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

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Yanhui Xi ◽  
Hui Peng ◽  
Yemei Qin
Keyword(s):  
Time Series ◽  
Market Microstructure ◽  
Financial Time Series ◽  
Gaussian White Noise ◽  
Composite Index ◽  
Theoretical Explanation ◽  
Model Specification ◽  
Leverage Effect ◽  
Financial Time ◽  
Microstructure Model

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.

A modeling approach to financial time series based on market microstructure model with jumps

2015 ◽  
Vol 29 ◽  
pp. 40-51 ◽  
Author(s):  
Hui Peng ◽  
Genshiro Kitagawa ◽  
Yoshiyasu Tamura ◽  
Yanhui Xi ◽  
Yemei Qin ◽  
...  
Keyword(s):  
Time Series ◽  
Market Microstructure ◽  
Financial Time Series ◽  
Financial Time ◽  
Modeling Approach ◽  
Microstructure Model

Fluctuation complexity of agent-based financial time series model by stochastic Potts system

2015 ◽  
Vol 26 (11) ◽  
pp. 1550123 ◽  
Author(s):  
Weijia Hong ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Dynamic System ◽  
Financial Time Series ◽  
Composite Index ◽  
Time Series Model ◽  
Agent Based ◽  
Financial Time ◽  
System A ◽  
Challenging Tasks ◽  
Financial Research

Financial market is a complex evolved dynamic system with high volatilities and noises, and the modeling and analyzing of financial time series are regarded as the rather challenging tasks in financial research. In this work, by applying the Potts dynamic system, a random agent-based financial time series model is developed in an attempt to uncover the empirical laws in finance, where the Potts model is introduced to imitate the trading interactions among the investing agents. Based on the computer simulation in conjunction with the statistical analysis and the nonlinear analysis, we present numerical research to investigate the fluctuation behaviors of the proposed time series model. Furthermore, in order to get a robust conclusion, we consider the daily returns of Shanghai Composite Index and Shenzhen Component Index, and the comparison analysis of return behaviors between the simulation data and the actual data is exhibited.

scholarly journals A Hypothesis Test Method for Detecting Multifractal Scaling, Applied to Bitcoin Prices

2020 ◽  
Vol 13 (5) ◽  
pp. 104
Author(s):  
Chuxuan Jiang ◽  
Priya Dev ◽  
Ross A. Maller
Keyword(s):  
Time Series ◽  
Heavy Tails ◽  
Financial Time Series ◽  
Hypothesis Test ◽  
Composite Index ◽  
Test Method ◽  
Scaling Functions ◽  
Financial Time ◽  
Nasdaq Composite Index ◽  
Multifractal Scaling

Multifractal processes reproduce some of the stylised features observed in financial time series, namely heavy tails found in asset returns distributions, and long-memory found in volatility. Multifractal scaling cannot be assumed, it should be established; however, this is not a straightforward task, particularly in the presence of heavy tails. We develop an empirical hypothesis test to identify whether a time series is likely to exhibit multifractal scaling in the presence of heavy tails. The test is constructed by comparing estimated scaling functions of financial time series to simulated scaling functions of both an iid Student t-distributed process and a Brownian Motion in Multifractal Time (BMMT), a multifractal processes constructed in Mandelbrot et al. (1997). Concavity measures of the respective scaling functions are estimated, and it is observed that the concavity measures form different distributions which allow us to construct a hypothesis test. We apply this method to test for multifractal scaling across several financial time series including Bitcoin. We observe that multifractal scaling cannot be ruled out for Bitcoin or the Nasdaq Composite Index, both technology driven assets.

scholarly journals Nonlinear Fluctuation Behavior of Financial Time Series Model by Statistical Physics System

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Wuyang Cheng ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Statistical Physics ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Contact Process ◽  
Composite Index ◽  
Return Time ◽  
Time Series Model ◽  
Financial Time

We develop a random financial time series model of stock market by one of statistical physics systems, the stochastic contact interacting system. Contact process is a continuous time Markov process; one interpretation of this model is as a model for the spread of an infection, where the epidemic spreading mimics the interplay of local infections and recovery of individuals. From this financial model, we study the statistical behaviors of return time series, and the corresponding behaviors of returns for Shanghai Stock Exchange Composite Index (SSECI) and Hang Seng Index (HSI) are also comparatively studied. Further, we investigate the Zipf distribution and multifractal phenomenon of returns and price changes. Zipf analysis and MF-DFA analysis are applied to investigate the natures of fluctuations for the stock market.

