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.
Abstract reviews Markov processes with jumps on manifolds and Lie groups.
New neural method for inferring Markov jump processes.
New Markov jump process improves Hamiltonian Monte Carlo efficiency.
A new method for smoothing and parameter inference of Markov jump processes.
We study optimal investment strategies that maximize expected utility from consumption and terminal wealth in a pure-jump asset price model with Markov-modulated (regime switching) jump-size distributions. We give sufficient conditions for existence of optimal policies and find closed-form expressions for the optimal v…
Study optimal portfolio selection in a complex market with jumps and regime shifts.
New model for insurance states using Markov jump processes with non-countable state space.
Proposes MLEs for MMJDM with EM-algorithm.
Markov jump processes and continuous time Bayesian networks are important classes of continuous time dynamical systems. In this paper, we tackle the problem of inferring unobserved paths in these models by introducing a fast auxiliary variable Gibbs sampler. Our approach is based on the idea of uniformization, and sets…
Masking diffusion outperforms other discrete diffusion models by incorporating jump times into the model.
New algorithm for Bayesian inference in population Markov Jump processes.
A new method for inferring latent states in Markov jump processes.
Generative models using PDMPs with explicit jump rates and kernels.
Enhances HDP-HMM for state transitions between similar states.
Markov jump processes (MJPs) are used to model a wide range of phenomena from disease progression to RNA path folding. However, maximum likelihood estimation of parametric models leads to degenerate trajectories and inferential performance is poor in nonparametric models. We take a small-variance asymptotics (SVA) appr…
New model improves cancer screening prediction accuracy.
New model for Knightian uncertainty with jumps.
Method infers MJPs from noisy observations without prior training.
The paper proposes a method to approximate posterior distributions of diffusion and jump processes from discrete observations.
Unified approach for drawdown and drawup of Markov processes.
We develop algorithms for the numerical computation of the quadratic hedging strategy in incomplete markets modeled by pure jump Markov process. Using the Hamilton-Jacobi-Bellman approach, the value function of the quadratic hedging problem can be related to a triangular system of parabolic partial integro-differential…
Paper develops models for better HFT and algorithmic trading.
This study bridges discrete and continuous state spaces using the Ehrenfest process and diffusion models.
New algorithm for continuous-time switching systems using variational inference.
We consider a general d-dimensional Levy-type process with killing. Combining the classical Dyson series approach with a novel polynomial expansion of the generator A(t) of the Levy-type process, we derive a family of asymptotic approximations for transition densities and European-style options prices. Examples of stoc…
The paper prices European options in a model with changing regimes and jumps.
We consider a Markov process , which is the solution of a stochastic differential equation driven by a Lévy process and an independent Wiener process . Under some regularity conditions, including non-degeneracy of the diffusive and jump components of the process as well as smoothness of the Lévy density of $Z…
Enlargement of filtrations is a classical topic in the general theory of stochastic processes. This theory has been applied to stochastic finance in order to analyze models with insider information. In this paper we study initial enlargement in a Markov chain market model, introduced by R. Norberg. In the enlargened fi…
The article uses jump-telegraph models to price zero coupon bonds and adjust convexity.
The article derives small-time expansions for jump-diffusion models with infinite jump activity.
In this paper we present an algorithm for pricing barrier options in one-dimensional Markov models. The approach rests on the construction of an approximating continuous-time Markov chain that closely follows the dynamics of the given Markov model. We illustrate the method by implementing it for a range of models, incl…
We introduce a new model for describing the fluctuations of a tick-by-tick single asset price. Our model is based on Markov renewal processes. We consider a point process associated to the timestamps of the price jumps, and marks associated to price increments. By modeling the marks with a suitable Markov chain, we can…
Bayesian inference for biochemical reaction networks using jump-diffusion approximations.
Develops new Markov processes with switching rates and past dependence.
It is well documented that a model for the underlying asset price process that seeks to capture the behaviour of the market prices of vanilla options needs to exhibit both diffusion and jump features. In this paper we assume that the asset price process is Markov with cadlag paths and propose a scheme for computing…
We present simple new examples of pure-jump strict local martingales. The examples are constructed as exponentials of self-exciting affine Markov processes. We characterize the strict local martingale property of these processes by an integral criterion and by non-uniqueness of an associated ordinary differential equat…
The chapter evaluates volatility and variance swap pricing under stochastic volatility models.
We derived similar to Bo et al. (2010) results but in the case when the dynamics of the FX rate is driven by a general Merton jump-diffusion process. The main results of our paper are as follows: 1) formulas for the Esscher transform parameters which ensure that the martingale condition for the discounted foreign excha…
We consider a self-exciting counting process, the parameters of which depend on a hidden finite-state Markov chain. We derive the optimal filter and smoother for the hidden chain based on observation of the jump process. This filter is in closed form and is finite dimensional. We demonstrate the performance of this fil…
We explore martingale and convex duality techniques to study optimal investment strategies that maximize expected risk-averse utility from consumption and terminal wealth. We consider a market model with jumps driven by (multivariate) marked point processes and so-called non-linear wealth dynamics which allows to take …
Proposes a new BSP-Tree process for flexible space partition modeling.
Study of Markov-modulated affine processes for richer models in finance.
Bayesian method selects interacting regions in Markov models.
The aim of this paper is to examine the time scaling of the semivariance when returns are modeled by various types of jump-diffusion processes, including stochastic volatility models with jumps in returns and in volatility. In particular, we derive an exact formula for the semivariance when the volatility is kept const…
Using a Levy process we generalize formulas in Bo et al.(2010) for the Esscher transform parameters for the log-normal distribution which ensure the martingale condition holds for the discounted foreign exchange rate. Using these values of the parameters we find a risk-neural measure and provide new formulas for the di…
The paper proposes a class of financial market models which are based on inhomogeneous telegraph processes and jump diffusions with alternating volatilities. It is assumed that the jumps occur when the tendencies and volatilities are switching. We argue that such a model captures well the stock price dynamics under per…
Jump Markov linear models consists of a finite number of linear state space models and a discrete variable encoding the jumps (or switches) between the different linear models. Identifying jump Markov linear models makes for a challenging problem lacking an analytical solution. We derive a new expectation maximization …
Develops RL for optimal market-making in non-Markov processes.