Modelling Price Volatility in Onion using Wavelet based Hybrid Models

2021 ◽  
pp. 256-265
Keyword(s):  
Arima Model ◽  
Garch Model ◽  
Price Volatility ◽  
Hybrid Models ◽  
Data Series ◽  
Ann Model ◽  
Wavelet Filters ◽  
Nonparametric Approach ◽  
Normality Test ◽  
Conventional Models

The performance of wavelet-based hybrid models using different combinations of wavelet filters was compared to that of other conventional models to model volatility in the onion prices and arrivals at the Lasalgaon market of Maharashtra, which is known to be one of the largest markets in terms of arrivals. Monthly data of more than twenty-three years from 1996 onwards were taken into account. The results of hybrid models were compared to that of the ARIMA model. A normality test was conducted for both data series, and both of them were found to be non-normal. Therefore, a suitable nonparametric approach, namely wavelet decomposition of the data, was called for. For the price data, too, the wavelet- GARCH model with LA8 filter at five-level decomposition performed best for single value forecast, whereas the ARIMA performed well at expanded horizons. For the arrivals data, the Wavelet-GARCH model with LA8 filter at four level decomposition outperformed all models for single value forecasts. However, the wavelet-ANN model was able to perform better as the horizon expanded to twelve months. The study concluded that the wavelet hybrid models do pretty well for single value forecast, but as the horizon expands, the accuracy of the models decreases.

scholarly journals Comparison of ARIMA and ARIMA/GARCH Models in EVN Traffic Prediction

2014 ◽  
pp. 71
Author(s):  
Tran Quang Thanh ◽  
Trinh Quang Khai
Keyword(s):  
Communication Network ◽  
Mobile Communication ◽  
Arima Model ◽  
Garch Model ◽  
Data Series ◽  
Volatility Clustering ◽  
Mobile Communication Network ◽  
Numerical Result ◽  
The Mean ◽  
Short Term Prediction

This  paper  focuses  on  building  statistical models  to  capture  and  forecast  the  traffic  of  mobile communication  network  in  Vietnam.  Following  BoxJenkins  method,  a  multiplicative  seasonal  ARIMA model is constructed  to  represent  the  mean  component using the past values of traffic, a GARCH model is then incorporated  to  represent  its  volatility.  The  traffic  is collected  from  EVN  Telecom  mobile  communication network.  The  numerical  result  comparisons  show  that the  multiplicative  seasonal  ARIMA/GARCH  model built  in  this paper gives a better estimate  when dealing with volatility clustering in the data series. However, in short-term  prediction  where  the  volatility  has  an insignificant influence, the achieved ARIMA model also can  be considered  as  a  good  model  to  capture  well  the characteristics  of  EVN  traffic  series  and  gives reasonable forecasting results.

The dependence structure in volatility between Shanghai and Shenzhen stock market in China

2016 ◽  
Vol 6 (3) ◽  
pp. 264-283 ◽  
Author(s):  
Mingyuan Guo ◽  
Xu Wang
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Estimation Method ◽  
Garch Model ◽  
Price Volatility ◽  
Market Volatility ◽  
High Frequency Data ◽  
Dependence Structure ◽  
Frequency Data ◽  
Content Type

Purpose – The purpose of this paper is to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data. Design/methodology/approach – Using a multiplicative error model (hereinafter MEM) to describe the margins in volatility of China’s Shanghai and Shenzhen stock market, this study adopts static and time-varying copulas, respectively, estimated by maximum likelihood estimation method to describe the dependence structure in volatility between Shanghai and Shenzhen stock market in China. Findings – This paper has identified the asymmetrical dependence structure in financial market volatility more precisely. Gumbel copula could best fit the empirical distribution as it can capture the relatively high dependence degree in the upper tail part corresponding to the period of volatile price fluctuation in both static and dynamic view. Originality/value – Previous scholars mostly use GARCH model to describe the margins for price volatility. As MEM can efficiently characterize the volatility estimators, this paper uses MEM to model the margins for the market volatility directly based on high-frequency data, and proposes a proper distribution for the innovation in the marginal models. Then we could use copula-MEM other than copula-GARCH model to study on the dependence structure in volatility between Shanghai and Shenzhen stock market in China from a microstructural perspective.

scholarly journals Predicting Stock Market Index Using Hybrid Intelligence Model

2018 ◽  
Vol 7 (3.15) ◽  
pp. 36 ◽  
Author(s):  
Sarah Nadirah Mohd Johari ◽  
Fairuz Husna Muhamad Farid ◽  
Nur Afifah Enara Binti Nasrudin ◽  
Nur Sarah Liyana Bistamam ◽  
Nur Syamira Syamimi Muhammad Shuhaili
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Linear Models ◽  
Forecast Error ◽  
Arima Model ◽  
Real Data ◽  
Stock Index ◽  
Support Vector ◽  
Ann Model ◽  
Stock Market Index

