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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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/5523468 |
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| author | Sun Yapeng Peng Hui Xie Wenbiao |
| author_facet | Sun Yapeng Peng Hui Xie Wenbiao |
| author_sort | Sun Yapeng |
| collection | DOAJ |
| 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 |
| id | doaj-art-8e2176ccfc2041a78ce45574ec6054ce |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-8e2176ccfc2041a78ce45574ec6054ce2025-02-03T06:43:50ZengWileyDiscrete Dynamics in Nature and Society1607-887X2021-01-01202110.1155/2021/5523468A Fast and Efficient Markov Chain Monte Carlo Method for Market Microstructure ModelSun Yapeng0Peng Hui1Xie Wenbiao2School of AutomationSchool of AutomationSchool of Electrical & Information EngineeringThe 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.http://dx.doi.org/10.1155/2021/5523468 |
| spellingShingle | Sun Yapeng Peng Hui Xie Wenbiao A Fast and Efficient Markov Chain Monte Carlo Method for Market Microstructure Model Discrete Dynamics in Nature and Society |
| title | 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_short | 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 |
| url | http://dx.doi.org/10.1155/2021/5523468 |
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