The document examines the relationship between crude oil prices, stock market performance, and macroeconomic variables in the United States over the past 10 years. It finds that GDP, industrial growth, per capita income, personal consumption, and net exports have a negative correlation with oil prices, while consumer price index has a positive correlation. The regression model shows these variables explain 38.3% of changes in oil prices. There is evidence of positive autocorrelation and heteroskedasticity in the model. Only GDP is found to be a statistically significant predictor of oil prices.
Oil has for decades been perceived as a necessary and highly addictive energy commodity, fueling the world economy. It is a crucial input good for most of the net-oil consumer countries, and it is an important source of revenue for the net-oil supplier countries. This means that any changes in the oil price will affect the entire world economy. Chloé Le Coq and Zorica Trkulja from Stockholm Institute of Transition Economics have written a policy brief that explains to what extent the oil-price fluctuations matter for the economy.
Read more: https://www.hhs.se/site
This paper examines the relationship between Saudi oil revenues and the Kingdom’s economic growth over the past 47 years. In analyzing the data that are needed for this analysis, problems were encountered with the basic real GDP and government oil revenue data that are typically used. The most widely-used measure of non-oil private sector activity that is available, the Non-Oil Private Institutional Sector GDP, does not include the Gross Value Added of all of the private activities, omitting over SAR 80 billion of real activity (in 2010 prices). A new series was constructed, consisting of all of the non-oil private activities, including the recently corporatized/privatized companies. In addition, the oil revenue data prior to 1987 were found to be unsatisfactory for use as published, due to their being based on the 354-355 day Hijra calendar. A new conversion methodology, based on a recently published paper by Qualls et al. (2017), was applied, and the pre-1987 data were converted to a consistent Gregorian basis with good results. The two series were determined to have a unit root of order one, with a highly significant long-run relationship. An error-correction model was then estimated, and highly significant short- and long-run relationships were found. A Ganger Causality test was performed, with the results confirming the ECM’s results, with real government oil revenue growth “Granger-causing” real private-sector GDP growth. Finally, the new non-oil activity GDP measure produced better results than did the traditionally-used Non-Oil Private Sector GDP.
Oil has for decades been perceived as a necessary and highly addictive energy commodity, fueling the world economy. It is a crucial input good for most of the net-oil consumer countries, and it is an important source of revenue for the net-oil supplier countries. This means that any changes in the oil price will affect the entire world economy. Chloé Le Coq and Zorica Trkulja from Stockholm Institute of Transition Economics have written a policy brief that explains to what extent the oil-price fluctuations matter for the economy.
Read more: https://www.hhs.se/site
This paper examines the relationship between Saudi oil revenues and the Kingdom’s economic growth over the past 47 years. In analyzing the data that are needed for this analysis, problems were encountered with the basic real GDP and government oil revenue data that are typically used. The most widely-used measure of non-oil private sector activity that is available, the Non-Oil Private Institutional Sector GDP, does not include the Gross Value Added of all of the private activities, omitting over SAR 80 billion of real activity (in 2010 prices). A new series was constructed, consisting of all of the non-oil private activities, including the recently corporatized/privatized companies. In addition, the oil revenue data prior to 1987 were found to be unsatisfactory for use as published, due to their being based on the 354-355 day Hijra calendar. A new conversion methodology, based on a recently published paper by Qualls et al. (2017), was applied, and the pre-1987 data were converted to a consistent Gregorian basis with good results. The two series were determined to have a unit root of order one, with a highly significant long-run relationship. An error-correction model was then estimated, and highly significant short- and long-run relationships were found. A Ganger Causality test was performed, with the results confirming the ECM’s results, with real government oil revenue growth “Granger-causing” real private-sector GDP growth. Finally, the new non-oil activity GDP measure produced better results than did the traditionally-used Non-Oil Private Sector GDP.
