (resp., terminal) probability measure μμ (resp., νν) on Rd\mathbb{R}^d, we characterize those optimal stopping times ττ that maximize or minimize the functional EB0Bτα\mathbb{E} |B_0 - B_τ|^α, α>0α> 0, where (Bt)t(B_t)_t is Brownian motion with initial law B0μB_0\sim μ and with final distribution --once stop…

2017-11-08abs ↗pdf ↗

This paper works out fair values of stock loan model with automatic termination clause, cap and margin. This stock loan is treated as a generalized perpetual American option with possibly negative interest rate and some constraints. Since it helps a bank to control the risk, the banks charge less service fees compared …

2010-05-09abs ↗pdf ↗

Study a continuous portfolio optimization with a new CVaR-like constraint using martingale approach.

problem Optimizing a portfolio under a new CVaR-like constraint that is not compatible with traditional methods.
method Follows a martingale approach in a complete market setting, solving a convex constrained minimization problem.
result Obtains a tractable and interpretable characterization of the optimal strategy.

Develops a framework for optimal investment in assets with different liquidity constraints.

problem Optimal investment-consumption problem for a utility-maximizing investor with lower-bound constraints.
method Generalized martingale approach and decomposition of the problem into subproblems.
result Explicit formulas for optimal strategies derived for power-utility functions.

We analyze linear McKean-Vlasov forward-backward SDEs arising in leader-follower games with mean-field type control and terminal state constraints on the state process. We establish an existence and uniqueness of solutions result for such systems in time-weighted spaces as well as a {convergence} result of the solution…

2018-09-12abs ↗pdf ↗

Study shows equivalence of four risk constraints in non-concave optimization problems.

problem Investigating risk constraints in non-concave optimization for financial companies.
method Analytical solutions for four risk constraints (ES, EDS, VaR, AVaR) under non-concave optimization.
result All four risk constraints lead to the same optimal solution, differing from concave optimization.

LatentFlow simplifies conditioning of stochastic processes without training.

problem Intractable conditional laws for complex stochastic models.
method Writing stochastic process as latent innovation, reducing conditioning to latent-space inference.
result Exact conditional sampling across various model classes.

Paper optimizes DC pension fund management with VaR and relative performance constraints.

problem Optimizing DC pension fund performance under VaR and relative performance constraints.
method Introduced an auxiliary process to transform the problem into a self-financing problem, combined linearization, Lagrange dual, martingale, and concavification methods.
result Explicit investment strategies obtained for certain penalty and reward functions.

The paper solves portfolio optimization problems with risk constraints.

problem Maximizing utility while ensuring a certain wealth threshold with risk constraints.
method Derives Nash equilibria for two agents and characterizes them for more than two agents.
result Characterizes Nash equilibria for different cases of competition probabilities.

Dynamic risk measures follow law invariance principles over time.

problem Tackles dynamic risk measurement principles.
method Shows equivalence between adapted law invariance and recursive one-step conditional-law representation for time-consistent risk measures.
result Identifies adapted law invariance as the dynamic counterpart of ordinary law invariance.

We study power utility maximization for exponential Lévy models with portfolio constraints, where utility is obtained from consumption and/or terminal wealth. For convex constraints, an explicit solution in terms of the Lévy triplet is constructed under minimal assumptions by solving the Bellman equation. We use a nove…

2009-12-09abs ↗pdf ↗

We study a robust maximization problem from terminal wealth and consumption under a convex constraints on the portfolio. We state the existence and the uniqueness of the consumption-investment strategy by studying the associated quadratic backward stochastic differential equation (BSDE in short). We characterize the op…

2013-07-02abs ↗pdf ↗

Assuming that agents' preferences satisfy first-order stochastic dominance, we show how the Expected Utility paradigm can rationalize all optimal investment choices: the optimal investment strategy in any behavioral law-invariant (state-independent) setting corresponds to the optimum for an expected utility maximizer w…

2013-02-19abs ↗pdf ↗

We consider the terminal wealth utility maximization problem from the point of view of a portfolio manager who is paid by an incentive scheme, which is given as a convex function gg of the terminal wealth. The manager's own utility function UU is assumed to be smooth and strictly concave, however the resulting utilit…

