scholarly journals Application of ARIMA-GARCH Model for Prediction of Indonesian Crude Oil Prices

2020 ◽  
Vol 1 (1) ◽  
pp. 25-33
Author(s):  
Sukono Sukono ◽  
Emah Suryamah ◽  
Fujika Novinta S
Keyword(s):  
Crude Oil ◽  
Stock Price ◽  
Price Index ◽  
Moving Average ◽  
Oil Prices ◽  
Garch Models ◽  
Crude Oil Price ◽  
Price Forecast ◽  
Crude Oil Prices ◽  
Auto Regressive

Crude oil is one of the most important energy commodities for various sectors. Changes in crude oil prices will have an impact on oil-related sectors, and even on the stock price index. Therefore, the prediction of crude oil prices needs to be done to avoid the future prices of these non-renewable natural resources to increase dramatically. In this paper, the prediction of crude oil prices is carried out using the Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) models. The data used for forecasting are Indonesian Crude Price (ICP) crude oil data for the period January 2005 to November 2012. The results show that the data analyzed follows the ARIMA(1,2,1)-GARCH(0,3) model, and the crude oil price forecast for December 2012 is 105.5528 USD per barrel. The prediction results of crude oil prices are expected to be important information for all sectors related to crude oil.

scholarly journals Comparative Performance of ARIMA and GARCH Model in Forecasting Crude Oil Price Data

2021 ◽  
pp. 251-275
Author(s):  
Atanu, Enebi Yahaya ◽  
Ette, Harrison Etuk ◽  
Amos, Emeka
Keyword(s):  
Time Series ◽  
Crude Oil ◽  
Moving Average ◽  
Oil Prices ◽  
Oil Price ◽  
Garch Models ◽  
Crude Oil Price ◽  
Autoregressive Integrated Moving Average ◽  
Crude Oil Prices ◽  
Best Fit

This study compares the performance of Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity models in forecasting Crude Oil Price data as obtained from (CBN 2019) Statistical Bulletin.  The forecasting of Crude Oil Price, plays an important role in decision making for the Nigeria government and all other sectors of her economy. Crude Oil Prices are volatile time series data, as they have huge price swings in a shortage or an oversupply period. In this study, we use two time series models which are Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heterocedasticity (GARCH) models in modelling and forecasting Crude Oil Prices. The statistical analysis was performed by the use of time plot to display the trend of the data, Autocorrelation Function (ACF), Partial Autocorrelation Functions (PACF), Dickey-Fuller test for stationarity, forecasting was done based on the best fit models for both ARIMA and GARCH models. Our result shows that ARIMA (3, 1, 2) is the best ARIMA model to forecast monthly Crude Oil Price and we also found GARCH (1, 1) model is the best GARCH model and using a specified set of parameters, GARCH (1, 1) model is the best fit for our concerned data set.

scholarly journals Pengaruh inflasi, nilai tukar rupiah per dollar Amerika, harga minyak mentah dunia dan indeks harga saham gabungan terhadap harga emas di Indonesia

2020 ◽  
Vol 8 (2) ◽  
pp. 55-64
Author(s):  
Fadhel Kesarditama ◽  
Haryadi Haryadi ◽  
Yohanes Vyn Amzar
Keyword(s):  
Exchange Rate ◽  
Crude Oil ◽  
Stock Price ◽  
Price Index ◽  
Oil Prices ◽  
Gold Price ◽  
Positive Trend ◽  
Macroeconomic Variables ◽  
Crude Oil Prices ◽  
Stock Price Index

This study aims to analyze the trend of macroeconomic variables and gold prices in Indonesia and to determine the effect of macroeconomic variables on gold prices in Indonesia. This study uses a quantitative approach. The data used is secondary data from January 2014-December 2019. The analytical tools and techniques used are trend analysis with a linear trend approach and multiple linear regression models using the Ordinary Least Square method. The five research variables that were processed showed that there were differences in the direction of the data trend. Where the variables of Gold Price, Exchange Rate, and Composite Stock Price Index show a positive trend, while the variables of Inflation and World Crude Oil Prices show a negative trend. Furthermore, the variables of Exchange Rate, world Crude Oil Price, and Composite Stock Price Index show a positive and significant influence on the Gold Price in Indonesia. While the inflation variable shows a negative and significant effect on the Gold Price in Indonesia. Keywords: Inflation, foreign exchange,crude oil prices, idx composite and gold prices

scholarly journals Forecasting Volatility of Crude oil Prices using Box-Jenkins's Autoregressive Moving Average: Evidence from Indian Chemical Industry