This year's SITE Energy Day was devoted to discussing the consequences of oil price fluctuations for markets and actors of the economy. The half-day conference engaged policy-oriented scholars and experts from the business community to discuss the impact of oil price fluctuations on macro fundamentals, international trade, strategies of oil cartels, strategic risk management, and opportunities for change in energy systems.
Torbjörn Becker, Director of SITE, gave a talk "The volatility of oil price forecasts and its macroeconomic implications"
For more information and research analysis please visit: www.hhs.se/site
Crude oil price in 2011
When analyzing the prospects of crude oil price in 2011, there are several aspects worth considering. The expected increase in world demand for Oil in 2011 - IEA (International Energy Agency) expects petroleum demand worldwide in 2011 to be 88.8 million barrels per day, which is roughly a 1.6% increase in demand for oil in 2011 compares to 2010; in 2010 the daily consumption was estimated at 87.4 MB/d. OPEC, which is responsible for about 40 percent of the world crude oil supply, announced, in a recent OPEC meeting, it will sustain its current quota of 24.845 million which was set back in 2008.
Despite credit market turbulence and slowing activity in many major advanced economies, oil prices have been reaching record highs in recent months. Besides oil-specific factors, such as geopolitical risks and speculations, the current price boom is driven by demand and supply forces that reinforce each other amid supportive financial conditions. This paper aims to a link macroeconomic variables together with oil prices in order to provide complement decision tools used by commercial and investment banks when optimizing their investment portfolios. For that reason, we apply financial programming model with incorporated oil price variable. We show that oil prices affect private consumption, gross domestic product, inflation, and imports. On the other hand, we also investigate effects of macroeconomic variables on oil market equilibrium. A decrease in oil supply as well as depreciation of the US$ lead to higher oil prices, which in turn decrease private consumption and output, but as well stimulate inflationary pressures. Empirical test is performed on the basis of quarterly US data from 2001 to 2007. Although financial programming models are subject to limitations and empirical implications are difficult to apply, some general relations between selected macroeconomic variables and oil price can be determined.
Understanding the decline of global oil exportsASPO.be
In this study, we use indicators such as the exports-to-production ratio, and the difference between the growth rate of oil exports and the growth rate of oil production, to characterize the dynamics that lead to a decline of oil exports. Many countries have passed their peak of oil exports, and the world as well, in 2005. The indicators presented here show that the deterioration of the fundamentals is a long term dynamics, thus meaning that global oil export will likely continue declining, though temporary rebounds can occur. These evolutions are then related to recent events such as the Arab Spring, the rise of oil prices on international markets, and the current economic crises. The peaking of world oil exports is a recent and significant turning point, though still largely ignored, but its implications for both oil-importing and oil-exporting countries are vast.
An Investigation of Crude Oil and its Implication for Financial Markets Priesnell Warren ✔
This research paper seeks to unearth the possible repercussions of fluctuations in Crude Oil markets and how they will affect global trade and financial markets. Crude oil or Black Gold is one of the world’s most precious commodities as its change in price affects the entire economy.
Russia’s dependence on oil and other natural resources is well known, but what does it actually mean for policy makers’ ability to control the economic fate of the country? This brief provides a more precise analysis of the depth of Russia’s oil dependence. This is based on a careful statistical analysis of the immediate correlation between international oil prices — that Russia does not control — and Russian GDP, which policy makers would like to control. I then look at how IMF’s forecast errors in oil prices spillover to forecast errors of Russian GDP. These numerical exercises are striking; over the last 25 years oil price changes explain on average two thirds of the variation in Russian GDP growth and in the last 15 years up to 80 percent of the one-year ahead forecast errors. Instead of controlling the economic fate of the country, the best policy makers can hope for is to dampen the short-run impact of oil price shocks. A flexible exchange rate and fiscal reserves are key volatility dampers, but not sufficient to protect long-term growth. The latter will always require serious structural reforms and the question is what needs to happen for policy makers to take action to get control over the long-term fate of the economy.
