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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.

168,695 papers · 148 categories

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1223 · Mar 202219922001200920172026
48 results for time-inhomogeneous

Paper examines floating exercise boundaries for American options in time-inhomogeneous models.

problem Floating exercise boundaries in time-inhomogeneous models with negative interest rates or yields.
method Semi-analytical approach for pricing American options.
result Specialized pricing methodologies are required for models with floating exercise boundaries.

Time homogeneous polynomial processes are Markov processes whose moments can be calculated easily through matrix exponentials. In this work, we develop a notion of time inhomogeneous polynomial processes where the coeffiecients of the process may depend on time. A full characterization of this model class is given by m…

2018-06-11abs ↗pdf ↗

This thesis is devoted to the study of affine processes and their applications in financial mathematics. In the first part we consider the theory of time-inhomogeneous affine processes on general state spaces. We present a concise setup for time-inhomogeneous Markov processes. For stochastically continuous affine proce…

2015-12-10abs ↗pdf ↗

Probabilistic proof of smooth boundaries in optimal stopping problems.

problem Continuous differentiability of time-dependent optimal boundaries in optimal stopping problems.
method Local probabilistic arguments for a wider range of conditions.
result First probabilistic proof of continuous differentiability under general conditions.

The paper analyzes convergence of Langevin dynamics with time-dependent metrics.

problem Analyzing convergence of Langevin dynamics with time-dependent metrics.
method Formulated a modified gradient flow of the Kullback-Leibler divergence, selected a time-dependent relative Fisher information functional, and developed a time-dependent Hessian matrix condition.
result Proved convergence conditions for various Langevin dynamics.

This paper develops methods for pricing American Parisian options under general Markov models.

problem Pricing American Parisian options with various types and payoff functions.
method General approaches using CTMC approximation for time-inhomogeneous Markov models, including state augmentation and variational inequalities.
result Efficient algorithms for pricing American Parisian options confirmed with numerical experiments.

A new ML algorithm solves complex economic control problems.

problem Solving high-dimensional, finite-horizon stochastic control problems in economics.
method Deep neural network representation of optimal policy functions with three key features.
result Efficiently solves various economic control problems including recursive utility and growth models.

We derive explicit valuation formulae for an exotic path-dependent interest rate derivative, namely an option on the composition of LIBOR rates. The formulae are based on Fourier transform methods for option pricing. We consider two models for the evolution of interest rates: an HJM-type forward rate model and a LIBOR-…

2009-02-19abs ↗pdf ↗

The latter author, together with collaborators, proposed a numerical scheme to calculate the price of barrier options. The scheme is based on a symmetrization of diffusion process. The present paper aims to give a mathematical credit to the use of the numerical scheme for Heston or SABR type stochastic volatility model…

2012-06-26abs ↗pdf ↗

Unified framework for pricing various debt securities.

problem Pricing of different types of debt securities under general short-rate processes.
method Unifying framework using continuous-time Markov chain approximations and bi-dimensional diffusion processes.
result Closed-form matrix expressions and efficient algorithms for pricing various debt securities.

Paper applies theorem to find optimal investment boundary in stochastic capacity expansion.

problem Finding optimal investment boundary in a stochastic, time-inhomogeneous capacity expansion problem.
method Applies Bank and El Karoui Representation Theorem to solve first order conditions involving a non-integral term.
result Existence of base capacity ly(t)l^{\star}_y(t), showing optimal investment process becomes active at this level.

In this article, we explore a class of tractable interest rate models that have the property that the price of a zero-coupon bond can be expressed as a polynomial of a state diffusion process. Our results include a classification of all such time-homogeneous single-factor models in the spirit of Filipovic's maximal deg…

2015-04-13abs ↗pdf ↗

This paper aims at transferring the philosophy behind Heath-Jarrow-Morton to the modelling of call options with all strikes and maturities. Contrary to the approach by Carmona and Nadtochiy (2009) and related to the recent contribution Carmona and Nadtochiy (2012) by the same authors, the key parametrisation of our app…

2013-05-24abs ↗pdf ↗

In this paper we study time-inhomogeneous affine processes beyond the common assumption of stochastic continuity. In this setting times of jumps can be both inaccessible and predictable. To this end we develop a general theory of finite dimensional affine semimartingales under very weak assumptions. We show that the co…

2018-04-20abs ↗pdf ↗

Improves predictions by integrating forward-looking views into dynamic factor models.

problem Poor forecasts from historical data when dynamics change.
method Combines historical data with forward-looking views using a dynamic factor model.
result Derives optimal portfolio strategies influenced by both myopic and intertemporal factors.

