been developed to study the relationship between oilprices and stock market indices. Some authors investigate the short-term influence of oil price changes on stock market returns using VAR models. Abu Zarour (2006) finds that oil price changes affect stock market returns in Saudi Arabia and Oman. Bashar and Sadorsky (2006) find also that only the Saudi and Omani stock markets have predictive power of oilprices. Arouri et al. (2011) estimate a VAR-GARCH for six GCC countries. They find that oil price changes affect positively the stock market returns in Bahrain, Oman and Qatar. In particular, they show that this effect is more pronounced during crisis period than normal one. Few authors like Lescaroux and Mignon (2008) find that oilprices do not cause share prices in sense of Granger in Oman, Qatar, Saudi Arabia and UAE. A similar result is found by Akoum et al. (2012) for six GCC countries. Wang et al. (2013) find that the response of stock market returns to oil demand shocks is significant in Saudi Arabia and Kuwait. Other authors test the presence of long-term relationship between oilprices and stock market indices using cointegration techniques. Hammoudeh and Aleisa (2004) find that oilprices and stock market indices are positively cointegrated in Saudi Arabia. For Bahrain, Kuwait, Oman and UAE, there is no significant cointegration relationship. Similarly, Hammoudeh and Choi (2006) examine the long-term relationship between stock market indices, US oil price, SP500 index and US Treasury bill rate. They find that US Treasury bill rate has a direct effect on these markets while oilprices and SP500 index have indirect effects. Using bootstrap panel cointegration tests and seemingly unrelated regression method, Arouri and Rault (2011) find that positive oil price shocks have positive impact on the stock market.
- In Europe, as in the US, there is a spot gas market, (a deal with immediate settlement): its price does not depend on the prices of possible substitutes. In this market short-term contracts are valid for a maximum of 2 years. Part of the volume of Russian gas, mainly LNG, is also sold on the spot gas market on the trading floors of the UK, Belgium, the Netherlands and France. But at present the European gas market is not ready to switch to the competition “gas-gas” as the main pricing mechanism (IGU, 2014). The bulk of Russian natural gas is sold to Continental Europe through long-term contracts (Konoplyannik, 2013, 2014).
This study sought to examine the impacts of oil price volatility on the economical development in Libya using a sample of observations from 1986 to 2016 through using a vector error correction methodology. To this end, a unit root test was conducted, in which data were shown to be non-stationary in all levels, and stationary in the first difference for all variables. Moreover, the cointegration model was applied, and the results showed that one cointegrating equation exists, suggesting the long-term effects of crude oil price on the agriculture, construction and manufacturing sectors. Based on the Granger causality test, oil price volatility can affect agriculture. Crude oilpricesinstability also effects the performance of the agriculture sector. Besides, the construction sector was found to be dependent on crude oil price. Our study found results that are similar with those of Torul and Alper (2010); Mehrara and Sarem (2009). Based on the results of this paper, this paper has an important implication for the Libyan economy in formulating policies on crude oil price fluctuations. The Libyan government must policies take that grow dramatically and diversify their economic base. This should go hand in hand with measures needed to enhance their capacity to withstand adverse external shocks and reduce their exposure to the fluctuations, reduce dependence on oil.
Three, is there a stable definition of money aggregates that incorporate all financial innovations? Some observers reckon that the money supply M1 must be revamped once again (Lucas and Nicolini 2015), after it suffered from what was dubbed the missing money phenomenon (Goldfeld et al., 1976). Narayan (2008) finds better stability with the usage of the M2 stock of money. However he finds also that the function breaks down during the last years of his sample, i.e., before 2014. Davis et al. (2013), using quarterly data find a break outside which the relation is unstable, and within which a stable relation exists. Foresti and Napolitano (2013) surmise that by including a wealth variable the money demand function becomes stable. In this regard a quote from Benati et al. (2017) is appropriate: “Yet, over recent decades many economists have come to the view that monetary aggregates convey no useful information and have turned to macroeconomic models in which measures of money do not appear at all. One driver of this change was the alleged instability of the relationships between these series.”