Volatility Modeling with Leverage Effect under Laplace Errors

2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhengjun Jiang ◽  
Weixuan Xia
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Financial Time Series ◽  
Laplace Distribution ◽  
Leverage Effect ◽  
Financial Returns ◽  
Financial Time ◽  
Error Distributions ◽  
Time Series Comparison ◽  
Unconditional Variance

AbstractThis paper discusses four GARCH-type models (A-GARCH, NA-GARCH, GJR-GARCH, and E-GARCH) in representing volatility of financial returns with leverage effect. In these models, errors are assumed to follow a Laplace distribution in order to deal with the typical leptokurtic feature of financial returns. The properties of these models are analyzed theoretically in terms of unconditional variance, kurtosis, autocorrelation function and news impact, and are further examined in the applications to real financial time series. Comparison is made with other choices of error distributions such as normal, Student-5, and Student-7 distributions, respectively. We also conduct residual analyses to justify the choice of error distributions and find that Laplace-E-GARCH model still performs very well. Our main purpose is to study and compare the proposed models’ relative adequacies and underlying limitations.

scholarly journals Predicting the Direction Movement of Financial Time Series Using Artificial Neural Network and Support Vector Machine

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Muhammad Ali ◽  
Dost Muhammad Khan ◽  
Muhammad Aamir ◽  
Amjad Ali ◽  
Zubair Ahmad
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Composite Index ◽  
Support Vector ◽  
Financial Time ◽  
Svm Model

Prediction of financial time series such as stock and stock indexes has remained the main focus of researchers because of its composite nature and instability in almost all of the developing and advanced countries. The main objective of this research work is to predict the direction movement of the daily stock prices index using the artificial neural network (ANN) and support vector machine (SVM). The datasets utilized in this study are the KSE-100 index of the Pakistan stock exchange, Korea composite stock price index (KOSPI), Nikkei 225 index of the Tokyo stock exchange, and Shenzhen stock exchange (SZSE) composite index for the last ten years that is from 2011 to 2020. To build the architect of a single layer ANN and SVM model with linear, radial basis function (RBF), and polynomial kernels, different technical indicators derived from the daily stock trading, such as closing, opening, daily high, and daily low prices and used as input layers. Since both the ANN and SVM models were used as classifiers; therefore, accuracy and F-score were used as performance metrics calculated from the confusion matrix. It can be concluded from the results that ANN performs better than SVM model in terms of accuracy and F-score to predict the direction movement of the KSE-100 index, KOSPI index, Nikkei 225 index, and SZSE composite index daily closing price movement.

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.

Volatility Behaviors of Financial Time Series by Percolation System on Sierpinski Carpet Lattice

2015 ◽  
Vol 14 (02) ◽  
pp. 1550015 ◽  
Author(s):  
Anqi Pei ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Moving Average ◽  
Composite Index ◽  
Stock Index ◽  
Sierpinski Carpet ◽  
Financial Model ◽  
Financial Time ◽  
Arfima Model ◽  
Sierpiński Carpet

The financial time series is simulated and investigated by the percolation system on the Sierpinski carpet lattice, where percolation is usually employed to describe the behavior of connected clusters in a random graph, and the Sierpinski carpet lattice is a graph which corresponds the fractal — Sierpinski carpet. To study the fluctuation behavior of returns for the financial model and the Shanghai Composite Index, we establish a daily volatility measure — multifractal volatility (MFV) measure to obtain MFV series, which have long-range cross-correlations with squared daily return series. The autoregressive fractionally integrated moving average (ARFIMA) model is used to analyze the MFV series, which performs better when compared to other volatility series. By a comparative study of the multifractality and volatility analysis of the data, the simulation data of the proposed model exhibits very similar behaviors to those of the real stock index, which indicates somewhat rationality of the model to the market application.

scholarly journals A Hybrid Approach Integrating Multiple ICEEMDANs, WOA, and RVFL Networks for Economic and Financial Time Series Forecasting

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Jiang Wu ◽  
Tengfei Zhou ◽  
Taiyong Li
Keyword(s):  
Time Series ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Hybrid Approach ◽  
Composite Index ◽  
Ensemble Prediction ◽  
Functional Link ◽  
Financial Time ◽  
Whale Optimization ◽  
Original Time

The fluctuations of economic and financial time series are influenced by various kinds of factors and usually demonstrate strong nonstationary and high complexity. Therefore, accurately forecasting economic and financial time series is always a challenging research topic. In this study, a novel multidecomposition and self-optimizing hybrid approach integrating multiple improved complete ensemble empirical mode decompositions with adaptive noise (ICEEMDANs), whale optimization algorithm (WOA), and random vector functional link (RVFL) neural networks, namely, MICEEMDAN-WOA-RVFL, is developed to predict economic and financial time series. First, we employ ICEEMDAN with random parameters to separate the original time series into a group of comparatively simple subseries multiple times. Second, we construct RVFL networks to individually forecast each subseries. Considering the complex parameter settings of RVFL networks, we utilize WOA to search the optimal parameters for RVFL networks simultaneously. Then, we aggregate the prediction results of individual decomposed subseries as the prediction results of each decomposition, respectively, and finally integrate these prediction results of all the decompositions as the final ensemble prediction results. The proposed MICEEMDAN-WOA-RVFL remarkably outperforms the compared single and ensemble benchmark models in terms of forecasting accuracy and stability, as demonstrated by the experiments conducted using various economic and financial time series, including West Texas Intermediate (WTI) crude oil prices, US dollar/Euro foreign exchange rate (USD/EUR), US industrial production (IP), and Shanghai stock exchange composite index (SSEC).

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