scholarly journals Research on Volatility of Return of Chinese Stock-Market Based on Generalized Hyperbolic Distribution Family

2021 ◽  
Vol 9 ◽  
pp. 1-7
Author(s):  
Wu Libin ◽  
Liu Shengyu ◽  
Gao Jun
Keyword(s):  
Time Series ◽  
Composite Index ◽  
Parametric Modeling ◽  
Return Rate ◽  
Statistical Characteristics ◽  
Chinese Stock Market ◽  
Generalized Hyperbolic Distribution ◽  
Full Account ◽  
Volatility Model ◽  
Distribution Family

Financial time series often present a nonlinear characteristics, and the distribution of financial data often show fat tail and asymmetry, but this don’t match with the standpoint that time series obey normal distribution of return on assets, etc, which is considered by linear parametric modeling in the traditional linear framework. This paper has a systematic introduction of the definitions of GH distribution family and related statistical characteristics, which is based on reviewing the basic properties of the ARCH/GARCH model family and a common distribution of its disturbance. And select the Shanghai Composite Index and the Shanghai and Shenzhen (CSI) 300 index daily return rate index to estimate volatility model. GH distribution is used for further fitting to disturbance. This is done after take full account of the effective extraction of the model for the disturbance distribution information. The results show that the GH distribution can effectively fitting residuals distribution of the volatility models about series on return rate.

Empirical Study on the Non-Periodic Cycles of Fractal Time Series: Examples of Indices of the Chinese Stock Market

2015 ◽  
Vol 734 ◽  
pp. 637-641
Author(s):  
Yang Li ◽  
Wei Yu Zhang ◽  
Yong Wei ◽  
Jin Hui Sun
Keyword(s):  
Time Series ◽  
Random Walk ◽  
Stock Market ◽  
Composite Index ◽  
Chinese Stock Market ◽  
Biased Random Walk ◽  
Fractal Time ◽  
Study Support ◽  
Two Indices ◽  
Periodic Cycles

By R/S analysis, non-periodic cycles of the SSE Composite Index and SZSE Composite Index are studied in this paper. With a different determinant method from the previous works about fractal behaviors of the Chinese stock market, the empirical results obtained in this study support the non-periodic cycle results but with different values. With more data available, the analysis shows that the two indices follow a biased random walk with two non-periodic cycles, one about 4.5 years and another about 9 years, which may be tied to the economic and politic cycles.

scholarly journals Big Data Business Actual Analysis: Stock Price Prediction Based on Time Series Model

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Aiwen Rui
Keyword(s):  
Time Series ◽  
Stock Price ◽  
Minimum Principle ◽  
Composite Index ◽  
Characteristic Root ◽  
Return Rate ◽  
Stock Price Prediction ◽  
Sparse Coefficient ◽  
Model Expression ◽  
Return Data

This paper selects the daily closing price data of the Shanghai Composite Index from January 1, 2016 to December 31, 2017, excluding holidays, and preprocesses the data. After taking the logarithm and converting it into the rate of return data, the first-order difference is performed to make it into a stable time series, and then the ARMA(p,q) model is constructed. Through parameter significance test, residual test and characteristic root test, according to the minimum principle of AIC, the optimal model is finally determined to be ARMA(2,5) of sparse coefficient, and the expression of the model is obtained. The GARCH(1,1) model is established for the residual of ARMA(2,5), and the model expression is obtained. In order to directly predict the return rate of the Shanghai Composite Index, the ARIMA(2,1,5) model of the sparse coefficient is constructed for the return rate of the Shanghai Composite Index, and the model expression is obtained. By predicting the Shanghai Composite Index return data on January 2, 2018, it is found that the prediction error of the model is small, and it can be used for subsequent predictions.

scholarly journals SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 602
Author(s):  
Luisa Martínez-Acosta ◽  
Juan Pablo Medrano-Barboza ◽  
Álvaro López-Ramos ◽  
John Freddy Remolina López ◽  
Álvaro Alberto López-Lambraño
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Autocorrelation Function ◽  
Information Criterion ◽  
Monthly Rainfall ◽  
Statistical Characteristics ◽  
Rainfall Time Series ◽  
River Watershed ◽  
Statistical Measures ◽  
Sarima Models

Seasonal Auto Regressive Integrative Moving Average models (SARIMA) were developed for monthly rainfall time series. Normality of the rainfall time series was achieved by using the Box Cox transformation. The best SARIMA models were selected based on their autocorrelation function (ACF), partial autocorrelation function (PACF), and the minimum values of the Akaike Information Criterion (AIC). The result of the Ljung–Box statistical test shows the randomness and homogeneity of each model residuals. The performance and validation of the SARIMA models were evaluated based on various statistical measures, among these, the Student’s t-test. It is possible to obtain synthetic records that preserve the statistical characteristics of the historical record through the SARIMA models. Finally, the results obtained can be applied to various hydrological and water resources management studies. This will certainly assist policy and decision-makers to establish strategies, priorities, and the proper use of water resources in the Sinú river watershed.