3 Rahman, 2016; Apergis and Vouzavalis, 2018). Delpachitra (2002) is one of scarce studies that shows that price adjustments in the domestic market do not respond effectively to changes in the international oilprices. By contrast, they report that domestic wholesale prices are the key to determining retail prices. Thus, the lack of competition in the wholesale market was found to be the main cause of the weak adjustment of retail prices. Galeotti, et al. (2003) and Kaufmann and Laskowski (2005) also focus on the refining industry, although they reach to different conclusions. The former study focuses on five European countries (Germany, Spain, France, Italy and the UK) and show that asymmetric behaviour is evident in both the refining and distribution stages. By contrast, Kaufmann and Laskowski (2005) study the US market and they show that the refining margin does not exhibit any asymmetric behaviour towards changes in the crude oilprices. More recently, Balaguer and Ripollés (2012) find evidence in favour of a symmetric behaviour of retail fuel prices to changes in the wholesale prices.
exchange rate change. At the same time, Vellianitis- Fidas (1976) made a cross-sectional study by using the stepwise ordinary least squares (OLS) method with the data from various time periods. In sum, both Kost and Vellianitis-Fidas agree that the US devalua- tions of the dollar were not the cause of high prices in 1972–1973. Chambers (1981) utilized a regres- sion to test the Granger causality among the money supply, agricultural exports, agricultural imports and interest rates. His findings were important and were consistent with others’ findings that the money supply/value of the dollar plays some role in the level of the agricultural trade. In contrast, Batten and Belongia (1984) support the view that the exchange rates do not matter. Batten and Belongia argue that the real stimulus for the export demand comes from the income enhancements in the importing countries. Chambers (1984) developed a theoretical model ca- pable of examining the short-run effects of various monetary policies on the agricultural sector. Also a Vector Auto Regression (VAR) model was created to help solve the statistical problem. Kwon and Koo (2009) explored the reason of the surges of the food prices based on the method proposed by Toda and Yamamoto (1996) of the Granger causality tests. They find that the food prices are affected by the exchange rate and energy prices through various channels which
The movement towards liberalization, privatization and globalization have strengthens the foundation and relatedness of stock market in the whole world since 1990s specifically in developed, emerging and developing countries. Around-the-world the need, emergence and popularity of stock-market considered as well-known fact and as such need arises among investors, researchers, academicians and policy makers to identify more specifically about the actual or real factors affecting the stock market prices. It is known that market-prices of stocks are driven by „information‟ and information is highly sensitive and can be affected by known and unknown factors from all around the world. Also, it could be said that stock market „react before‟ and „interpret after‟ about the right and wrong information and finally perform corrective action if required according to interpretation. Now it is essential to add today‟s most important key element which makes this market to run at a speed of rocket which is, „information technology‟, through which whole world is connected so tight that if little vibration is their at one
Our main findings are that: First, on average, gas import prices are passed through fully in to consumer gas prices, albeit with some lag. An implication of this is that the elasticity of consumer prices with respect to upstream prices is an increasing function of their level. Second, accounting for the regular calendar for price ‘resetting’ significantly improves the fit of the estimations. However, this cannot be done via regular seasonal dummies but they must be interacted with the error correction term as the reset adjustment may be up or down. Third, in general the fit of the estimations is higher using oilprices as the explanatory variable rather than gas import prices. This may be because gas price setters take into account the signal from current oil price levels when resetting their prices, whereas gas import prices are generally backward looking as in Europe they are mostly index-linked to oil price developments in the preceding months. Fourth, regarding cross-country patterns, there is some evidence of differences which may be linked to differences in the degree of market liberalisation. Lastly, whilst there are signs of some changes in historical long-term relationship between oilprices and gas import prices, these are too recent to be identified robustly using the relatively low frequency, aggregate data we utilise in this paper.
