[PDF] Top 20 Estimation of stochastic volatility models via Monte Carlo maximum likelihood
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Estimation of stochastic volatility models via Monte Carlo maximum likelihood
... the Monte Carlo likelihood (MCL) method of estimating stochastic volatility (SV) models is implemented ...Gaussian likelihood function via the prediction error ... See full document
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Filtering and likelihood estimation of latent factor jump diffusions with an application to stochastic volatility models
... Filtering and likelihood estimation of latent factor jump-diffusions with an application to stochastic volatility models esposito, francesco paolo and cummins, mark dublin city universit[r] ... See full document
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Bayesian Estimation of Non Gaussian Stochastic Volatility Models
... [4] fitted a student-t-distribution and a Generalized Error Distribution (GED) as well as a normal distribution to the error distribution in the SV model by using the simulated maximum likelihood method ... See full document
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Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics
... noise-contrastive estimation (NCE, red squares), Monte Carlo maximum likelihood (IS, blue circles) and score matching (SM, black ...the estimation error at ...noise-contrastive ... See full document
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Simulated maximum likelihood for general stochastic volatility models: a change of variable approach
... fitting stochastic volatility (SV) models to price return data has ...Parameter estimation in such models is made difficult by the presence of a latent volatility ...the ... See full document
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Modelling stochastic volatility with leverage and jumps: a simulated maximum likelihood approach via particle filtering
... the stochastic volatility literature was made by Harvey and Shephard ...Quasi Maximum Likelihood (QML) technique used in parameter estimation in standard SV models (see Harvey et ... See full document
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Estimation of Multivariate Sample Selection Models via a Parameter Expanded Monte Carlo EM Algorithm
... parameter-expanded Monte Carlo EM (PX-MCEM) algorithm to perform maximum likelihood estimation in a multivariate sample selection ...of estimation, the proposed algorithm does ... See full document
7
Employing a Monte Carlo algorithm in Newton-type methods for restricted maximum likelihood estimation of genetic parameters
... by Monte Carlo (MC) expectation maximization (EM) restricted maximum likelihood (REML) is computationally efficient for large data sets and complex linear mixed effects ...generated via ... See full document
8
Maximum likelihood parameter estimation for latent variable models using sequential Monte Carlo
... the volatility estimated by its posterior mean under the empirical distribution of the final particle set – as the distri- bution over θ converges to a point mass at the maximum ... See full document
5
Genetic Algorithm Sequential Monte Carlo Methods For Stochastic Volatility And Parameter Estimation
... instantaneous volatility of an asset is perfectly predictable. In practice volatility varies ...complex models with two stochastic variables; the stock price and its ...The stochastic ... See full document
6
Maximum likelihood estimation for stochastic Lotka–Volterra model with jumps
... the maximum likelihood estimation for the drift coefficients based on continuous time ...The likelihood function and explicit estimator are derived by using semimartingale ... See full document
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Maximum likelihood estimation for directional conditionally autoregressive models
... spatial models is developed using different weights given to neighbors in dif- ferent ...anisotropy. Maximum likelihood estimators are derived and shown to be consistent under some regularity ... See full document
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Mean exit times and the multilevel Monte Carlo method
... Of course there are several other general approaches to mean exit time computation, each having their own advantages and disadvantages [31], and in future work it would be of great interest to compare this multilevel ... See full document
17
Multiple particle filtering for tracking wireless agents via Monte Carlo likelihood approximation
... the likelihood, which is required to com- pute the weight update, for example ...the likelihood is ...the likelihood is nonseparable for the following two reasons: first, the necessity of considering ... See full document
20
Maximum Likelihood Estimation of Factored Regular Deterministic Stochastic Languages
... co-emission product determines the probabilities each L ∈ C assigns to strings. Essentially, the co- emission product of these PDFAs factor the prob- abilities each L ∈ C assigns to strings. Each L is a complex joint ... See full document
12
Maximum likelihood estimation of a stochastic frontier model with residual covariance
... the maximum likelihood approach for estimating a stochastic production frontier ...the maximum likelihood estimation procedure suggested in Cliff and Ord (1973) and Kapoor, ... See full document
12
Computational approaches for maximum likelihood estimation for nonlinearmixed models.
... eects models have become a routine tool in biomedical appli- cations to represent repeated measurement data on each of several ...eects models have seen widespread use is popula- tion pharmacokinetics, ... See full document
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Estimation of Admixture Proportions: A Likelihood-Based Approach Using Markov Chain Monte Carlo
... account drift since the admixture event, variation caused by sampling, and uncertainty in the estimation of the ancestral allele frequencies. The method is tested on simulated data sets and then applied to a human ... See full document
16
Methods to account for uncertainties in exposure assessment in studies of environmental exposures
... the models with no adjustment for dose uncertainties in studies of thyroid cancer after the Chornobyl accident ...exposure estimation errors is required, MCML and BMA methods require less information be- ... See full document
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A fully Bayesian approach to shape estimation of objects from tomography data using MFS forward solutions
... chain Monte Carlo (MCMC) method presented – for a detailed theoretical discussion of the MCMC method see, for exam- ple, Geyer (2011) and Brooks et ... See full document
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