Oil & Gas Intelligence Report: A Discussion of Price Forecasting MethodolgiesDuff & Phelps
Throughout this report, Duff & Phelps will analyse the nature of crude oil prices, their historical evolution and the factors that condition their changes in order to evaluate certain tools for their prediction.
As observed during the last decades, oil prices, mainly because of the influence of exogenous factors, have shown significant oscillations that have created a frame of uncertainty that may not be easy to manage.
This year's SITE Energy Day was devoted to discussing the consequences of oil price fluctuations for markets and actors of the economy. The half-day conference engaged policy-oriented scholars and experts from the business community to discuss the impact of oil price fluctuations on macro fundamentals, international trade, strategies of oil cartels, strategic risk management, and opportunities for change in energy systems.
Torbjörn Becker, Director of SITE, gave a talk "The volatility of oil price forecasts and its macroeconomic implications"
For more information and research analysis please visit: www.hhs.se/site
Crude oil price in 2011
When analyzing the prospects of crude oil price in 2011, there are several aspects worth considering. The expected increase in world demand for Oil in 2011 - IEA (International Energy Agency) expects petroleum demand worldwide in 2011 to be 88.8 million barrels per day, which is roughly a 1.6% increase in demand for oil in 2011 compares to 2010; in 2010 the daily consumption was estimated at 87.4 MB/d. OPEC, which is responsible for about 40 percent of the world crude oil supply, announced, in a recent OPEC meeting, it will sustain its current quota of 24.845 million which was set back in 2008.
Despite credit market turbulence and slowing activity in many major advanced economies, oil prices have been reaching record highs in recent months. Besides oil-specific factors, such as geopolitical risks and speculations, the current price boom is driven by demand and supply forces that reinforce each other amid supportive financial conditions. This paper aims to a link macroeconomic variables together with oil prices in order to provide complement decision tools used by commercial and investment banks when optimizing their investment portfolios. For that reason, we apply financial programming model with incorporated oil price variable. We show that oil prices affect private consumption, gross domestic product, inflation, and imports. On the other hand, we also investigate effects of macroeconomic variables on oil market equilibrium. A decrease in oil supply as well as depreciation of the US$ lead to higher oil prices, which in turn decrease private consumption and output, but as well stimulate inflationary pressures. Empirical test is performed on the basis of quarterly US data from 2001 to 2007. Although financial programming models are subject to limitations and empirical implications are difficult to apply, some general relations between selected macroeconomic variables and oil price can be determined.
Understanding the decline of global oil exportsASPO.be
In this study, we use indicators such as the exports-to-production ratio, and the difference between the growth rate of oil exports and the growth rate of oil production, to characterize the dynamics that lead to a decline of oil exports. Many countries have passed their peak of oil exports, and the world as well, in 2005. The indicators presented here show that the deterioration of the fundamentals is a long term dynamics, thus meaning that global oil export will likely continue declining, though temporary rebounds can occur. These evolutions are then related to recent events such as the Arab Spring, the rise of oil prices on international markets, and the current economic crises. The peaking of world oil exports is a recent and significant turning point, though still largely ignored, but its implications for both oil-importing and oil-exporting countries are vast.
An Investigation of Crude Oil and its Implication for Financial Markets Priesnell Warren ✔
This research paper seeks to unearth the possible repercussions of fluctuations in Crude Oil markets and how they will affect global trade and financial markets. Crude oil or Black Gold is one of the world’s most precious commodities as its change in price affects the entire economy.