Predicting financial market changes is an important issue in time series analysis, receiving an increasing attention due to financial crisis. Autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting but ARIMA model cannot capture nonlinear patterns easily. Generalized autoregressive conditional heteroscedasticity (GARCH) model applied understanding of volatility depending to the estimation of previous forecast error and current volatility, improving ARIMA model. Support vector machine (SVM) and artificial neural network (ANN) have been successfully applied in solving nonlinear regression estimation problems. This study proposes hybrid methodology that exploits unique strength of GARCH + SVM model, and GARCH + ANN model in forecasting stock index. Real data sets of stock prices FTSE Bursa Malaysia KLCI were used to examine the forecasting accuracy of the proposed model. The results shows that the proposed hybrid model achieves best forecasting compared to other model.  

scholarly journals The Dynamic Relationship between Crude Oil Prices and Stock Market Price Volatility in Nigeria: A Cointegrated VAR-GARCH Model

2019 ◽  
pp. 1-12
Author(s):  
David Adugh Kuhe
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Market Price ◽  
Garch Model ◽  
Price Volatility ◽  
Oil Prices ◽  
Market Prices ◽  
Dynamic Relationship ◽  
Crude Oil Prices ◽  
Long Run

This study investigates the dynamic relationship between crude oil prices and stock market price volatility in Nigeria using cointegrated Vector Generalized Autoregressive conditional Heteroskedasticity (VAR-GARCH) model. The study utilizes monthly data on the study variables from January 2006 to April 2017 and employs Dickey-Fuller Generalized least squares unit root test, simple linear regression model, unrestricted vector autoregressive model, Granger causality test and standard GARCH model as methods of analysis. Results shows that the study variables are integrated of order one, no long-run stable relationship was found to exist between crude oil prices and stock market prices in Nigeria. Both crude oil prices and stock market prices were found to have positive and significant impact on each other indicating that an increase in crude oil prices will increase stock market prices and vice versa. Both crude oil prices and stock market prices were found to have predictive information on one another in the long-run. A one-way causality ran from crude oil prices to stock market prices suggesting that crude oil prices determine stock prices and are a driven force in Nigerian stock market. Results of GARCH (1,1) models show high persistence of shocks in the conditional variance of both returns. The conditional volatility of stock market price log return was found to be stable and predictable while that of crude oil price log return was found to be unstable and unpredictable, although a dependable and dynamic relationship between crude oil prices and stock market prices was found to exist. The study provides some policy recommendations.

scholarly journals Comparison of ARIMA, ANN and LSTM for Stock Price Prediction

2020 ◽  
Vol 218 ◽  
pp. 01026
Author(s):  
Qihang Ma
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Moving Average ◽  
Arima Model ◽  
Predictive Ability ◽  
Research Direction ◽  
Ann Model ◽  
Advantages And Disadvantages ◽  
Stock Price Prediction

The prediction of stock prices has always been a hot topic of research. However, the autoregressive integrated moving average (ARIMA) model commonly used and artificial neural networks (ANN) still have their own advantages and disadvantages. The use of long short-term memory (LSTM) networks model for prediction also shows interesting possibilities. This article compares three models specifically through the analysis of the principles of the three models and the prediction results. In the end, it is believed that the LSTM model may have the best predictive ability, but it is greatly affected by the data processing. The ANN model performs better than that of the ARIMA model. The combination of time series and external factors may be a worthy research direction.

Hybrid Neural Models For Rice Yields Times Forecasting

2012 ◽  
Author(s):  
Ruhaidah Samsudin ◽  
Puteh Saad ◽  
Ani Shabri
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Moving Average ◽  
Time Series Prediction ◽  
Arima Model ◽  
Data Sets ◽  
Ann Model ◽  
Yield Data ◽  
Rice Yields ◽  
Artificial Neural Network Ann

In this paper, time series prediction is considered as a problem of missing value. A model for the determination of the missing time series value is presented. The hybrid model integrating autoregressive intergrated moving average (ARIMA) and artificial neural network (ANN) model is developed to solve this problem. The developed models attempts to incorporate the linear characteristics of an ARIMA model and nonlinear patterns of ANN to create a hybrid model. In this study, time series modeling of rice yield data in Muda Irrigation area. Malaysia from 1995 to 2003 are considered. Experimental results with rice yields data sets indicate that the hybrid model improve the forecasting performance by either of the models used separately. Key words: ARIMA; Box and Jenkins; neural networks; rice yields; hybrid ANN model

