An adaptive modeling and asset allocation approach to financial markets based on discrete microstructure model

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

doi:10.1016/j.asoc.2016.02.034

Cited by 4 publications

(8 citation statements)

References 33 publications

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“…Iino and Ozaki [4] treated the market liquidity 1/λ t and the surplus demand ϕ t as stochastic processes and proposed a market microstructure model, which consists of a set of continuous time stochastic differential equations. Peng et al (see e.g., Peng et al [5]; Peng et al [6]; Peng et al [7]; Xi et al [8]; Qin et al [9]; Xi et al [10]), also extended the market microstructure model in continuous-time domain and discretetime domain. More generally, in this paper, we build the continuous-time market microstructure model as follows:…”

Section: Preliminary: Problem Descriptionmentioning

confidence: 99%

“…is methodology was implemented in, e.g., Peng et al [5], Peng et al [6], Peng et al [7], and Qin et al [9]. Besides stochastic volatility, another prominent characteristic in typical financial time series is the existence of outliers.…”

Section: Preliminary: Problem Descriptionmentioning

confidence: 99%

“…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%

“…In this paper, unlike the non-linear Kalman filter and maximum likelihood method-based estimation way for the MM model (see, e.g., Peng et al [5]; Peng et al [6]; Peng et al [7]; and Qin et al [9]), we develop an efficient MCMC method to estimate the market microstructure model. 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.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Preliminary: Problem Descriptionmentioning

confidence: 99%

Section: Preliminary: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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show abstract

“…To overcome the divergence issue of EKF-based single-beacon tracking methods, this paper will propose an adaptive Kalman filter (AKF)-based single-beacon underwater tracking algorithm. Although the AKF has been intensively investigated to reduce the influence of process noise covariance matrix and measurement noise covariance matrix errors in fields like integrated navigation, economic projection, and chemistry, to the best knowledge of the authors, little literature has been published implementing AKF for single-beacon underwater tracking, especially based on the model with unknown ESV [23,24,25]. Through simulation and field data, the estimation results between using the proposed adaptive algorithm and the traditional EKF will be compared.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Kalman Filter-Based Single-Beacon Underwater Tracking with Unknown Effective Sound Velocity

Deng

Qin

et al. 2018

Sensors

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In the single-beacon underwater tracking system, vehicles rely on slant range measurements from an acoustic beacon to bound errors accumulated by dead reckoning. Ranges are usually obtained based on a presumed known effective sound velocity (ESV). Since the ESV is difficult to determine accurately, traditional methods suffer from large positioning error. By treating the unknown ESV as a state variable, a novel single-beacon tracking model (the so called “5-sv” model) and an extended Kalman filter (EKF)-based solution method have been discussed to solve the problem of ESV estimation. However, due to the uncertainty of underwater acoustic propagation, the probabilistic characteristics of the ESV uncertainty and acoustic measurement noise are unknown and varying both with time and location. EKF, which runs with presupposed noise parameters, cannot describe the practical noise specifications. To overcome the divergence issue of EKF-based single-beacon tracking methods, this paper proposes an adaptive Kalman filter-based single-beacon tracking algorithm which employs the “5-sv” model as the baseline model. Through numerical examples using simulated and field data, both the filter and smoother results show that while implementing the proposed algorithm, the tracking accuracy can be significantly improved, and the estimated noise parameter agrees well with its true value.

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Simplex filter: A novel heuristic filter for nonlinear systems state estimation

Nobahari

Zandavi

Mohammadkarimi

2016

Applied Soft Computing

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An adaptive modeling and asset allocation approach to financial markets based on discrete microstructure model

Cited by 4 publications

References 33 publications

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

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

Adaptive Kalman Filter-Based Single-Beacon Underwater Tracking with Unknown Effective Sound Velocity

Simplex filter: A novel heuristic filter for nonlinear systems state estimation

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