Russia’s dependence on oil and other natural resources is well known, but what does it actually mean for policy makers’ ability to control the economic fate of the country? This brief provides a more precise analysis of the depth of Russia’s oil dependence. This is based on a careful statistical analysis of the immediate correlation between international oil prices — that Russia does not control — and Russian GDP, which policy makers would like to control. I then look at how IMF’s forecast errors in oil prices spillover to forecast errors of Russian GDP. These numerical exercises are striking; over the last 25 years oil price changes explain on average two thirds of the variation in Russian GDP growth and in the last 15 years up to 80 percent of the one-year ahead forecast errors. Instead of controlling the economic fate of the country, the best policy makers can hope for is to dampen the short-run impact of oil price shocks. A flexible exchange rate and fiscal reserves are key volatility dampers, but not sufficient to protect long-term growth. The latter will always require serious structural reforms and the question is what needs to happen for policy makers to take action to get control over the long-term fate of the economy.
Oil & Gas Intelligence Report: A Discussion of Price Forecasting MethodolgiesDuff & Phelps
Throughout this report, Duff & Phelps will analyse the nature of crude oil prices, their historical evolution and the factors that condition their changes in order to evaluate certain tools for their prediction.
As observed during the last decades, oil prices, mainly because of the influence of exogenous factors, have shown significant oscillations that have created a frame of uncertainty that may not be easy to manage.
Impact Analysis of Petroluem Product Price Changes on Households’ Welfare in ...inventionjournals
This paper examines the impact of petroleum products price changes on household welfare in Zaria metropolis of Kaduna state. Respondents communities were stratified selected base on their geographical locations. Descriptive statistics and inferential statistics tools were employed and use for data analysis. Descriptive statistics was used to analyze socio economic characteristics of household head and to determine the price changes of petroleum products on households. while inferential statistical tools was employed to specifically show how price changes of petroleum products affect the household through increase in prices of petroleum products which causes decrease in demand for the products, and also have multiplier effect on goods and services. On the other hand, decrease in prices of petroleum products also increase the demand for the products in Zaria metropolis. To achieved this objective, non parametric chi-square test was employed. The results shows that, the three petroleum products that is, petrol (PMS), gas (LPG) and kerosene (DPK) of the study have an impact on household welfare. This indicated that increase in the petroleum products price changes causes decrease in demand of the products, while on the other hand the decrease of the petroleum products prices causes increase in demand for the products which was in conformity with the demand theory that was adopted in this study. The study also recommends, government should deregulate the downstream petroleum sector to allow for increase participation and competition which will alternatively result in reducing prices of petroleum products Moreover, emphasis on alternative sources of energy such as gas, solar, wind and hydraulic sources should put into consideration. Government should expanded consumption capacity effect which will translate to increased demand for varied consumer good and hence increased sales and profitability of a number of Nigerians
Forecasting the Causal Relationship between Oil Prices and Exchange Rate in N...iosrjce
This study empirically forecasted the causal relationship between oil prices and exchange rate in
Nigeria using data for 45 years (1970 - 2014). The data which is purely secondary data was sourced through
the Central Bank of Nigeria Statistical Bulletin for various years. The study modified the Sibanda and Mlambo
(2014)’s model to estimate the relationship and long-run effect of oil prices and exchange rates in Nigeria. With
the Durbin-Watson statistic value that there is no autocorrelation in the model, t-test statistic was used to test
the hypothesis that “there is no significant relationship between oil prices and exchange rate in Nigeria”, using
the e-view statistical software. The empirical findings indicate that a unit increase in oil price will lead to
44.91% increases in exchange rate in Nigeria. This implies that oil prices significantly influence exchange rate
in Nigeria, with the t-statistic P-value (0.0000) and table value (1.671) at 5% level of significance. The study
then recommended that exchange rate management policy makers should ensure that the oil price changes are
included in exchange rate management in Nigeria
Kamiar Mohaddes - University of Cambridge
Hashem Pesaran - USC Dornsife INET & Trinity College, Cambridge
ERF Conference on “Arab Oil Exporters: Coping with a New Global Oil Order”
Kuwait, November 26-27, 2017
www.erf.org.eg
Oil Prices and Nigerian Aggregate Economic Activitiesiosrjce
