scholarly journals Embarrassingly parallel sequential Markov-chain Monte Carlo for large sets of time series

2016 ◽  
Vol 9 (4) ◽  
pp. 497-508 ◽  
Author(s):  
Roberto Casarin ◽  
Radu V. Craiu ◽  
Fabrizio Leisen
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Large Sets

scholarly journals A Fast and Efficient Markov Chain Monte Carlo Method for Market Microstructure Model

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Sun Yapeng ◽  
Peng Hui ◽  
Xie Wenbiao
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Market Microstructure ◽  
Financial Time Series ◽  
Market Liquidity ◽  
Time Varying ◽  
Financial Time ◽  
Simulation Smoother

The non-linear market microstructure (MM) model for financial time series modeling is a flexible stochastic volatility model with demand surplus and market liquidity. The estimation of the model is difficult, since the unobservable surplus demand is a time-varying stochastic variable in the return equation, and the market liquidity arises both in the mean term and in the variance term of the return equation in the MM model. A fast and efficient Markov Chain Monte Carlo (MCMC) approach based on an efficient simulation smoother algorithm and an acceptance-rejection Metropolis–Hastings algorithm is designed to estimate the non-linear MM model. Since the simulation smoother algorithm makes use of the band diagonal structure and positive definition of Hessian matrix of the logarithmic density, it can quickly draw the market liquidity. In addition, we discuss the MM model with Student-t heavy tail distribution that can be utilized to address the presence of outliers in typical financial time series. Using the presented modeling method to make analysis of daily income of the S&P 500 index through the point forecast and the density forecast, we find clear support for time-varying volatility, volatility feedback effect, market microstructure theory, and Student-t heavy tails in the financial time series. Through this method, one can use the estimated market liquidity and surplus demand which is much smoother than the strong stochastic return process to assist the transaction decision making in the financial market.

Time-Series Fatigue Damage States Forecasting and Probabilistic Environmental Fatigue Life Prediction Using Markov-Chain-Monte-Carlo Techniques

2019 ◽  
Author(s):  
Jae Phil Park ◽  
Subhasish Mohanty ◽  
Chi Bum Bahn
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Markov Chain ◽  
Fatigue Life ◽  
Markov Chain Monte Carlo ◽  
End Of Life ◽  
Time Evolution ◽  
Life Data ◽  
Damage States ◽  
Strain Stress

Abstract In general, probabilistic fatigue life estimation were performed using Weibull analysis and end-of-life fatigue data. This is basically using traditional strain/stress ∼ life (S∼N) data. Although it is always better to estimate the reliability of a component based on probabilistic evaluation of end-of-life data, it may not be always possible to conduct hundreds of costly and time-consuming fatigue experiments for each and every different loading case. The strain/stress ∼ life data are mostly related to push-pull type symmetric (R = −1) loading cases and the traditional Weibull probabilistic model are based on the associated end-of-life data. However, the Weibull approach doesn’t depend on the time evolution of damage but is just based on end-of-life data. It is our assumption that for a given loading and environment, the time-evolution based probabilistic risk assessment (PRA) and reliability model would produce more accurate results compared to a PRA model simply based on the end-of-life data obtained under a different loading conditions. However, the time-evolution based PRA for a particular loading and environment requires hundreds of fatigue tests, which might not always be possible to perform due to the high cost and time requirements. To overcome these issues, we propose the use of Markov-Chain-Monte-Carlo (MCMC) techniques for time series or time-evolution prediction of damage states under a particular loading and environment (e.g. in this case, high-temperature PWR coolant-water condition) condition. Then, the probabilistic fatigue life can be estimated on the basis of the simulated scatter band in damage states for a given failure criteria. In this paper, we discuss the MCMC model based probabilistic damage state estimation and associated probabilistic fatigue life of 316 stainless steel subjected to cyclic loading at 300 °C in-air and PWR-coolant-water conditions.

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.

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