Results 221 to 230 of about 9,750 (263)
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Jump-diffusion processes as models for neuronal activity
Biosystems, 1997Aiming at an improvement of the existing neuronal models, we consider a mixed process ensuing from the superposition of continuous diffusions and of Poisson time-distributed sequence of impulses and focus our attention on the moments of the firing time.
GIRAUDO, Maria Teresa +1 more
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Neural Conformal Inference for jump diffusion processes
Journal of EconometricszbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hyeong Jin Hyun, Xiao Wang
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Minimal martingale measures for jump diffusion processes
Journal of Applied Probability, 2004We consider an incomplete market model whose stock price fluctuation is given by a jump diffusion process. For this model, we calculate the density process of the minimal martingale measure. Also, we state the relation to a locally risk-minimizing strategy.
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Stability for multidimensional jump-diffusion processes
Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826), 2005The aim of this work is to obtain sufficient conditions for stability of multidimensional jump-diffusion processes in the sense of exponential stability. The technique employed is to construct appropriate Lyapunov functions.
null Qi-Min Zhang, null Xi-Ning Li
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Calculations of Greeks for Jump Diffusion Processes
Mediterranean Journal of Mathematics, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Jump-diffusion processes in tracking/recognition
IEEE Transactions on Signal Processing, 1998Advances in sensor technology permit more sophisticated tracking/identification algorithms to be implemented. This correspondence compares and contrasts the modeling framework employed in two recent image-enhanced trackers and generalizes one of them (the PME) for use in target identification.
D.D. Sworder, J.E. Boyd
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2019
In this chapter we introduce jump-diffusion processes and provide a theoretical framework that justifies the nonparametric (data-based) extraction of the parameters and functions controlling the arrival of a jump and the distribution of the jump size from the estimated conditional Kramers–Moyal moments. The method and the results are applicable to both
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In this chapter we introduce jump-diffusion processes and provide a theoretical framework that justifies the nonparametric (data-based) extraction of the parameters and functions controlling the arrival of a jump and the distribution of the jump size from the estimated conditional Kramers–Moyal moments. The method and the results are applicable to both
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Stability of regime-switching jump diffusion processes
Journal of Mathematical Analysis and Applications, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ji, Huijie, Shao, Jinghai, Xi, Fubao
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Option Pricing Under Jump-Diffusion Processes
2015This chapter extends the hedging argument of option pricing developed for continuous diffusion processes previously to the situations when the underlying asset price is driven by the jump-diffusion stochastic differential equations. By constructing hedging portfolios and employing the capital asset pricing model, we provide an option pricing integro ...
Carl Chiarella +2 more
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Implicit–explicit numerical schemes for jump–diffusion processes
Calcolo, 2007The authors consider the numerical solution of the Cauchy problem for a parabolic integro-differential equation (PIDE) in one spatial dimension. Such equations arise in applications in financial mathematics with Lévy processes. The focus here is on the time discretization of the stiff system of ordinary differential equations resulting from a spatial ...
Briani M, Natalini R, Russo G
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