This paper examines fluctuations in crude oilprices and how exogenous shocks (news) to these fluctuations may have a permanent effect on them and how they may be affected differently by good or bad news. The paper uses daily crude oil price data from the past decade to test the volatility of crude oilprices. Tests show high persistence and asymmetric behavior in oil price volatility indicating that positive and negative news have different impacts on future volatility of oilprices. This paper provides a better understanding of energy markets, especially the behavior of oilprices over time. The first section gives a brief introduction on the background and goals of this paper. The second section discusses some earlier research that supports some of the ideas and techniques employed in this paper. The third section describes the source of the data followed by the methodology used to obtain the results for this research. The fifth section discusses the empirical results displayed in tables and a graph. The paper concludes with some policy implications and final remarks.
There is a common belief that the price of commodities tends to move in unison. It is because they are influenced by common macroeconomic factors such as interest rate, exchange rate and inflation (Hammoudeh et al, 2008). Oil and gold, among others, are the two strategic commodities which have received much attention recently, partly due to the surge in their prices and the increase in their economic uses. Crude oil is the world’s most commonly traded commodity and its price is the most volatile in the commodity market. Gold is considered the leader in the market of precious metals as increases in its price seem to lead to parallel movements in the price of other precious metals (Sari et al, 2010). Gold is also an investment asset and commonly known as a “safe haven” to avoid the increasing risk in financial markets. Using gold is one of risk management tools in hedging and diversifying commodity portfolios. Investors in both advanced and emerging markets often switch between oil and gold or combine them to diversify their portfolios (Soytas et al, 2009).
As shown by Hamilton (2008), nine out of the last 10 recessions in the United States since World War II have been preceded by a rise in oilprices. This has not gone unnoticed by economists and has generated a substantial amount of research, particularly given the fact that oil consumption represents only 4% of GDP. 3 The study of the relationship between oil and the macroeconomy strengthens with the seminal work of Hamilton (1983). He uses Sims’s (1980) bivariate VARs and six-variable VARs with quarterly data for the 1948-1980 period to show that oilprices strongly Granger-caused the GDP growth rate and the U.S. unemployment rate. According to his calculations, an increase in the oil price is followed by four successive quarters of lower GDP growth rates. Gisser and Goodwin (1986) confirm these findings and reject the existence of a structural break in this relationship as a result of the OPEC embargo in 1973. As shown by Ferderer (1996), the common transmission channels of oil price shocks to the real economy are: inflation, terms of trade (Huntington, 2007) and the capital utilization rate (Finn, 2000). 4
The two effects also have intuitive, yet opposite effects on the overall level of risk in the economy. The increased risk associated with oil price shocks leads to an increase in overall economic risk. However, the high (low) expectations of future oilprices that accompany increases (decreases) in expected aggregate growth serve to mitigate the effect of these growth shocks, which serves to reduce overall risk. This effect, that oilprices act as a counterweight to changes in aggregate economic growth is one that has been central to discussions surrounding the financial crisis and the subsequent recovery. One of the few silver linings of the period following the ”Great Recession” of 2008 and 2009 was the significant reduction in oilprices created by lower demand. As the economy begins to grow again, there is concern that the high oilprices that come along with increasing demand have the potential to slow down the recovery. Though the intuition is not novel, the contribution here is to show that this effect is particularly strong in a LRR setting due to the highly persistent nature of the long-run aggregate growth shocks. In fact, in my calibrations I find that it is this second effect which dominates, and the total effect is a significant reduction the overall equity premium.