scholarly journals On the Connection between the GEP Performances and the Time Series Properties

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1853
Author(s):  
Alina Bărbulescu ◽  
Cristian Ștefan Dumitriu
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Functional Form ◽  
Financial Time Series ◽  
Gene Expression Programming ◽  
Parametric Models ◽  
Statistical Characteristics ◽  
Data Series ◽  
Financial Time ◽  
Data Generating Process

Artificial intelligence (AI) methods are interesting alternatives to classical approaches for modeling financial time series since they relax the assumptions imposed on the data generating process by the parametric models and do not impose any constraint on the model’s functional form. Even if many studies employed these techniques for modeling financial time series, the connection of the models’ performances with the statistical characteristics of the data series has not yet been investigated. Therefore, this research aims to study the performances of Gene Expression Programming (GEP) for modeling monthly and weekly financial series that present trend and/or seasonality and after the removal of each component. It is shown that series normality and homoskedasticity do not influence the models’ quality. The trend removal increases the models’ performance, whereas the seasonality elimination results in diminishing the goodness of fit. Comparisons with ARIMA models built are also provided.

scholarly journals Modeling the Covid-19 Epidemic Using Time Series Econometrics

2020 ◽  
Author(s):  
Adam Goliński ◽  
Peter Spencer
Keyword(s):  
Time Series ◽  
Logistic Model ◽  
Classical Model ◽  
Realistic Model ◽  
Statistical Characteristics ◽  
Asian Countries ◽  
Time Series Econometrics ◽  
Case Count ◽  
The Uk ◽  
Empirical Stance

AbstractThe classic ‘logistic’ model has provided a realistic model of the behavior of Covid-19 in China and many East Asian countries. Once these countries passed the peak, the daily case count fell back, mirroring its initial climb in a symmetric way, just as the classic model predicts. However, in Italy and Spain, and now the UK and many other Western countries, the experience has been very different. The daily count has fallen back gradually from the peak but remained stubbornly high. The reason for the divergence from the classical model remain unclear. We take an empirical stance on this issue and develop a model that is based upon the statistical characteristics of the time series. With the possible exception of China, the workhorse logistic model is decisively rejected against more flexible alternatives.

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 Analysis on the Impact of Short-Term International Capital Flows on Chinese Stock Market on the Basis of VAR Model

2018 ◽  
Vol 10 (8) ◽  
pp. 77
Author(s):  
Ning Wu
Keyword(s):  
Stock Market ◽  
Capital Flows ◽  
Composite Index ◽  
International Capital Flows ◽  
Capital Inflows ◽  
Var Model ◽  
Chinese Stock Market ◽  
International Capital ◽  
Short Term ◽  
The Impact

With the continuous development of global economic integration and financial markets, international capital flows more and more frequently, the frequent flow of international capital will inevitably affect the yield of Chinese stock market. This article uses short-term international capital inflows SS and Shanghai composite index R as research objects. Based on monthly data from January 2002 to October 2017, VAR model was constructed using Eviews8.0 to study the impact of short-term international capital flows on Chinese stock market. Empirical studies have found that short-term international capital flow is the granger cause of changes in the Shanghai composite index yield, while the yield of Chinese stock market will not affect short-term international capital flows. At the end of this paper, relevant suggestions are put forward according to the conclusions.

scholarly journals An assessment of Bayesian bias estimator for numerical weather prediction

2008 ◽  
Vol 15 (6) ◽  
pp. 1013-1022 ◽  
Author(s):  
J. Son ◽  
D. Hou ◽  
Z. Toth
Keyword(s):  
Time Series ◽  
Numerical Weather Prediction ◽  
Sampling Error ◽  
Weather Prediction ◽  
Training Data ◽  
Statistical Characteristics ◽  
Forecast Errors ◽  
Data Sets ◽  
Data Set ◽  
Numerical Weather

Abstract. Various statistical methods are used to process operational Numerical Weather Prediction (NWP) products with the aim of reducing forecast errors and they often require sufficiently large training data sets. Generating such a hindcast data set for this purpose can be costly and a well designed algorithm should be able to reduce the required size of these data sets. This issue is investigated with the relatively simple case of bias correction, by comparing a Bayesian algorithm of bias estimation with the conventionally used empirical method. As available forecast data sets are not large enough for a comprehensive test, synthetically generated time series representing the analysis (truth) and forecast are used to increase the sample size. Since these synthetic time series retained the statistical characteristics of the observations and operational NWP model output, the results of this study can be extended to real observation and forecasts and this is confirmed by a preliminary test with real data. By using the climatological mean and standard deviation of the meteorological variable in consideration and the statistical relationship between the forecast and the analysis, the Bayesian bias estimator outperforms the empirical approach in terms of the accuracy of the estimated bias, and it can reduce the required size of the training sample by a factor of 3. This advantage of the Bayesian approach is due to the fact that it is less liable to the sampling error in consecutive sampling. These results suggest that a carefully designed statistical procedure may reduce the need for the costly generation of large hindcast datasets.

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