This paper examines the oil prices and Nigerian aggregate economic activities. The data series
employed were guttered from various sources such as the central bank of Nigeria statistical Bulletin, Economic
and Financial Review, and the publications of International monetary fund. The study employed the linear
Dynamic VAR. results from VAR showed that oil price shocks and output in Nigeria is negative. This shows that
oil prices shock leads to reduction in gross domestic products. It is recommended that government should
diversify its revenue base and develop other sectors of Nigerian economy to contribute significantly to the
growth not of Nigerian Economy
1. The united states stock and Oil Prices1
The Relationship between the United States Stock
Return and the Oil Prices
SubjectCode: ECON939
Submittedby :
NourMutair 4553573
AhmedAbuSharkh
Hassan Wahdan
2. The united states stock and Oil Prices2
Abstract
Our project will examine the relationship between crude oil price(cp), stock market and
macroeconomic variables, the duration of research for the last 10 years for the macroeconomic
variables, we will include industrial production(ip), money supply(M2), inflation(IR)and it’s relation
with oil price and stock market. These factors have an effect on the performance of the economy that
can be analyzed and presented. Changes in oil prices coincide with changes in the stock prices of
some stocks but not necessarily the stock index not unless the change is experienced over a long
period.
3. The united states stock and Oil Prices3
Contents
The Relationship between the United States Stock and the Brent...........................................................1
Abstract............................................................................................................................................2
METHODOLOGY.................................................................................................................................5
CONCLUSION ...................................................................................................................................11
4. The united states stock and Oil Prices4
Introduction
Many studies concentrate on the macroeconomic variables and their direct effects on the stock
market. The vague effect on stock market due to change in oil price as well as other economic factors
might lead to delay of investor decision to go further for investment sin the stock market. As oil price
have a direct effect on other macroeconomic factors, hence oil price increase will lead to inflation and
reduction in customers spending. On other hand, increase in money supply leads to an increase in
liquidity for purchasing securities, while an increase in money supply leads to inflation which will
lead to higher interest rates and a fall in stock prices (Billah Dar, Shah, Bhanja & Samantaraya,
2014). Industrial production followed by company high sales and increase in profit will lead an
increase in company capital and stock prices that Leeds to confidence in investment of stock market.
This is witnessed in the economy when oil prices change; it is accompanied by changes in food
prices, stock price and a general improvement in the performance of the sectors of economy
(Büyüksalvarci & Abdioglu, 2010).
Literature review
Oil prices play a key role in the performance of the world economy. Studies have found
out that changes in oil prices affect consumer behaviors (Anoruo & Elike, 2009). When oil prices
decrease, consumers spend more on luxuries, which in turn drives the stock prices of companies
offering luxury goods. The prices of oil stocks drop since the companies record reduced income
that translates to low profits or even losses. Therefore, investors are lose faith in them and off
load them leading to a drop in their stock value. This shift in prices usually results to negligible
changes in overall stock index (Kapusuzoglu, 2011). This is because some stocks drop in price
while others increase. There are those whose companies are not affected in any way by the
5. The united states stock and Oil Prices5
changes in stock price. When oil prices increase, the stock prices of oil companies increase.
Consumers spend a significant amount of their money on oil leaving them with little money to
spend on luxuries. Consequently, the stocks of luxury good producers remain constant or do not
record significant changes (Asteriou, Dimitras & Lendewig, 2013).
METHODOLOGY
Historical data of the United States of America was collected from 1959 to 2014 to test
the significance of oil prices in the United States in determining the return on the U.S. stock
market. Theoretically, oil prices have been seen to impact greatly on prices of most items in the
market and we therefore wanted to see what impact it has had on the U.S. stock market.
Considering that oil prices affect most of the major indicators of a country’s development rate,
we develop a model to determine the impact of oil prices on various historical indices as follows:
OP=∝ + βGDP+β2IG+β3CPI+β4PCI+β5PCE+β6NE+ε;
Where;
OP is the adjusted inflation price of oil;
GDP is the gross domestic product;
IG is the industrial growth;
CPI is the consumer price index;
PCI is the per capita income;
PCE is the personal consumption expenditure;
NE is the Net Exports; and
ε is the error term .
