Preface and acknowledgements
Literature Review 2.1. Oil prices and the economic growth
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- 2.2. Oil prices and the stock market
- 2.3. Fama French three factor model
2. Literature Review2.1. Oil prices and the economic growthAmong the extreme supply shocks impacting the world economies, since the 1970s there has been a sharp increase in the price of oil. Hamilton (1983) stated that oil supply shocks were a contributing factor to the recession in the USA. Kim et al. (1992) also supported the research suggesting that these shocks had played a significant role with regards to economic fluctuations over the past three decades. However, Hooker’s (1996) contribution only showed its relationship specifically when oil prices failed to Granger cause macroeconomic variables when data samples were extended past the mid-1980s. This was due to the decreasing nominal price in 1981, followed by a market collapse in 1985 which the relationship between the increase in oil and economic fluctuations seemed less convincing. This instability problem has been investigated by several authors such as Mork (1994) and Hamilton (1996). They argued that a breakdown of the oil prices-macroeconomics relationship reveals a non-linear relationship, thus proposing an alternative so-called asymmetric relationship. Cunado et al. (2003) used this transformation to investigate the oil price-economic growth relationship within some European and Asian countries. Hamilton (2003) declared that since the oil prices and macroeconomics relationship is non-linear, the effect of oil price increases is bigger than oil price decreases. Furthermore, Hamilton developed a flexible approach for capturing the relationship between oil price shocks and economic growth in post-war US data. Many papers have investigated the relationship between price of oil and economic growth in Japan. Early studies involved linear relationship models. Darby (1982) estimated the long run oil effects in 1975 and a year after. The results attributed to a decrease in real income relative to the increase in oil price. Burbridge and Harrison (1984) used a seven–variable auto regressive (VAR) model to investigate the response of industrial production to an oil price shock from 1976-1982. However, various recent studies have shifted to non-linear studies for investigating the relationship between oil price and economic growth. Mork (1994) pioneered a non-linear study and investigated the existence of asymmetry in the relationship between oil prices and economic growth in Japan. Lee et al. (2001) argued that approximately around 30% to 50% of the negative impacts on Japanese output in the mid 1970s were triggered by monetary policies after the oil price shocks. Rodriguez and Sanchez (2005) used a seven-variable autoregressive (VAR) model to estimate the linkage between the GDP and the oil price shocks that occured from 1972-2001. They found a negative correlation between oil prices and the Japanese GDP. Cunado and Garcia (2005) analyzed the granger-causality between oil prices and macroeconomic variables (economic growth and inflation) for six Asian countries over the period 1975-2002. They summarized that oil prices have a significant effect on both macroeconomic activities, even though the impact is limited to the short run and is more significant if oil price shocks are defined in local currencies. They also examined a net exporter (Malaysia) and net importers (Japan, Singapore, South Korea, Thailand, and Phillipines) to see whether an oil price-macroeconomy relationship in emerging Asian countries is dependent on the difference between net import and net export behaviors of each country. In addition, Hanabusa (2009) investigated the price of oil is useful in predicting in economic growth rate in Japan. The change in the oil price can give information for the domestic economy. As has been suggested, oil prices influence the macroeconomic variable. This impact is perceived sequentially by major industries in the local as well as global economy. There are some industries which use oil as an input (e.g. petroleum refinery and chemical industry), and there are those which use oil as an output (e.g. petroleum industry). Thus, the shocks can either present itself in the supply side or the demand side. Lee and Ni (2002) investigated the effects of oil price shocks on supply and demand in 14 manufacturing industries. Using VAR models, they indicated that where industries using oil as an input, oil price shocks severely reduced supply. In other industries (e.g. automobile industry) the contrary occurred where oil price shocks reduced demand. This study was expanded by Rodriguez (2008), who examined the dynamic effects of oil price shocks on outputs of manufacturing industries in six OECD countries. The results demonstrated that the industrial outputs of France, Germany, Italy, and Spain had a diverse pattern of responses to oil price shocks, yet they were widely similar in the UK and the USA. 2.2. Oil prices and the stock marketApart from the studies revealing that oil price shocks have a significant impact on an economy’s performance, relatively few economists have provided market participants with a framework that identifies how oil-price changes affect the stock market. Jones et al. (1996) gave a theoretical grounding, suggesting that oil price shocks affect stock market returns through their expected earnings. They focused on testing the reaction of advanced stock markets (US, UK, Japan, and Canada) to oil price shocks on the basis of the standard cash flow dividend valuation model. They found that for the US and Canada, the reaction can be ascertained by the impact of oil shocks on cash flow. The outcome for Japan and the UK on the other hand were indecisive. Huang et al. (1996) adjusted an unrestricted VAR model, which confirmed that a significant