2019 ◽  
Vol 8 (3) ◽  
pp. 229-234
Keyword(s):  
Crude Oil ◽  
Moving Average ◽  
Oil Prices ◽  
Information Criteria ◽  
Oil Price ◽  
Spot Price ◽  
Autoregressive Moving Average ◽  
Crude Oil Price ◽  
Oil Companies ◽  
Crude Oil Prices

The current paper deals with to forecast volatility in crude oil prices in Indian economy. In the current study volatility is measured through change in monthly crude oil prices per barrel. The monthly data of crude oil price have taken from January 1995 to May, 2017. The different unit root tests are applied to test check change in crude oil price series is stationary or non stationary. Box-Jenkins's Autoregressive Moving Average of Box-Jenkins methodology has been used for developing a forecasting model. Minimum Akaike Information Criteria (AIC) has been opted to arrive at fit good ARMA model. According to this criteria (4, 3)(0,0) was observed as one of the best model to predict the volatility in future crude oil prices. Forecasted volatility in prices may be utilized for calculating future spot price and hedging future risk. Moreover, forecasted prices volatility of crude oil will also beneficial to oil companies, policy makers for formulating different economic policies and taking some crucial economic decision.

Crude Oil Price and Food Security Related Variables in Malaysia

2016 ◽  
Vol 4 (2) ◽  
pp. 17-21
Author(s):  
Shri Dewi Applanaidu ◽  
Mukhriz Izraf Azman Aziz
Keyword(s):  
Food Security ◽  
Crude Oil ◽  
Price Index ◽  
Price Volatility ◽  
Food Price ◽  
Oil Price ◽  
Crude Oil Price ◽  
Price Changes ◽  
Oil Price Volatility ◽  
Crude Oil Prices

Objective - This study analyzes the dynamic relationship between crude oil price and food security related variables (crude palm oil price, exchange rate, food import, food price index, food production index, income per capita and government development expenditure) in Malaysia using a Vector Auto Regressive (VAR) model. Methodology/Technique - The data covered the period of 1980-2014. Impulse response functions (IRFs) was applied to examine what will be the results of crude oil price changes to the variables in the model. To explore the impact of variation in crude oil prices on the selected food security related variables forecast error variance decomposition (VDC) was employed. Findings - Findings from IRFs suggest there are positive effects of oil price changes on food import and food price index. The VDC analyses suggest that crude oil price changes have relatively largest impact on real crude palm oil price, food import and food price index. This study would suggest to revisiting the formulation of food price policy by including appropriate weight of crude oil price volatility. In terms of crude oil palm price determination, the volatility of crude oil prices should be taken into account. Overdependence on food imports also needs to be reduced. Novelty - As the largest response of crude oil price volatility on related food security variables food vouchers can be implemented. Food vouchers have advantages compared to direct cash transfers since it can be targeted and can be restricted to certain types of products and group of people. Hence, it can act as a better aid compared cash transfers. Type of Paper - Empirical Keywords: Crude oil price, Food security related variables, IRF, VAR, VDC

Forecasting Crude Oil price using Artificial Neural Network model

2021 ◽  
pp. 321-326
Author(s):  
Sivaprakash J. ◽  
Manu K. S.
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Crude Oil ◽  
Back Propagation ◽  
Oil Prices ◽  
Oil Price ◽  
Back Propagation Algorithm ◽  
Crude Oil Price ◽  
Propagation Algorithm ◽  
Crude Oil Prices

