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==메타데이터==
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===위키데이터===
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* ID :  [https://www.wikidata.org/wiki/Q1557613 Q1557613]
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===Spacy 패턴 목록===
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* [{'LOWER': 'lévy'}, {'LEMMA': 'process'}]
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* [{'LOWER': 'levy'}, {'LEMMA': 'process'}]

2021년 2월 17일 (수) 00:46 기준 최신판

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말뭉치

  1. Informally speaking, a Lévy process is a random trajectory, generalizing the concept of Brownian motion, which may contain jump discontinuities.[1]
  2. The terms of this triplet suggest that a Lévy process can be seen as having three independent components: a linear drift, a Brownian motion, and a Lévy jump process, as described below.[2]
  3. Khintchine theorem suggests that every Lévy process is the sum of Brownian motion with drift and another independent random variable.[2]
  4. t≥0 be a free Lévy process (in law) affiliated with aW*-probability space (𝒜, τ) and with marginal distributions (μ t ).[3]
  5. t≥0 is termed the background driving Lévy process or the BDLP corresponding to Y; this is due to its role for processes of Ornstein–Uhlenbeck type (see ref. 16).[3]
  6. Now choose a free Lévy process (Z t ) affiliated with some W*-probability space (𝒜′, τ′) and corresponding to (X t ) as in Proposition 1.2.[3]
  7. In order to describe precisely the sum of jumps of a Lévy process, one needs to introduce the concept of Poisson random measures.[3]
  8. The distributions that can appear as the distribution of the instantaneous value of a homogeneous Lévy process are exactly those that have the property called infinite divisibility.[4]
  9. The fact that a Lévy process τ exists such that τ(1) is generalized inverse Gaussian distributed follows because these distributions are infinitely divisible, as mentioned in Section 1.[4]
  10. This model is the generalized hyperbolic Lévy process with a gamma mixing distribution.[4]
  11. A theory of the term structure of interest rates based on the hyperbolic Lévy process was developed in Eberlein and Raible (1999).[4]
  12. In this paper we compute equivalent martingale measures when the asset price returns are modelled by a Lévy process.[5]
  13. Moreover, the original process is still a Lévy process under this new probability and is called the Esscher transform of the original process.[5]
  14. and we consider a Lévy process with the following triplet (0,0, P ).[5]
  15. In this paper we have used a Lévy process to model asset price returns, which allow us to capture more stylized facts from real data.[5]
  16. This Demonstration shows the path of a symmetric stable Lévy process.[6]
  17. For , the stable Lévy process coincides with ordinary Brownian motion.[6]
  18. A strictly stable Lévy process can be viewed as a generalization of Brownian motion, and has the property for all , , has the same distribution as , for some index of stability , where .[6]
  19. This method allows the residual to submit to a Lévy process.[7]
  20. In this part, a dynamic asset distribution evolution process based on Lévy process and asymmetrical heteroscedastic process is built.[7]
  21. We assume that the residual in (1) submits to infinite-jump Lévy process and it is recorded as .[7]
  22. Infinite-jump Lévy process usually has three characteristic items, , , and , representing linear drift, Brownian motion diffusion, and jump, respectively.[7]
  23. More generally, it is possible to define the notion of a Lévy process with respect to a given filtered probability space .[8]
  24. In particular, if X is a Lévy process according to definition 1 then it is also a Lévy process with respect to its natural filtration .[8]
  25. The most common example of a Lévy process is Brownian motion, where is normally distributed with zero mean and variance independently of .[8]
  26. This is called a Cauchy process, which is a purely discontinuous Lévy process.[8]
  27. A typical model is obtained by considering finite dimensional linear stochastic SISO systems driven by a Levy process.[9]
  28. In this paper we consider a discrete-time version of this model driven by the increments of a Levy process, such a system will be called Levy system.[9]

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Spacy 패턴 목록

  • [{'LOWER': 'lévy'}, {'LEMMA': 'process'}]
  • [{'LOWER': 'levy'}, {'LEMMA': 'process'}]