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We develop a recursive approach for deriving closed-form solutions to both conditional and unconditional moments of affine jump diffusions with state-independent jump intensities. Using these moment solutions, we construct closed-form…

Mathematical Finance · Quantitative Finance 2025-04-10 Yan-Feng Wu , Jian-Qiang Hu

We put forward a complete theory on moment explosion for fairly general state-spaces. This includes a characterization of the validity of the affine transform formula in terms of minimal solutions of a system of generalized Riccati…

Probability · Mathematics 2016-01-07 Eberhard Mayerhofer

This paper proposes a widely applicable method of approximate maximum-likelihood estimation for multivariate diffusion process from discretely sampled data. A closed-form asymptotic expansion for transition density is proposed and…

Statistics Theory · Mathematics 2013-08-14 Chenxu Li

In this paper we discuss a closed-form approximation of the likelihood functions of an arbitrary diffusion process. The approximation is based on an exponential ansatz of the transition probability for a finite time step $\Delta t$, and a…

Physics and Society · Physics 2008-12-10 Luca Capriotti

The paper presents new simple sharp bounds for transition density functions for time-homogeneous diffusions processes. The bounds are obtained under mild conditions on the drift and diffusion coefficients, extending and substantially…

Probability · Mathematics 2008-12-08 Andrew N. Downes

In this paper we study the transition density and exponential ergodicity in total variation for an affine process on the canonical state space $\mathbb{R}_{\geq0}^{m}\times\mathbb{R}^{n}$. Under a H\"ormander-type condition for diffusion…

Probability · Mathematics 2020-06-18 Martin Friesen , Peng Jin , Jonas Kremer , Barbara Rüdiger

Affine jump-diffusions constitute a large class of continuous-time stochastic models that are particularly popular in finance and economics due to their analytical tractability. Methods for parameter estimation for such processes require…

Mathematical Finance · Quantitative Finance 2018-11-02 Xiaowei Zhang , Peter W. Glynn

A computational technique borrowed from the physical sciences is introduced to obtain accurate closed-form approximations for the transition probability of arbitrary diffusion processes. Within the path integral framework the same technique…

Physics and Society · Physics 2008-12-10 Luca Capriotti

We assume that we observe $N$ independent copies of a diffusion process on a time-interval $[0,2T]$. For a given time $t$, we estimate the transition density $p_t(x,y)$, namely the conditional density of $X_{t + s}$ given $X_s = x$, under…

Statistics Theory · Mathematics 2025-05-01 Fabienne Comte , Nicolas Marie

We present a new approximation scheme for the price and exercise policy of American options. The scheme is based on Hermite polynomial expansions of the transition density of the underlying asset dynamics and the early exercise premium…

Computational Finance · Quantitative Finance 2021-04-27 Li Chen , Guang Zhang

We show the existence of a broad class of affine Markov processes in the cone of positive self-adjoint Hilbert-Schmidt operators. Such processes are well-suited as infinite dimensional stochastic volatility models. The class of processes we…

Probability · Mathematics 2022-01-28 Sonja Cox , Sven Karbach , Asma Khedher

This paper develops power series expansions of a general class of moment functions, including transition densities and option prices, of continuous-time Markov processes, including jump--diffusions. The proposed expansions extend the ones…

Econometrics · Economics 2023-08-21 Dennis Kristensen , Young Jun Lee , Antonio Mele

A variety of researchers have successfully obtained the parameters of low dimensional diffusion models using the data that comes out of atomistic simulations. This naturally raises a variety of questions about efficient estimation,…

Statistical Mechanics · Physics 2015-11-06 Christopher P. Calderon

This paper provides closed-form expansions for the log-likelihood function of multivariate diffusions sampled at discrete time intervals. The coefficients of the expansion are calculated explicitly by exploiting the special structure…

Statistics Theory · Mathematics 2008-12-18 Yacine Aït-Sahalia

This paper shows that large nonparametric classes of conditional multivariate densities can be approximated in the Kullback--Leibler distance by different specifications of finite mixtures of normal regressions in which normal means and…

Statistics Theory · Mathematics 2010-10-05 Andriy Norets

The transition density of a diffusion process does not admit an explicit expression in general, which prevents the full maximum likelihood estimation (MLE) based on discretely observed sample paths. A\"{\i}t-Sahalia [J. Finance 54 (1999)…

Statistics Theory · Mathematics 2012-03-12 Jinyuan Chang , Song Xi Chen

We revisit affine diffusion processes on general and on the canonical state space in particular. A detailed study of theoretic and applied aspects of this class of Markov processes is given. In particular, we derive admissibility conditions…

Probability · Mathematics 2009-10-10 Damir Filipovic , Eberhard Mayerhofer

We introduce a closed-form expansion for the transition density of elliptic and hypo-elliptic multivariate Stochastic Differential Equations (SDEs), over a period $\Delta\in (0,1)$, in terms of powers of $\Delta^{j/2}$, $j\ge 0$. Our…

Numerical Analysis · Mathematics 2025-09-17 Yuga Iguchi , Alexandros Beskos

We study the long-time behavior of affine processes on positive self-adjoiont Hilbert-Schmidt operators which are of pure-jump type, conservative and have finite second moment. For subcritical processes we prove the existence of a unique…

Probability · Mathematics 2022-03-29 Martin Friesen , Sven Karbach

Diffusion models offer stable training and state-of-the-art performance for deep generative modeling tasks. Here, we consider their use in the context of multivariate subsurface modeling and probabilistic inversion. We first demonstrate…

Computer Vision and Pattern Recognition · Computer Science 2026-01-28 Roberto Miele , Niklas Linde
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