relationship between some US oil company stock returns and oil price changes. However, they found no evidence of a relationship between oil prices and the S&P 500 market. Sadorsky (2001) used a multifactor market model and considered the risk premium, exchange rates, and interest rates, along with oil prices themselves, as major determinants of oil and gas stock returns. He reported a statistically significant positive relationship between oil prices and stock returns of oil and gas firms. El Sharif et al. (2005) investigated the correlation between oil prices and stock returns of companies that listed on the London Stock Exchange. Through the empirical findings they found that there was a significant positive association between oil prices and oil-related stock returns. Notably, stock prices exhibit asymmetrically to changes in oil prices. This means that higher oil prices are negatively related with stock prices. For example, Papapetrou (2001) studied the dynamic interaction between oil price, real stock prices, and interest rates in Greece. Papapetrou found that oil price shocks have a negative impact on stock prices since they negatively affect the output in the form of industrial production and employment growth. Maghyereh et al. (2007) found that oil prices affected the stock prices indexed in the GCC (Gulf Cooperation Council) countries, in a nonlinear fashion, and supported the statistical analysis of a non-linear modeling relationship between oil prices and the economy. This result was also consistent with the outcome from Mork et al. (1994). Nandha and Faff (2008) argued that oil price shocks can have adverse effects on a firm’s output, and therefore on a firm’s profitability and also revealed that increases in oil prices negatively impact stock returns for all industries including mining, oil, and gas industries. Furthermore, Bjornland (2008) denoted that oil prices may affect stock prices only through an indirect manner via monetary policy shocks, whilst Cong et al. (2008) found evidence that oil prices do not show significantly impact on the majority of stocks in the Chinese markets. They did however find an association within the manufacturing sector. The above results indicate a positive or a negative relationship between oil price shocks and stock returns, which should come as no surprise. Understandably, it is confirmed that the price enhances the cash flow of oil firms and proves beneficial for them. More precisely, there was Park and Ratti’s (2008) detailed study on the effects of oil shocks on stock markets in the US and 13 European countries, using monthly data within the period 1986 to 2005. Their results showed a statistically significant impact of oil price shocks on real stock returns within one month of the event date. Using similar evidence, Miller and Ratti (2009) delved the long run relationship between the crude oil prices and international stock markets within the period 1971 to 2008. They observed a clear long run relationship for six OECD countries, and suggested that stock market indices respond negatively to increases in the oil price over the long run. Nevertheless, this seems to be less likely after year 1999. The findings supported a presumption of change in the relationship between real oil prices and stock returns in the last decade which might suggest the presence of stock market bubbles and/or oil price bubbles since the turn of the century. Thus, by looking at this linkage between oil price shocks and stock returns, it can lead several investors to predict the direction of the stock market in case of an unexpected move in oil prices. To study the affect of oil price changes on stock fluctuations in oil importing countries, several researchers distinguish between developed countries and developing countries, in response to oil price volatility. Maghyereh (2004) discovered the relationship between oil prices, and stock market returns for 22 emerging countries within the period 1998 to 2004. He showed that an elevated intensity of energy consumption in a country results in an elevated response to oil price changes. Maghyereh conducted the experiment based on the efficient market hypothesis, which states that stock markets in emerging markets are inefficient in the conveyance of new information with regard to the oil market, and stock market returns in these markets are not rationally alert to changes in crude oil prices. Park and Ratti (2008) found that oil prices play a critical role in the stock market of oil importing countries. Furthermore, they summarized that stock markets in oil exporting oil countries are less affected by oil prices relative to oil importing countries and are also less sensitive to interest rate changes. Additionally, Fayyad and Daly (2010) performed an investigation into the relationship between oil prices and stock market returns, comparing GCC countries with the UK and USA, by applying VAR analysis. They employed daily market data from September 2005 until February 2010. Their empirical findings suggested that the predictive power of oil for stock return are aggravated after a rise in oil prices and during the Global Financial Crises. Hereafter, they also concluded that Qatar and the UAE show more perceptiveness to oil shocks with regard to the GCC countries; a relation which also holds between the UK and developed countries. 