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On first passage times of a hyper-exponential jump diffusion process

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Title: On first passage times of a hyper-exponential jump diffusion process
Authors: Cai, Ning1 ningcai@ust.hk
Source: Operations Research Letters. Mar2009, Vol. 37 Issue 2, p127-134. 8p.
Subject Terms: EXPONENTS, DIFFUSION, EXPONENTIAL functions, NUMERICAL roots
Abstract: Abstract: We investigate some important probabilistic properties relating to the first passage time of a hyper-exponential jump diffusion process, including its finiteness, expectation, conditional memorylessness, and conditional independence. Moreover, the joint distribution of the first passage time and the overshoot is studied from a primal–dual perspective. [Copyright &y& Elsevier]
Copyright of Operations Research Letters is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: On first passage times of a hyper-exponential jump diffusion process
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  Data: <searchLink fieldCode="JN" term="%22Operations+Research+Letters%22">Operations Research Letters</searchLink>. Mar2009, Vol. 37 Issue 2, p127-134. 8p.
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  Data: Abstract: We investigate some important probabilistic properties relating to the first passage time of a hyper-exponential jump diffusion process, including its finiteness, expectation, conditional memorylessness, and conditional independence. Moreover, the joint distribution of the first passage time and the overshoot is studied from a primal–dual perspective. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Operations Research Letters is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.orl.2009.01.002
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              Text: Mar2009
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