# Forecasting of Exxon Stock Price

Essay by   •  December 4, 2016  •  Research Paper  •  3,023 Words (13 Pages)  •  995 Views

## Essay Preview: Forecasting of Exxon Stock Price

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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 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.

## 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 nymex-gas 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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