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

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 e...

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Autores principales:	Sun Yapeng, Peng Hui, Xie Wenbiao
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/8e2176ccfc2041a78ce45574ec6054ce
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spelling	oai:doaj.org-article:8e2176ccfc2041a78ce45574ec6054ce2021-11-08T02:37:28ZA Fast and Efficient Markov Chain Monte Carlo Method for Market Microstructure Model1607-887X10.1155/2021/5523468https://doaj.org/article/8e2176ccfc2041a78ce45574ec6054ce2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5523468https://doaj.org/toc/1607-887XThe 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.Sun YapengPeng HuiXie WenbiaoHindawi LimitedarticleMathematicsQA1-939ENDiscrete Dynamics in Nature and Society, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mathematics QA1-939
spellingShingle	Mathematics QA1-939 Sun Yapeng Peng Hui Xie Wenbiao A Fast and Efficient Markov Chain Monte Carlo Method for Market Microstructure Model
description	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.
format	article
author	Sun Yapeng Peng Hui Xie Wenbiao
author_facet	Sun Yapeng Peng Hui Xie Wenbiao
author_sort	Sun Yapeng
title	A Fast and Efficient Markov Chain Monte Carlo Method for Market Microstructure Model
title_short	A Fast and Efficient Markov Chain Monte Carlo Method for Market Microstructure Model
title_full	A Fast and Efficient Markov Chain Monte Carlo Method for Market Microstructure Model
title_fullStr	A Fast and Efficient Markov Chain Monte Carlo Method for Market Microstructure Model
title_full_unstemmed	A Fast and Efficient Markov Chain Monte Carlo Method for Market Microstructure Model
title_sort	fast and efficient markov chain monte carlo method for market microstructure model
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/8e2176ccfc2041a78ce45574ec6054ce
work_keys_str_mv	AT sunyapeng afastandefficientmarkovchainmontecarlomethodformarketmicrostructuremodel AT penghui afastandefficientmarkovchainmontecarlomethodformarketmicrostructuremodel AT xiewenbiao afastandefficientmarkovchainmontecarlomethodformarketmicrostructuremodel AT sunyapeng fastandefficientmarkovchainmontecarlomethodformarketmicrostructuremodel AT penghui fastandefficientmarkovchainmontecarlomethodformarketmicrostructuremodel AT xiewenbiao fastandefficientmarkovchainmontecarlomethodformarketmicrostructuremodel
_version_	1718443020670468096

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

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