Results 231 to 240 of about 15,601 (265)
Some of the next articles are maybe not open access.
2015
This chapter considers jump-diffusion processes to allow for price fluctuations to have two components, one consisting of the usual increments of a Wiener process, the second allows for “large” jumps from time-to-time. We introduce Poisson jump process with either absolute or proportional jump sizes through the stochastic integrals and provide ...
Carl Chiarella +2 more
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This chapter considers jump-diffusion processes to allow for price fluctuations to have two components, one consisting of the usual increments of a Wiener process, the second allows for “large” jumps from time-to-time. We introduce Poisson jump process with either absolute or proportional jump sizes through the stochastic integrals and provide ...
Carl Chiarella +2 more
openaire +1 more source
Distributions of Functionals of Switching Diffusions with Jumps
Journal of Mathematical Sciences, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Transformations of diffusions with jumps
Journal of Mathematical Sciences, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Optimal Harvesting of a Jump Diffusion Population and the Effect of Jump Uncertainty
SIAM Journal on Control and Optimization, 2003Summary: The problem of irreversibly harvesting from a general one-dimensional (Wiener--Poisson) jump diffusion population model is studied. For a wide class of stochastic models, the optimal strategy has a downwards local time reflection at a trigger level x*, which is typically known to be larger than in the corresponding deterministic problem if the
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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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Diffusions, Jump-Diffusions and Heat Equations
2019We study diffusions and jump-diffusions on a Euclidean space determined by SDE studied in Chap. 3. We select topics which are related to the stochastic flow generated by the SDE; topics are concerned with heat equations and backward heat equations.
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Option Pricing Under a Mixed-Exponential Jump Diffusion Model
Management Science, 2011Cai Ning, S G Kou
exaly
On the discounted penalty at ruin in a jump-diffusion and the perpetual put option
Insurance: Mathematics and Economics, 1998Hans U Gerber
exaly