In the advanced global economy, crude oil is a commodity that plays a major role in every economy. As Crude oil is highly traded commodity it is essential for the investors, analysts, economists to forecast the future spot price of the crude oil appropriately. In the last year the crude oil faced a historic fall during the pandemic and reached all time low, but will this situation last? There was analysis such as fundamental analysis, technical analysis and time series analyses which were carried out for predicting the movement of the oil prices but the accuracy in such prediction is still a question. Thus, it is necessary to identify better methods to forecast the crude oil prices. This study is an empirical study to forecast crude oil prices using the neural networks. This study consists of 13 input variables with one target variable. The data are divided in the ratio 70:30. The 70% data is used for training the network and 30% is used for testing. The feed forward and back propagation algorithm are used to predict the crude oil price. The neural network proved to be efficient in forecasting in the modern era. A simple neural network performs better than the time series models. The study found that back propagation algorithm performs better while predicting the crude oil price. Hence, ANN can be used by the investors, forecasters and for future researchers.

scholarly journals Time-Gap effects of crude oil prices on the foreign exchange rates: Evidence from Nigeria

2021 ◽  
Vol 3 (3) ◽  
pp. 31-44
Author(s):  
Nenubari Ikue John ◽  
Emeka Nkoro ◽  
Jeremiah Anietie
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Crude Oil ◽  
Foreign Exchange ◽  
Oil Prices ◽  
Cointegration Analysis ◽  
External Information ◽  
Garch Models ◽  
Foreign Exchange Rates ◽  
Crude Oil Prices

There is a pool of techniques and methods in addressing dynamics behaviors in higher frequency data, prominent among them is the ARCH/GARCH techniques. In this paper, the various types and assumptions of the ARCH/GARCH models were tried in examining the dynamism of exchange rate and international crude oil prices in Nigeria. And it was observed that the Nigerian foreign exchange rates behaviors did not conform with the assumptions of the ARCH/GARCH models, hence this paper adopted Lag Variables Autoregressive (LVAR) techniques originally developed by Agung and Heij multiplier to examine the dynamic response of the Nigerian foreign exchange rates to crude oil prices. The Heij coefficient was used to calculate the dynamic multipliers while the Engel & Granger two-step technique was used for cointegration analysis.  The results revealed an insignificant dynamic long-term response of the exchange rate to crude oil prices within the periods under review. The coefficient of dynamism was insignificantly in most cases of the sub-periods. The paper equally revealed that the significance of the dynamic multipliers depends greatly on external information about both market indicators which are two-way interactions. Thus, the paper recommends periodic intervention in the foreign exchange market by the monetary authorities to stabilize the market against any shocks in the international crude oil market, since crude oil is the main source of foreign exchange in Nigeria.

scholarly journals Daqing Crude Oil Price Forecast Based on the ARIMA Model

2015 ◽  
Vol 8 (1) ◽  
pp. 457-462
Author(s):  
Li Quan
Keyword(s):  
Crude Oil ◽  
Arima Model ◽  
Oil Prices ◽  
Industrial Country ◽  
Oil Price ◽  
Crude Oil Price ◽  
Major Industrial Country ◽  
Price Forecast ◽  
Industrial Economy ◽  
Monthly Average

Oil is the lifeblood of the industrial economy, oil prices are affected by many factors. China is a major industrial country, changes in the price of oil will affect many aspects of economic development, and therefore the price of crude oil research is extremely important. In this paper, monthly average prices of crude oil in Daqing from January 2000 to December 2010 are utilized to do the research. Based on ARIMA model by building software using EVIEWS, rule of oil price movements is found and a prediction of oil price is made using the data from the first 10 months of 2011.

A Comparative Study on Box-Jenkins and Garch Models in Forecasting Crude Oil Prices

2011 ◽  
Vol 11 (7) ◽  
pp. 1129-1135 ◽  
Author(s):  
Siti Roslindar Yaziz ◽  
Maizah Hura Ahmad ◽  
Lee Chee Nian ◽  
Noryanti Muhammad
Keyword(s):  
Comparative Study ◽  
Crude Oil ◽  
Oil Prices ◽  
Garch Models ◽  
Crude Oil Prices
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