The efficient Markov chain Monte Carlo estimation of the stochastic volatility model with changing regimes

1998 ◽  
Author(s):  
An An Chen
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Stochastic Volatility ◽  
Stochastic Volatility Model ◽  
Volatility Model ◽  
Monte Carlo Estimation

scholarly journals Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Nima Nonejad
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Stochastic Volatility ◽  
Stochastic Volatility Model ◽  
Time Series Models ◽  
Unobserved Component ◽  
Volatility Model ◽  
Component Time

AbstractThis paper details particle Markov chain Monte Carlo (PMCMC) techniques for analysis of unobserved component time series models using several economic data sets. The objective of this paper is to explain the basics of the methodology and provide computational applications that justify applying PMCMC in practice. For instance, we use PMCMC to estimate a stochastic volatility model with a leverage effect, Student-t distributed errors or serial dependence. We also model time series characteristics of monthly US inflation rate by considering a heteroskedastic ARFIMA model where heteroskedasticity is specified by means of a Gaussian stochastic volatility process.

scholarly journals Posterior-based proposals for speeding up Markov chain Monte Carlo

2019 ◽  
Vol 6 (11) ◽  
pp. 190619 ◽  
Author(s):  
C. M. Pooley ◽  
S. C. Bishop ◽  
A. Doeschl-Wilson ◽  
G. Marion
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Latent Variables ◽  
Latent Variable ◽  
Directed Acyclic Graphs ◽  
Stochastic Volatility Model ◽  
Mcmc Methods ◽  
Volatility Model ◽  
Large Joint

Markov chain Monte Carlo (MCMC) is widely used for Bayesian inference in models of complex systems. Performance, however, is often unsatisfactory in models with many latent variables due to so-called poor mixing, necessitating the development of application-specific implementations. This paper introduces ‘posterior-based proposals' (PBPs), a new type of MCMC update applicable to a huge class of statistical models (whose conditional dependence structures are represented by directed acyclic graphs). PBPs generate large joint updates in parameter and latent variable space, while retaining good acceptance rates (typically 33%). Evaluation against other approaches (from standard Gibbs/random walk updates to state-of-the-art Hamiltonian and particle MCMC methods) was carried out for widely varying model types: an individual-based model for disease diagnostic test data, a financial stochastic volatility model, a mixed model used in statistical genetics and a population model used in ecology. While different methods worked better or worse in different scenarios, PBPs were found to be either near to the fastest or significantly faster than the next best approach (by up to a factor of 10). PBPs, therefore, represent an additional general purpose technique that can be usefully applied in a wide variety of contexts.

scholarly journals VARIANCE AND VOLATILITY SWAPS UNDER A TWO-FACTOR STOCHASTIC VOLATILITY MODEL WITH REGIME SWITCHING

2019 ◽  
Vol 22 (04) ◽  
pp. 1950009
Author(s):  
XIN-JIANG HE ◽  
SONG-PING ZHU
Keyword(s):  
Markov Chain ◽  
Characteristic Function ◽  
Stochastic Volatility ◽  
Regime Switching ◽  
Numerical Experiments ◽  
Stochastic Volatility Model ◽  
Heston Model ◽  
Volatility Model ◽  
Significant Difference ◽  
Cir Process

In this paper, the pricing problem of variance and volatility swaps is discussed under a two-factor stochastic volatility model. This model can be treated as a two-factor Heston model with one factor following the CIR process and another characterized by a Markov chain, with the motivation originating from the popularity of the Heston model and the strong evidence of the existence of regime switching in real markets. Based on the derived forward characteristic function of the underlying price, analytical pricing formulae for variance and volatility swaps are presented, and numerical experiments are also conducted to compare swap prices calculated through our formulae and those obtained under the Heston model to show whether the introduction of the regime switching factor would lead to any significant difference.

scholarly journals Markov chain Monte Carlo estimation of quantiles

2014 ◽  
Vol 8 (2) ◽  
pp. 2448-2478 ◽  
Author(s):  
Charles R. Doss ◽  
James M. Flegal ◽  
Galin L. Jones ◽  
Ronald C. Neath
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Estimation
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