Forecasting of Exxon Stock Price
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[pic 1][pic 2][pic 3][pic 4]ContentsIntroduction: 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 CO-INTEGRATION 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 co-integrating relationships. If there doesn’t exist any co-integrating 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 co-integrating 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 co-integrating 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: RegressionOBJECTIVE: 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 VariableRationaleCrude Oil Price (Brent)As reserves are valued on crude price, basic assetGas Price – NymexSimilar reasons as above; basic assetGoldBasic CommodityShell EquitySimilar company in the same sectorDow Jones and NasdaqMajor stock indicesDATA DESCRIPTION:Source of Data – Bloomberg Terminal DatabaseData Span – 12th Feb, 2016 – 15th Sept, 2016Data Plots[pic 5]Descriptive Statistics –nasdaqnymex-gasbrent Mean4923.426028Mean2.469646617Mean48.96192982Standard Error10.15133864Standard Error0.017843033Standard Error0.465390293Median4952.251Median2.591Median48.58Mode#N/AMode2.716Mode48.61Standard Deviation202.7728306Standard Deviation0.356414301Standard Deviation9.296163824Sample Variance41116.82082Sample Variance0.127031154Sample Variance86.41866184Kurtosis0.352549867Kurtosis-0.982683977Kurtosis-0.532407744Skewness-0.742214392Skewness-0.559092986Skewness0.077115461Range1017.089Range1.377Range39.89Minimum4266.837Minimum1.639Minimum27.88Maximum5283.926Maximum3.016Maximum67.77Sum1964446.985Sum985.389Sum19535.81Count399Count399Count399