فهرست مقالات Markov chain Monte Carlo Simulation

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  1 - Oil Price estimating Under Dynamic Economic Models Using Markov Chain Monte Carlo Simulation Approach
  Kianoush Fathi Vajargah Hossein Eslami Mofid Abadi Ebrahim Abbasi
  10.22034/amfa.2020.1902265.1446
  This study, attempts to estimate and compare four different models of jump-diffusion class combined with stochastic volatility that are based on stochastic differential equations, and their parameters latent variables are estimated by Markov chain Monte Carlo (MCMC) met چکیده کامل

  This study, attempts to estimate and compare four different models of jump-diffusion class combined with stochastic volatility that are based on stochastic differential equations, and their parameters latent variables are estimated by Markov chain Monte Carlo (MCMC) methods. In the Stochastic Volatility with Correlated Jumps (SVCJ) model, volatilities are scholastic, and the term jump is added to both scholastic prices and volatilities. The results of this study showed that this model is more efficient than the others are, as it provides a significantly better fit to the data, and therefore, corrects the shortcomings of the previous models and that it is closer to the actual market prices. Therefore, our estimating model under the Monte Carlo simulation allows an analysis on oil prices during certain times in the periods of tension and shock in the oil market پرونده مقاله
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  2 - The Lindley-Lindley Distribution: Characterizations, Copula, Properties, Bayesian and Non-Bayesian Estimation
  Christophe Chesneau Haitham Yousof G. Hamedani Mohamed Ibrahim
  10.30495/ijm2c.2021.681361
  ‎A new continuous distribution called Lindley-Lindley distribution is defined‎ ‎and studied‎. ‎Relevant mathematical properties are derived‎. ‎We‎ ‎present three characterizations of the new distribution based on the truncated moments چکیده کامل

  ‎A new continuous distribution called Lindley-Lindley distribution is defined‎ ‎and studied‎. ‎Relevant mathematical properties are derived‎. ‎We‎ ‎present three characterizations of the new distribution based on the truncated moments of certain functions of the random variable;‎ ‎the hazard function and in terms of the conditional expectation of a‎‎function of the random variable‎. ‎Some new bivariate type distributions using‎ ‎Farlie Gumbel Morgenstern copula‎, ‎modified Farlie Gumbel Morgenstern‎ ‎copula and Clayton copula are introduced‎. ‎The main‎ ‎justification of this paper is to show how different frequentist estimators‎ ‎of the new model perform for different sample sizes and different parameter‎‎values and to provide a guideline for choosing the best estimation method‎ ‎for the parameters of the proposed model‎. ‎The unknown parameters of the new‎ ‎distribution are estimated using the maximum likelihood‎, ‎ordinary‎ ‎least squares‎, ‎Cramer-Von-Mises‎, ‎weighted least squares and Bayesian methods‎. ‎The obtained estimators are compared using‎ ‎Markov Chain Monte Carlo simulations and observed that Bayesian estimators‎ ‎are generally more efficient than the other estimators. پرونده مقاله