On running a Pearson correlation test of the variables, the following was obtained:
6. The united states stock and Oil Prices6
Correlations
Nominal
Price
Inflation
Adjusted
Price
GDP Indus
Growth
consum
er price
index
per
capita
income
personal
consumptio
n
expenditure
net
exports
Nominal Price 1 .826** -.399** .764** .820** .779** .800** -.795**
Inflation
Adjusted Price
.826** 1 -.406** .435** .486** .460** .482** -.471**
GDP -.399** -.406** 1 -.570** -.549** -.604** -.580** .339*
Idus Growth .764** .435** -.570** 1 .974** .990** .972** -.871**
consumer price
index
.820** .486** -.549** .974** 1 .986** .998** -.856**
per capita
income
.779** .460** -.604** .990** .986** 1 .987** -.854**
personal
consumption
expenditure
.800** .482** -.580** .972** .998** .987** 1 -.833**
net exports -.795** -.471** .339* -.871** -.856** -.854** -.833** 1
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
7. The united states stock and Oil Prices7
As can be observed, the inflation-adjusted prices are correlated to all the variables listed. The
correlations however are negative for GDP and net exports but positive for all the other
variables. From this, we can comfortably say that since none of the variables exhibits a
correlation higher than o.65, the multi-collinearity does not need to be performed for this data.
Next, we carry out a linear regression analysis of the variables using the model obtained above.
Model Summaryb
Mode
l
R R Square Adjusted R
Square
Std. Error of the Estimate
1 .619a .383 .307 21.35042
a. Predictors: (Constant), net exports, GDP, personal consumption expenditure,
IndusGrowth, per capita income, consumer price index
b. Dependent Variable: InflationAdjustedPrice
From the SPSS output on the summary, we can tell that the 6 predictors used explain on
38.3% of the impact of oil prices on the stock market. This means that the variables have
quite an impact on the oil prices though they may not be the only predictors.
The coefficients of regression were obtained as follows:
8. The united states stock and Oil Prices8
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 99.221 38.234 2.595 .012
GDP -.041 .016 -.448 -2.532 .015
IndusGrowth -1.180 .956 -1.147 -1.235 .223
consumer price index .904 .954 2.429 .948 .348
per capita income -.001 .004 -.502 -.361 .720
personal consumption
expenditure
-.855 2.194 -1.026 -.390 .698
net exports -.055 .032 -.521 -1.723 .091
a. Dependent Variable: Inflation AdjustedPrice
9. The united states stock and Oil Prices9
It can be observed that the coefficients of regression are negative with only one positive
for consumer price index. This means that the other variables have a decreasing impact on
the prices of oil as opposed to the consumer price index. Therefore, we can say that GDP,
industrial growth, per capita income, personal consumption expenditure and net exports
have negative significance but the consumer price index has a positive significance on oil
prices.
We can further determine the auto-correlation of the variables using the Durbin-Watson
statistic test. The following results were obtained:
Model Summaryb
Mode
l
R R Square Adjusted R
Square
Std. Error of
the Estimate
Durbin-
Watson
1 .619a .383 .307 21.35042 .368
a. Predictors: (Constant), net exports, GDP, personal consumption
expenditure, IndusGrowth, per capita income, consumer price index
b. Dependent Variable: InflationAdjustedPrice
Autocorrelation is used to determine the correlation error terms or residuals over
time and it violates the regression assumption that residuals are random and independent.
We can therefore use this test to tell how useful our regression model is by taking the
Durbin-Watson statistic, which is obtained as 0.368. Theoretically, the statistic ranges
from 0 to 4 and indicates the strength of the autocorrelation. If the value is between 0 and
2, there is positive auto correlation while if it is between 2 and 4, there is negative
autocorrelation.
