scholarly journals Equity Return Modeling and Prediction Using Hybrid ARIMA-GARCH Model

2017 ◽  
Vol 8 (3) ◽  
pp. 154
Author(s):  
Kaiying Sun
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Model Fitting ◽  
Garch Model ◽  
Information Criterion ◽  
Equity Returns ◽  
Series Data ◽  
Arima Models ◽  
Fitting Procedure

In this paper, a hybrid ARIMA-GARCH model is proposed to model and predict the equity returns for three US benchmark indices: Dow Transportation, S&P 500 and VIX. Equity returns are univariate time series data sets, one of the methods to predict them is using the Auto-Regressive Integrated Moving Average (ARIMA) models. Despite the fact that the ARIMA models are powerful and flexible, they are not be able to handle the volatility and nonlinearity that are present in the time series data. However, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models are designed to capture volatility clustering behavior in time series. In this paper, we provide motivations and descriptions of the hybrid ARIMA-GARCH model. A complete data analysis procedure that involves a series of hypothesis testings and a model fitting procedure using the Akaike Information Criterion (AIC) is provided in this paper as well. Simulation results of out of sample predictions are also provided in this paper as a reference.

Modelling Monthly Volatility of the Muscat Securities Market (MSM) Index Using Auto Regressive Integrated Moving Average (ARIMA)

2019 ◽  
Vol 10 (11) ◽  
pp. 1045-1056
Author(s):  
Shaik Nafeez Umar Shaik ◽  
◽  
Labeeb Mohammed Zeeshan ◽  
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Data ◽  
Moving Average ◽  
Performance Model ◽  
Securities Market ◽  
Series Data ◽  
Arima Models ◽  
Augmented Dickey Fuller ◽  
Auto Regressive

The Stock market is eyewitness’s responsive activities and is gradually more gaining importance. The purpose of the study is to measure the volatility of selected emerging indices Muscat Securities Market (MSM). Time series analysis techniques were used including Auto Regressive Integrated Moving Average (ARIMA) models. The time series data considered of this study taken MSM 30. The study period has taken from January 2013 to December 2018 except Sharia-compliant index would be June 2013 to December 2018. Tools used for the study is Unit Toot Test (Augmented Dickey–Fuller and Phillips-Perron), ARIMA models and for performance model using Theil’s U-Statistic. The study made a few observations which may help the investors and model builders to understand better about the stock market.

scholarly journals Application of Seasonal Autoregressive Moving Average Models to Analysis and Forecasting of Time Series Monthly Rainfall Patterns in Embu County, Kenya

2019 ◽  
pp. 1-15
Author(s):  
Tartisio Njoki Filder ◽  
Moses Mahugu Muraya ◽  
Robert Mathenge Mutwiri
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Rainfall Variability ◽  
Model Fitting ◽  
Model Identification ◽  
Monthly Rainfall ◽  
Series Data ◽  
Estimation Model ◽  
Rainfall Variation

Rainfall is of critical importance for many people, particularly those whose livelihoods depend on rain-fed agriculture. Predicting the trend of rainfall is a difficult task, and statistical approaches such as time series analysis provide a means for predicting the patterns of rainfall. The models also offer the potential to improve areas such as increased food production, profitability, and improved food security policing. However, these forecasts and information systems may, in some instances, not be suitable for direct use by stakeholders in their decision-making. The objective of this study was to investigate rainfall variability and develop a Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model for fitting the monthly rainfall using time series data. Secondary monthly data from 1998 to 2017 for Embu County was collected from the Kenya Meteorological Department, Embu and recorded into an excel sheet. R-software was utilized to analyse data for descriptive statistics, rainfall variability, and model fitting. The coefficient of variation for annual and seasonal rainfall was calculated. The Box Jenkin's ARIMA modelling procedure (model identification, model estimation, model validation) was used to determine the best models for the data. The main study findings indicated the existence of annual variability of 34%, March-April-May rainfall variability of 44%, and October-November-December variability of 44%. A first-order differenced SARIMA (1, 1, 1) (0, 1, 2)12 model with an AIC score of 9.99356 was found suitable for predicting rainfall pattern in Embu, County. The study outcome revealed that Embu County experiences high seasonal and rainfall variation of rainfall, thus requires a reliable model for better prediction. 

scholarly journals State space modelling and forecasting of sugarcane yield in Haryana, India

2017 ◽  
Vol 9 (4) ◽  
pp. 2036-2042 ◽  
Author(s):  
Suman Suman ◽  
Urmil Verma
Keyword(s):  
Time Series ◽  
State Space ◽  
Time Series Data ◽  
Moving Average ◽  
State Space Models ◽  
Series Data ◽  
Arima Models ◽  
Department Of Agriculture ◽  
Error Metrics ◽  
Sugarcane Yield

Box and Jenkins’ Autoregressive Integrated Moving Average (ARIMA) models are widely used for analyzing and forecasting the time-series data. In this approach, the underlying parameters are assumed to be constant however the data in agriculture are generally collected over time and thus have the time-dependency in parameters. Such data can be analyzed using state space (SS) procedures by the application of Kalman filtering technique. The purpose of this article is to illustrate the usefulness of state space models in sugarcane yield forecasting and to pro-vide some empirical evidence for its superiority over the classical time-series analysis. ARIMA and state space models individually could provide the suitable relationship(s) to reliably forecast the sugarcane yield in Karnal, Ambala, Kurukshetra, Yamunanagar and Panipat districts of Haryana (India). However, the state space models with lower error metrics showed the superiority over ARIMA models for this empirical study. The sugarcane yield forecasts based on SS models in the districts under consideration showed good agreement with State Department of Agriculture (DOA) yields by showing 3-6 percent average absolute deviations.

