Bayesian analysis of heavy-tailed market microstructure model and its application in stock markets

Yang, Xi; Peng, Hui; Qin, Yemei; Xie, Wei; Chen, Xiaohong

doi:10.1016/j.matcom.2015.06.006

Cited by 7 publications

(13 citation statements)

References 45 publications

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“…In the year 2007 Ives and Scandol proposed the utilization of Bayesian Analysis [18] which was then used in the methodology by Su and Peterman in 2012 [19], Ticknor in 2013 [20], and later in 2015 it was utilized as part of the methodology by Miao, Wang and Xu [21], Wang et al [22], and Peng et al [5].…”

Section: Artificial Intelligence Systemsmentioning

confidence: 99%

A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets

et al. 2018

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Knowledge-based decision support systems for financial management are an important part of investment plans. Investors are avoiding investing in traditional investment areas such as banks due to low return on investment. The stock exchange is one of the major areas for investment presently. Various non-linear and complex factors affect the stock exchange. A robust stock exchange forecasting system remains an important need. From this line of research, we evaluate the performance of a regression-based model to check the robustness over large datasets. We also evaluate the effect of top stock exchange markets on each other. We evaluate our proposed model on the top 4 stock exchanges—New York, London, NASDAQ and Karachi stock exchange. We also evaluate our model on the top 3 companies—Apple, Microsoft, and Google. A huge (Big Data) historical data is gathered from Yahoo finance consisting of 20 years. Such huge data creates a Big Data problem. The performance of our system is evaluated on a 1-step, 6-step, and 12-step forecast. The experiments show that the proposed system produces excellent results. The results are presented in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).

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Section: Artificial Intelligence Systemsmentioning

confidence: 99%

A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets

et al. 2018

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“…e estimation of the MM model is not straightforward, since the market liquidity appears both in the mean term and in the variance term of the model, and the surplus demand is also time-varying stochastic variable. In previous works, the non-linear Kalman filter and maximum likelihood method (see, e.g., [5][6][7][8][9][10]) were used to estimate the parameters of the MM model, but this kind of method has some shortcomings in dealing with strong non-Gaussian non-linear financial data. us, we consider using the MCMC method to improve estimation of the model.…”

Section: Introductionmentioning

confidence: 99%

“…Note that the Kalman filter-based simulation smoother algorithm (see, e.g., De Jong [15]; Koopman [16]; De Jong and Shephard [17]; and Durbin and Koopman [18]) and the auxiliary mixture sampler (see, e.g., Kim et al [19]; Omori et al [20]; Nakajima and Omori [21]; Delatola and Griffin [22]; and Jensen and Maheu [23]) cannot be directly applied to the MM model, since the market liquidity enters the mean term of return equation. To make an efficient posterior inference easier using WinBUGS (Windows version of Bayesian Analysis Using Gibbs Sampler, a statistical software for Bayesian analysis using MCMC methods), Xi et al [8] and Xi et al [10] developed a MCMC method to estimate a MM model. But the status of the chain after a huge number of steps is then used as a sample of the desired distribution, which may lead to complex desired distribution and an inaccurate estimate result.…”

Section: Introductionmentioning

confidence: 99%

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

Sun

Peng

Xie

2021

Discrete Dynamics in Nature and Society

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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.

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“…Some improvements of the market microstructure model and their estimation approaches as well as applications [20,21] were also presented. Although Peng et al [22,23] and Xi et al [24,25] proposed the generalized market microstructure (GMMS) models, which included jump component for capturing the low-frequency and large-amplitude abnormal vibrations of price, they did not consider the important property, namely, leverage effect. Recently, to explain essential characteristics of skewness and heavy tails, Xi et al [25] proposed the heavy-tailed market microstructure model based on Studentdistribution (MM-).…”

Section: Introductionmentioning

confidence: 99%

“…Although Peng et al [22,23] and Xi et al [24,25] proposed the generalized market microstructure (GMMS) models, which included jump component for capturing the low-frequency and large-amplitude abnormal vibrations of price, they did not consider the important property, namely, leverage effect. Recently, to explain essential characteristics of skewness and heavy tails, Xi et al [25] proposed the heavy-tailed market microstructure model based on Studentdistribution (MM-). However, all of the above market microstructure models are difficult to explain the asymmetry in the relation between volatility and price/return.…”

Section: Introductionmentioning

confidence: 99%

Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect

Yang

Peng

Qin

2016

Discrete Dynamics in Nature and Society

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The basic market microstructure model specifies that the price/return innovation and the volatility innovation are independent Gaussian white noise processes. However, the financial leverage effect has been found to be statistically significant in many financial time series. In this paper, a novel market microstructure model with leverage effects is proposed. The model specification assumed a negative correlation in the errors between the price/return innovation and the volatility innovation. With the new representations, a theoretical explanation of leverage effect is provided. Simulated data and daily stock market indices (Shanghai composite index, Shenzhen component index, and Standard and Poor’s 500 Composite index) via Bayesian Markov Chain Monte Carlo (MCMC) method are used to estimate the leverage market microstructure model. The results verify the effectiveness of the model and its estimation approach proposed in the paper and also indicate that the stock markets have strong leverage effects. Compared with the classical leverage stochastic volatility (SV) model in terms of DIC (Deviance Information Criterion), the leverage market microstructure model fits the data better.

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Bayesian analysis of heavy-tailed market microstructure model and its application in stock markets

Cited by 7 publications

References 45 publications

A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets

A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets

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

Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect

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