Forecasting of Exxon Stock Price
Essay by Ayan Panda • December 4, 2016 • Research Paper • 3,023 Words (13 Pages) • 928 Views
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Contents
Introduction: What are we trying to achieve?
PART 1: Regression
OBJECTIVE:
RATIONALE FOR SELECTING INDEPENDENT VARIABLES:
DATA DESCRIPTION:
MODELLING PROCEDURE:
OUTPUT:
PART –II: ARIMA and GARCH Modelling
OBJECTIVE –
DATA DESCRIPTION –
PROCEDURE –
ARCH EFFECT TEST FOR HETEROSKEDASTICITY:
MODELLING OF ERROR VARIANCE USING EGARCH:
PART –III: VAR AND COINTEGRATION
OBJECTIVE:
PROCEDURE:
CONCLUSION
SCOPE FOR IMPROVEMENT
Introduction: What are we trying to achieve?
Our model is primarily devised to understand an empirical dependency between the share price of Exxon Mobil and Shell in addition to Dow Jones index, Brent Oil price, Gold price and USD/EUR exchange rate. By default, we keep the share price of Exxon Mobil as a dependent variable while treating all others as independent variables. The categorical aim of this study is to understand whether there exists any arbitrage opportunity between these variables or are they all independent of each other. In either case, we then go ahead to develop a forecasting model to estimate the share price of Exxon Mobil over a period after taking all of these variables into consideration. There is an equal chance that the share price of Exxon Mobil depends on its past values (an attribute which will be understood after auto correlation tests). Once these observations are established, we move on to understand if these variables have any cointegrating relationships. If there doesn’t exist any cointegrating relationships, this signifies there does exist ample opportunities for an arbitrage and depending upon the flow of causality, investment choices need to be made. However, if there do exist cointegrating relationships, it would then mean that the prices of all the variables under consideration will converge in the long run and hence we need to take our decision accordingly. So for instance, if Shell prices are higher as of now rather than Exxon Mobil, it means we need to go long on Exxon Mobil and short on Shell prices, for in case of cointegrating relationships, the prices will converge in the long run. This eventually helps us to devise a prudent portfolio strategy in order to maximize our returns.
We choose these variables while forecasting the share price value of Exxon Mobil because Exxon Mobil is into oil refining and Shell is its close competitor. Any changes in the stock price of the latter is supposed to have effect on Exxon’s share price if these changes are due to some industry wide phenomena. Further, Exxon Mobil is listed on NASDAQ and hence Dow Jones Index can have an effect on it. Moreover, Brent crude oil is the input to these refining operations and hence can have an effect. Besides, the USD/EUR exchange rate is important for all global oil trades are done in the USD.
Thus, after making a judicious statistical study of the above variables we will be able to determine and forecast the future share price of Exxon Mobil. The model utilizes ARIMA to make the time data series stationary and then goes on to use the GARCH model in order to understand the long term causality between the variables.
PART 1: Regression
OBJECTIVE:
The objective of the study is to model the stock price of Exxon Mobil, a US based global oil and gas major.
RATIONALE FOR SELECTING INDEPENDENT VARIABLES:
Independent Variable  Rationale 
Crude Oil Price (Brent)  As reserves are valued on crude price, basic asset 
Gas Price  Nymex  Similar reasons as above; basic asset 
Gold  Basic Commodity 
Shell Equity  Similar company in the same sector 
Dow Jones and Nasdaq  Major stock indices 
DATA DESCRIPTION:
 Source of Data – Bloomberg Terminal Database
 Data Span – 12th Feb, 2016 – 15th Sept, 2016
 Data Plots[pic 5]
 Descriptive Statistics –
nasdaq  nymexgas  brent  






Mean  4923.426028  Mean  2.469646617  Mean  48.96192982 
Standard Error  10.15133864  Standard Error  0.017843033  Standard Error  0.465390293 
Median  4952.251  Median  2.591  Median  48.58 
Mode  #N/A  Mode  2.716  Mode  48.61 
Standard Deviation  202.7728306  Standard Deviation  0.356414301  Standard Deviation  9.296163824 
Sample Variance  41116.82082  Sample Variance  0.127031154  Sample Variance  86.41866184 
Kurtosis  0.352549867  Kurtosis  0.982683977  Kurtosis  0.532407744 
Skewness  0.742214392  Skewness  0.559092986  Skewness  0.077115461 
Range  1017.089  Range  1.377  Range  39.89 
Minimum  4266.837  Minimum  1.639  Minimum  27.88 
Maximum  5283.926  Maximum  3.016  Maximum  67.77 
Sum  1964446.985  Sum  985.389  Sum  19535.81 
Count  399  Count  399  Count  399 
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