Predicting Rice Production using Autoregressive Integrated Moving Average Model

2019 ◽  
Vol 6 (04) ◽  
Author(s):  
R C BHARATI ◽  
ANIL KUMAR SINGH
Keyword(s):  
Time Series ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Time Series Data ◽  
Moving Average ◽  
Information Criterion ◽  
Rice Production ◽  
Series Data ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average

A study was conducted on time-series data on rice production in India. Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) time-series process was considered for predicting country's rice production using the time series data from 1950–51 to 2017–18. Data from 1950–51 to 2014–15 were used for model development and three years data from 2015–16 and 2017–18 were kept for validation The augmented Dicky Fuller test was applied to test stationarity in data set. Root mean square error. Based on ACF and PACF, the model was defined and tested for its suitability. Akaike information criterion and Bayesian information criterion were used to judge the suitability of the model to be fitted. The performance of the fitted model was examined using mean absolute error, mean percent forecast error, root mean square error and Theil's inequality coefficients. IMA (0, 1, 1) model performed well for forecasting purposes. The percent prediction error for the last three years i.e. from 2015–16 and 2017–18, was below 3%. The predicted values along with their standard errors up to the year 2099, were also obtained using the model.

scholarly journals Massive social protests amid the pandemic in selected Colombian cities: Did they increase COVID-19 cases?

2021 ◽  
Author(s):  
Jose Moreno-Montoya ◽  
Laura A Rodriguez Villamizar ◽  
Alvaro Javier Idrovo
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Interrupted Time Series ◽  
Series Data ◽  
Arima Models ◽  
Interrupted Time Series Analysis ◽  
Social Protests ◽  
Show Evidence ◽  
The Impact

Background. Since April 28, 2021, in Colombia there are social protests with numerous demonstrations in various cities. This occurs whereas the country faces the third wave of the COVID-19 pandemic. The aim of this study was to assess the effect of social protests on the number and trend of the confirmed COVID-19 cases in some selected Colombian cities where social protests had more intensity. Methods. We performed and interrupted time-series analysis (ITSA) and Autoregressive Integrated Moving Average (ARIMA) models, based on the confirmed COVID-19 cases in Colombia, between March 1 and May 15, 2021, for the cities of Bogota, Cali, Barranquilla, Medellin, and Bucaramanga. The ITSA models estimated the impact of social demonstrations on the number and trend of cases for each city by using Newey-West standard errors and ARIMA models assessed the overall pattern of the series and effect of the intervention. We considered May 2, 2021, as the intervention date for the analysis, five days after social demonstrations started in the country. Findings. During the study period the number of cases by city was 1,014,815 for Bogota, 192,320 for Cali, 175,269 for Barranquilla, 311,904 for Medellin, and 62,512 for Bucaramanga. Heterogeneous results were found among cities. Only for the cities of Cali and Barranquilla statistically significant changes in trend of the number of cases were obtained after the intervention: positive in the first city, negative in the second one. None ARIMA models show evidence of abrupt changes in the trend of the series for any city and intervention effect was only positive for Bucaramanga. Interpretation. The findings confer solid evidence that social protests had an heterogenous effect on the number and trend of COVID-19 cases. Divergent effects might be related to the epidemiologic time of the pandemic and the characteristics of the social protests. Assessing the effect of social protests within a pandemic is complex and there are several methodological limitations. Further analyses are required with longer time-series data.

scholarly journals Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 39-55
Author(s):  
Rodgers Makwinja ◽  
Seyoum Mengistou ◽  
Emmanuel Kaunda ◽  
Tena Alemiew ◽  
Titus Bandulo Phiri ◽  
...  
Keyword(s):  
Time Series ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Efficiency Coefficient ◽  
Arima Models ◽  
Catch Per Unit Effort ◽  
Annual Fish ◽  
Unit Effort ◽  
The Mean

Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

scholarly journals A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3561
Author(s):  
Diego Duarte ◽  
Chris Walshaw ◽  
Nadarajah Ramesh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
General Regression Neural Network ◽  
Series Data ◽  
Machine Learning Model ◽  
Hospital Patients ◽  
Major Incidents ◽  
General Regression ◽  
The Impact

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

scholarly journals Studying monthly rainfall over Dibrugarh, Assam: Use of SARIMA approach

MAUSAM ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 349-356
Author(s):  
J. HAZARIKA ◽  
B. PATHAK ◽  
A. N. PATOWARY
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Demand Management ◽  
Arima Model ◽  
Monthly Rainfall ◽  
Series Data ◽  
Data Set ◽  
Modeling And Forecasting ◽  
Moving Average Model

Perceptive the rainfall pattern is tough for the solution of several regional environmental issues of water resources management, with implications for agriculture, climate change, and natural calamity such as floods and droughts. Statistical computing, modeling and forecasting data are key instruments for studying these patterns. The study of time series analysis and forecasting has become a major tool in different applications in hydrology and environmental fields. Among the most effective approaches for analyzing time series data is the ARIMA (Autoregressive Integrated Moving Average) model introduced by Box and Jenkins. In this study, an attempt has been made to use Box-Jenkins methodology to build ARIMA model for monthly rainfall data taken from Dibrugarh for the period of 1980- 2014 with a total of 420 points.  We investigated and found that ARIMA (0, 0, 0) (0, 1, 1)12 model is suitable for the given data set. As such this model can be used to forecast the pattern of monthly rainfall for the upcoming years, which can help the decision makers to establish priorities in terms of agricultural, flood, water demand management etc.  

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