This paper is trying to analyze the determinants of housing prices in an oil-based economy, where the price of oil plays a major role in such economies. It is also common to find that government spending represents the most important component of aggregate spending and that governments usually play a central role in the provision of public services to citizens at substantial subsidies, housing is on top of them. The paper is trying to identify the role played by oil price in the housing market in Kuwait. The model is composed of four major determinants of house prices including the price of oil, government expenditures, inflation rate and interest rate. Results confirm the role played by the four factors in determining the price of houses. Variance decomposition indicates that up to 10 quarters, 94.3% of the forecast error variance in housing prices is explained by house price itself, whereas, only 2.3%, 1.6%, 1.5% and 0.8% are explained by Interest rates, inflation rates, government expenditures, and price of oil respectively. The oil price does not seem to play an important impact on price changes in Kuwait. One important recommendation is for the government to relax its monopoly on land and invite the private sector to come up with housing solutions to increase the supply of houses in the private housing market and reduce the upward pressures on house prices.
Crude oilprices have been fluctuating over time and by a large range. It is the disorganization of oil price series that makes it difficult to deduce the changing trends of oilprices in the middle- and long-terms and predict their price levels in the short-term. Following a price-state classification and state transition analysis of changing oilprices from January 2004 to August 2009, this paper first verifies that the observed crude oil price series during the soaring period follow a Markov Chain. Next, the paper deduces the changing trends of oilprices by the limit probability of a Markov Chain. We then undertake a probability distribution analysis and find that the oil price series have a log-normality distribution. On this basis, we integrate the two models to deduce the changing trends of oilprices from the short-term to the middle- and long-terms, thus making our deduction academically sound. Our results match the actual changing trends of oilprices, and show the possibility of re-emerging soaring oilprices.
This study examined the relationship between oilprices, interest rate, exchange rate and other macroeconomic variables and stock market in Srilanka. Monthly data of oilprices, interest rate, and total oil consumption of the country, exchange rate and stock market indices are modeled into a linear regression model. The secondary data of this research for diesel prices obtained from Petroleum cooperation in Srilanka, data of oil consumption obtained from the ministry of energy, the data of exchange rate and the interest rates obtained from the central bank of Srilanaka. Pearson correlation and regression were used to test the relationship between that both diesel prices and interest rates have significance relationship with stock market in SriIanka. However, when the relationship is positive to the diesel prices, the relationship of the interest rates is negative. The finding also indicated that total oil consumption and the exchange rates are positive relationship with the stock market operation in Srilanka, that finding also indicated that a very strong relationship between diesel prices and exchange rate.
The main objective of the study is to ascertain the implications of dwindling oilprices in the Nigeria economy. This study adopts descriptive statics such as mean and standard deviation and inferential statistics which include Regression and Correlation analysis. Operational variables used in this study are gross domestic product (GDP) as a measure of national productivity and crude oil, industrial/services products, and capacity utilization. The study thereby concluded that dwindling oil price indicators significantly influence Nigeria Economic Development within the study period and that dwindling oil price and Nigeria economy exhibit significant relationship within the study period. In the light of the above findings, the study recommends that the Government should give a clear economic policy direction to develop and assist key financial industry players in stabilizing the financial economy. Also, the budget should be built based on the prevailing economic realities occasioned by the oil price fall to ensure prudency and accountability and to discourage wasteful allocation of the meager resources to non-productive expenditures.
on the U.S. producer price index of oil. Oil price changes based on this index demonstrate up to three-month lags in movements compared to WTI spot price changes. Hence, if investors use different measures for oil price information, their actions will have very different outcomes which compare favorably with predictions of the underreaction hypothesis. In our study, we find evidence in favor of the hypothesis that investors may find it hard to analyze the information contained in oil price changes in industries which seem to be less oil-dependent, such as telecom or construction. We do have a puzzling outcome in our results. One expects the automotive industry to closely follow and immediately incorporate information contained in oil price data. But this is not the case. Oil price changes predict returns of automotive industry quite well. We believe that this, at first glance puzzling, result is due to difficulty of accurate assessment of secondary oil effects on profitability of the automotive industry. We believe that oilprices satisfy the criteria of Hong and Stein (1996) model. We empirically test the underreaction hypothesis, following Driesprong et al. (2008) steps.