Since our value stands at 0.368, then there is positive autocorrelation.
On testing for heteroskedasticity, the White’s test was used. From the diagram above,
Number of observations = 55 and R^2=0.383.
10. The united states stock and Oil Prices10
From the tables of Chi-square, we can average the chi-square as 11.514. Comparing the two
values with df=54 and 0.05 level of significance, we can clearly justify that there is very high
heteroskedasticity in our model.
This can also be observed from the scatterplot shown below which shows a non-uniform and
unequal distribution as the values increase in our model.
11. The united states stock and Oil Prices11
CONCLUSION
From the tests carried out, we can therefore say that most of the variables in our regression
model had negative coefficients, which include GDP, Industrial growth, per capita income,
personal consumption index and net exports. These therefore impact the oil prices negatively.
Only consumer price index had a positive coefficient hence showing that it had a positive impact
on oil prices. However, it is noteworthy that only GDP had a significance of 0.015 which is less
than 0.05 hence is the only significant predictor of oil prices in the U.S. according to the model.
All the other predictors have higher significances hence cannot be relied upon as significant
predictors.
It is also noteworthy that the variables used in the model only account for 38.3% of the
dependent variable, hence there are other variables, which strongly impact oil prices in the
market.
On multicollinearity, it was observed that none of the variables has a correlation higher or equal
to 0.65, hence we can say that no multicollinearity exists between the variables in the model.
On heteroskedasticity, the values obtained from the chi-square test were used to compare with
value of R^2 and the null hypothesis was rejected. We therefore, conclude that very high
heteroskedasticity exists between the variables. The scatter plot obtained also can be used
informally to explain the same finding
12. The united states stock and Oil Prices12
Bibliography
Anoruo, E., PhD. & Elike, U., PhD. 2009, "An Empirical Investigation into the Impact of High
Oil Prices on Economic Growth of Oil-Importing African Countries", International Journal of
Economic Perspectives, vol. 3, no. 2, pp. 121-129,152.
Asteriou, D., Dimitras, A. & Lendewig, A. 2013, "The Influence of Oil Prices on Stock Market
Returns: Empirical Evidence from Oil Exporting and Oil Importing Countries", International
Journal of Business and Management, vol. 8, no. 18, pp. 101-120.
Billah Dar, A., Shah, A., Bhanja, N. & Samantaraya, A. 2014, "The relationship between stock
prices and exchange rates in Asian markets", South Asian Journal of Global Business
Research, vol. 3, no. 2, pp. 209.
Büyüksalvarci, A. & Abdioglu, H. 2010, "The Causal Relationship between Stock Prices and
Macroeconomic Variables: A Case Study for Turkey", International Journal of Economic
Perspectives, vol. 4, no. 4, pp. 601-610.
Kapusuzoglu, A. 2011, "Relationships between Oil Price and Stock Market: An Empirical
Analysis from Istanbul Stock Exchange (ISE)", International Journal of Economics and
Finance, vol. 3, no. 6, pp. 99-106.
13. The united states stock and Oil Prices13
Appendixes
Historical oil prices
Annual Average
Domestic Crude Oil Prices (in $/Barrel)
Inflation Adjusted to November 2014
1946-
Present
Year Nominal
Price
Inflation Adjusted
Price
1946 $1.63 $19.41
1947 $2.16 $22.81
1948 $2.77 $27.21
1949 $2.77 $27.48
1950 $2.77 $27.19
1951 $2.77 $25.20
1952 $2.77 $24.64
1953 $2.92 $25.72
1954 $2.99 $26.30
1955 $2.93 $25.80
1956 $2.94 $25.56
1957 $3.14 $26.38
1958 $3.00 $24.55
1959 $3.00 $24.30
1960 $2.91 $23.26
1961 $2.85 $22.52
1962 $2.85 $22.25