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Bayesian Analysis of Asymmetric Double Exponential Jump-Diffusion Model

Author: RenFeng
Tutor: ZhangTong
School: Tianjin University
Course: Technology Economics and Management
Keywords: Asymmetric double exponential jump diffusion model Bayesian analysis Markov chain Monte Carlo method MH algorithm
CLC: F830.9
Type: Master's thesis
Year: 2007
Downloads: 117
Quote: 0
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Abstract


Asymmetric double exponential jump diffusion model is a simple jump diffusion model proposed by Kou . The model for capital gains the partial spikes features and \Kou proposed the model when not on the model parameters to estimate , based on this , this paper Markov Monte Carlo (MCMC) method as a tool of the model were estimated . This paper summarizes the financial assets since the advent of the BS model continuous time model of development and the main results , discussion has continuous-time model parameter estimation method . The article focuses on the parameter estimation MCMC method , discussed the general process of using MCMC methods to estimate the model parameters . Finally, we use the MCMC methods to estimate the asymmetric double exponential jump diffusion model . The method is to use the Euler discretization asymmetric double exponential jump diffusion model , the likelihood function likelihood function as an approximation of the model parameters using the discrete process , then use the VC language developed a continuous-time model contains implicit variables the estimated MH algorithm based MCMC method , and the model parameters were estimated . MCMC methods for dealing with issues like non - symmetrical double exponential jump diffusion model containing hidden variables estimates of the multi- parameter model is very effective , at the same time show that the asymmetric double exponential jump diffusion model can reflect the assets estimated by the model parameters , the income distribution leptokurtic biased characteristics.

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CLC: > Economic > Fiscal, monetary > Finance, banking > Finance, banking theory > Financial market
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