Monotonicity, linearity and symmetry in the price volatility–volume relationship

2020 ◽  
Vol 37 (1) ◽  
pp. 110-133 ◽  
Author(s):  
Panos Fousekis ◽  
Dimitra Tzaferi
Keyword(s):  
Trading Volume ◽  
Futures Markets ◽  
Statistical Tests ◽  
Price Volatility ◽  
Local Linear Regression ◽  
Linear Regression Models ◽  
Content Type ◽  
Nonparametric Approach ◽  
Volume Relationship ◽  
The Usa

Purpose This paper aims to investigate the contemporaneous link between price volatility and trading volume in the futures markets of energy. Design/methodology/approach Non-parametric (local linear) regression models and formal statistical tests are used to assess monotonicity, linearity and symmetry. The data are daily price and volumes from five futures markets (West Texas Intermediate, Brent, gasoline, heating oil and natural gas) in the USA. Findings Trading volume and price volatility have, in all markets, a strong nonlinear relation to each other. There are violations of monotonicity locally but not globally. The qualitative nature of the price shocks may have implications for the trading activity locally. Originality/value To the authors’ best knowledge, this is the first manuscript that investigates simultaneously and formally all the three important issues (i.e. monotonicity, linearity and asymmetry) for the price volatility–volume relationship using a highly flexible nonparametric approach.

JUMP, NON-NORMAL ERROR DISTRIBUTION AND STOCK PRICE VOLATILITY — A NONPARAMETRIC SPECIFICATION TEST

2009 ◽  
Vol 54 (01) ◽  
pp. 101-121
Author(s):  
MOHAMMAD MASUDUR RAHMAN ◽  
LAILA ARJUMAN ARA ◽  
ZHENLONG ZHENG
Keyword(s):  
Stock Price ◽  
Stock Return ◽  
Garch Model ◽  
Price Volatility ◽  
Stock Index ◽  
Specification Test ◽  
Significance Level ◽  
Student T Distribution ◽  
T Distribution ◽  
Distribution Assumption

This paper examines a wide variety of popular volatility models for stock index return, including Random Walk model, Autoregressive model, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, and extensive GARCH model, GARCH-jump model with Normal, and Student t-distribution assumption as well as nonparametric specification test of these models. We fit these models to Dhaka stock return index from 20 November 1999 to 9 October 2004. There has been empirical evidence of volatility clustering, alike to findings in previous studies. Each market contains different GARCH models, which fit well. From the estimation, we find that the volatility of the return and the jump probability were significantly higher after 27 November 2001. The model introducing GARCH jump effect with normal and Student t-distribution assumption can better fit the volatility characteristics. We find that RW-GARCH-t, RW-AGARCH-t RW-IGARCH-t and RW-GARCH-M-t can pass the nonparametric specification test at 5% significance level. It is suggested that these four models can capture the main characteristics of Dhaka stock return index.

Spatiotemporal forecasting for groundwater level using a WT-LSTM model

2020 ◽  
Author(s):  
wei qin ◽  
Chengpeng Lu ◽  
Long Sun ◽  
Jiayun Lu
Keyword(s):  
Neural Network ◽  
Groundwater Level ◽  
Wavelet Transforms ◽  
Groundwater Resources ◽  
Arima Model ◽  
Discrete Wavelet ◽  
Linear Regression Method ◽  
Ann Model ◽  
Periodic Term ◽  
Proposed Model

<p>Accurate groundwater level forecasting models is essential to ensure the sustainable utilization and efficient protection of groundwater resources. In this paper, a novel method for groundwater level forecasting is proposed on the basis of coupling discrete wavelet transforms (WT) and long and short term memory neural network (LSTM) . In this model, the wavelet transform is used to decompose the cumulative displacement into the term of trend and term of periodicity . The trend term reflects the long-term tendency of groundwater level variation, which is simulated by a linear regression method. The periodic term driven by external factors such as rainfall, the river stage and the distance from river, is modelled using a LSTM method. The distance from river and the distance from observation wells are used for spatiotemporal model interpretation. Finally, the trend term and periodic term are superposed to achieve the cumulative spatiotemporal prediction of groundwater level. A typical study area located in Haihe basin is taken as an example to validate the performance of the proposed model. The proposed mode (WT-LSTM) is compared with the regular artificial neural network (ANN) model and autoregressive integrated moving average (ARIMA) model. The results show that the prediction accuracy of WT-LSTM model is higher than ANN model and ARIMA model, especially during the flood period. Furthermore, the spatiotemporal groundwater level forecasting is not only included the observation of groundwater and precipitation, but should also take the influence factors of surface water into consideration. The proposed model gives a new sight in the prediction of groundwater level.</p>

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