2011-09-13abs ↗pdf ↗

This paper focuses on martingale optimal transport problems when the martingales are assumed to have bounded quadratic variation. First, we give a result that characterizes the existence of a probability measure satisfying some convex transport constraints in addition to having given initial and terminal marginals. Sev…

2018-04-12abs ↗pdf ↗

Study finds 'Dragon Kings' in stock market volatility during major economic crises.

problem Identifying significant deviations from normal market volatility.
method Analyzed S&P500 index volatility, categorized as Black Swans, Dragon Kings, or Negative Dragon Kings, using modified Generalized Beta and Generalized Beta Prime distributions.
result Observed 'potential' Dragon Kings that eventually turn into Negative Dragon Kings, with more pronounced phenomenon as time averaging increases.

Stabilized neural differential equations enforce constraints on dynamical systems.

problem Ensuring dynamical systems preserve known constraints like conservation laws.
method SNDEs with a stabilization term to enforce manifold constraints.
result SNDEs outperform existing methods and broaden constraint types.

This paper deals with the super-replication of non path-dependent European claims under additional convex constraints on the number of shares held in the portfolio. The corresponding super-replication price of a given claim has been widely studied in the literature and its terminal value, which dominates the claim of i…

2013-07-23abs ↗pdf ↗

We consider the martingale optimal transport duality for càdlàg processes with given initial and terminal laws. Strong duality and existence of dual optimizers (robust semi-static superhedging strategies) are proved for a class of payoffs that includes American, Asian, Bermudan, and European options with intermediate m…

2017-07-26abs ↗pdf ↗

This paper optimizes insurance reinsurance design under solvency constraints.

problem Optimizing risk transfer from an insurance company to a reinsurer under solvency constraints.
method Martingale method to derive optimal reinsurance design maximizing terminal value of surplus.
result Optimal reinsurance designs include a combination of proportional and stop-loss protection.

Study explains Zipf's law using geometric mechanisms from a finite alphabet.

problem Explains Zipf's law in language without relying on linguistic elements.
method Uses the Full Combinatorial Word Model (FCWM) to generate geometric distributions of word lengths.
result Supports predictions of power-law rank-frequency curves, matching various languages.

Investigates ways to train larger models with fewer resources, finding that test loss depends only on the actual number of trainable parameters.

problem Training larger models for cheaper under hardware constraints.
method Emulates an increase in effective parameters using frozen random parameters or fast structured transforms.
result Scaling laws cannot be deceived by spurious parameters; test loss depends only on the actual number of trainable parameters.

A framework for eliciting utility functions from investor preferences.

problem Hard elicitation of specific utility functions in portfolio selection.
method Preference-fitting method using probability-wealth pairs and PHARA approximation.
result Fitted utility function converges to the optimal one as more data is used.

Zipf's law states that the number of firms with size greater than S is inversely proportional to S. Most explanations start with Gibrat's rule of proportional growth but require additional constraints. We show that Gibrat's rule, at all firm levels, yields Zipf's law under a balance condition between the effective grow…

2010-12-01abs ↗pdf ↗

We treat utility maximization from terminal wealth for an agent with utility function U:RRU:\mathbb{R}\to\mathbb{R} who dynamically invests in a continuous-time financial market and receives a possibly unbounded random endowment. We prove the existence of an optimal investment without introducing the associated dual prob…

2017-02-03abs ↗pdf ↗

New approach to control diffusion processes with soft constraints.

problem Finding an optimal diffusion process with a target terminal distribution.
method Generalized Schrödinger bridge problem with soft constraints, solving for a geometric mixture of target and other distributions.
result The terminal distribution of the optimally controlled process is a geometric mixture of the target and another distribution.

Generative AI connects to Schrödinger bridge problems with soft constraints for stability.

problem Stability issues in generative AI due to hard terminal constraints.
method Soft-constrained Schrödinger bridge formulation and convergence analysis.
result Existence and convergence of optimal solutions as penalty grows.