2.3. Fama French three factor modelIn the portfolio management field, Fama and French have researched extensive studies on the subject of equity price returns. These studies aimed at enhancing the results explained by CAPM, which uses a single factor, beta, to compare the excess returns of a portfolio with the overall market return. In comparison, Fama and French (1993) presented three factors model and Fama and French (1996) gave an outstanding summary and showed that along with the market risk premium, most of the returns in a portfolio can be determined by cross section returns on stock, using market capitalization and book to market value factors. They started with the observation that: (i) the small cap stocks are represented by SMB (small minus big), and HML (high minus low) factors. Small firms have low market capitalization. Value stocks have low market value relative to fundamentals (earnings, dividend, and book value) and has given higher average returns over growth stocks which have high market value relative to fundamentals. However, Cochrane (2005) stated that book values essentially track past investment expenditures so that book values is a better divisor for individual firm than other fundamentals. They ran regressions on stock return data from the period 1963 until 1990. Their analysis reveals that small cap stocks and high book-to-market equity stocks have higher average returns, because of unmeasured risk factors. Consequently, market capitalization and book-to-market value were also indeed proxy for sensitivity to common risk factors in stock returns. Fama and French (1996) argued that in their three factors model, size and Book-to-Market ratios play an important role in explaining cross sectional differences in expected returns for non financial firms. As a result, the Fama and French three factors model explains the expected stock returns in any markets better than the CAPM model. Cochrane (2005) argued that CAPM model that use portfolio returns might be successful in describing asset returns, but the model might not be able to explain these returns, because the model leave suspicious questions on the rationale behind the return-based factors. Not all researchers concluded that those factors provide a satisfactory explanation for the size (SMB) and Book-to-market (HML) factors in the three factor model. Lakonishok, Shleifer, and Vishny (1994) suggested that the prices of low book-to-market stocks are more fascinating than high book-to-market stocks, as proposed by Fama and French (1993). Thus, it may attract naive investors who push up prices and lower the expected returns of the securities. Knez and Ready (1997) found that the risk premiums of size which estimated by Fama and French (1993) disappear. They also explained for further research that size effect may also involve into the firm’s growth. Another research was conducted by Daniel and Titman (1997), who suggested that the risk premiums on small size and high book-to-market do not arise because of the co-movements of these stocks with pervasive factors. Moreover, Daniel, Titman, and Wei (2001) also rejected the Fama and French three factor model by using the Japanese capital market. The asset pricing model does not compare all possible outcomes. It only considers the mean and variance for the outcomes. Therefore, mean-variance framework is clearly not a good description of reality because the assumption of normal distribution is not possible in the reality. Investors should think about the higher moments for instance skewness and kurtosis that also determine the risk factor. However, investors still use the asset pricing model due to its simplicity and the framework is hard enough for some people. In addition, Tsiang (1972) exhibited that the asset pricing approximation is valuable although it is not a good description of reality. Tsiang argued that the asset pricing model which considers mean-variance approximation will only fail in extreme observation, in other words the extreme observation is rare. The above literatures examine the relationship between oil prices and stock markets. Additionally, they examine how this impact is perceived across various industries. Nonetheless, there are still a few papers which discuss the impact of oil prices on the transportation industries, which are some of the heaviest consumer of oil. Cameron and Schnusenberg (2008) investigated the relationship between the oil prices and stock prices of automobile manufacturers in the US. They added an oil price factor to the Fama and French three factor model over the period March 2001 to September 2008. The factor was measured by the change in WTI crude oil prices in excessive risk free rate, or alternatively was measured by the excess return on energy ETF. The index comprising of SUV vehicles was chosen as the dependent variable. In general, their result exhibited an inverse relationship between oil prices and stock returns of automobile manufacturers in the US. The result was statistically significant for manufacturers of SUV vehicles, while using the excess return on energy ETF instead of price of crude oil as the fourth factor. They have divided their time period into before and after the start of the second Iraq war, when the impact of oil prices on the stock returns of automobile manufacturers in the US seemed to be notably coherent following the invasion of Iraq in March of 2003. In summary, there are various theories on what relationship between crude oil price and economic implications are. Nevertheless, there is still limited consensus explaining the relationship between crude oil prices and stock returns of automobile manufacturers in Japan. Directory: pub pub -> Project Scoring Template Pre-Construction Phase New Core/Branch Campus Projects pub -> Acm word Template for sig site pub -> The german unification, 1815-1870 pub -> Baltic Olympiads in Informatics: Challenges for Training Together pub -> Preparation of Papers for ieee transactions on medical imaging pub -> Forthcoming meetings and conferences pub -> Asilomar Conference on Circuits, Systems & Computers, Pacific Grove, ca, November 1978, pp. 55 58 pub -> Forthcoming meetings and conferences pub -> Harmonised compatibility and sharing conditions for video pmse in the 7 9 ghz frequency band, taking into account radar use Download 420.96 Kb. Share with your friends:
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