The paper analyzes diffusion condensation for data geometry and topology.

problem Understanding the geometry and topology of high-dimensional data.
method Time-inhomogeneous diffusion process with geometric, spectral, and topological analysis.
result The condensation process defines intrinsic condensation homology and ambient persistent homology.

This paper proposes a market consistent valuation framework for variable annuities with guaranteed minimum accumulation benefit, death benefit and surrender benefit features. The setup is based on a hybrid model for the financial market and uses time-inhomogeneous Lévy processes as risk drivers. Further, we allow for d…

2019-05-23abs ↗pdf ↗

Shot-Noise processes constitute a useful tool in various areas, in particular in finance. They allow to model abrupt changes in a more flexible way than processes with jumps and hence are an ideal tool for modelling stock prices, credit portfolio risk, systemic risk, or electricity markets. Here we consider a general f…

2016-12-20abs ↗pdf ↗

Large deviation principles for multivariate stochastic volatility models.

problem Understanding the behavior of log-processes in multivariate stochastic volatility models.
method Establishing a comprehensive sample path large deviation principle for log-processes.
result Asymptotic formulas for first exit times and barrier option prices derived from the LDP.

New method estimates Schrödinger bridge potentials via empirical risk minimization.

problem Estimating Schrödinger bridge potentials from samples.
method Rewriting Schrödinger system as a fixed-point equation and estimating the potential via empirical risk minimization.
result Uniform concentration of empirical risk around population counterpart under sub-Gaussian assumptions.

Paper improves Bayesian regret bounds for Thompson Sampling in reinforcement learning.

problem Improving Bayesian regret bounds for Thompson Sampling in reinforcement learning.
method Using a discrete set of surrogate environments and posterior consistency analysis, the authors derive an upper bound of order O(Hdl1T)O(H\sqrt{d_{l_1}T}).
result The derived upper bound of O(Hdl1T)O(H\sqrt{d_{l_1}T}) is a significant improvement over previous bounds.

VA-OPE improves OPE by incorporating variance information, achieving tighter error bounds.

problem Estimating value function of a target policy from offline data collected by a behavior policy.
method Proposes VA-OPE, an algorithm that reweights Bellman residual using estimated variance of the value function.
result Achieves a tighter error bound than the best-known result.

A multi-task GP model tracks time-varying transition probabilities between two states.

problem Tracking time-varying transition probabilities between 'moves' and 'pauses' states.
method Kernel-based multi-task Gaussian Process model with time-variability and constraints.
result Enforces constraints while learning transition probabilities.

The challenge to fruitfully merge state-of-the-art techniques from mathematical finance and numerical analysis has inspired researchers to develop fast deterministic option pricing methods. As a result, highly efficient algorithms to compute option prices in Lévy models by solving partial integro differential equations…

2015-02-26abs ↗pdf ↗

LGS-Net improves NCO performance on combinatorial optimization tasks.

problem NP-hard combinatorial optimization problems in logistics, manufacturing, and drug discovery.
method LGS-Net uses a latent space model that conditions on problem instances and introduces Latent Guided Sampling for efficient inference.
result Empirical results show state-of-the-art performance on benchmark routing tasks.

The paper optimizes retirement spending considering habit formation and pension income.

problem Optimizing lifetime consumption under habit formation and pension income.
method Time inhomogeneous stochastic control problem with numerical solution using finite difference scheme.
result Consumption patterns change over time based on habit and optimal strategy.

New risk metric for RL in finance considers time splits of returns.

problem Optimizing financial decisions with a balance between return and risk.
method Developed a new risk metric for reinforcement learning that allows for flexible target levels of rewards over time.
result Proposed risk metric optimizes for arbitrary time splits of returns, improving upon classical risk measures.

The paper explains how continuous language models can produce discrete, interpretable meanings.

problem Semantic collapse in continuous systems of large language models.
method Formalizing large language models as Continuous State Machines (CSMs) and analyzing the associated transfer operator.
result The leading eigenfunctions of the transfer operator induce a finite number of invariant meaning basins, explaining how continuous computation can produce discrete, interpretable semantics.