رابطه علی میان قیمت نفت و بازار سرمایه در ایالات متحده آمریکا: رژیم سوئیچینگ مدل
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|12565||2013||12 صفحه PDF||سفارش دهید|
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 12784 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Energy Economics, Volume 39, September 2013, Pages 271–282
The aim of this paper is to analyse the causal link between monthly oil futures price changes and a sub-grouping of S&P 500 stock index changes. The causal linkage between oil and stock markets is modelled using a vector autoregressive model with time-varying parameters so as to reflect changes in Granger causality over time. A Markov switching vector autoregressive (MS-VAR) model, in which causal link between the series is stochastic and governed by an unobservable Markov chain, is used for inferring time-varying causality. Although we do not find any lead–lag type Granger causality, the results based on the MS-VAR model clearly show that oil futures price has strong regime prediction power for a sub-grouping of S&P 500 stock index during various sub-periods in the sample, while there is a weak evidence for the regime prediction power of a sub-grouping of S&P 500 stock indexes. The regime-prediction non-causality tests on the MS-VAR model show that both variables are useful for making inference about the regime process and that the evidence on regime-prediction causality is primarily found in the equation describing a sub-grouping of S&P 500 stock market returns. The evidence from the conditional non-causality tests shows that past information on the other series fails to improve the one step ahead prediction for both oil futures and stock returns.
There are mainly two reasons that might affect the stock market concerning the oil prices. Oil prices are the main determinant of global economic activities. The oil price shocks have a great impact on the economy implying that, oil has always been an essential part of economy. The higher the oil prices, the more channels appear to play vital roles in global economy. One of the significant roles is the transmission of prosperity from oil consumers to oil producers. The other factor is the increase in the cost of services and goods. Moreover, the effect of oil price shocks can be observed on consumer confidence, financial markets and inflation. The studies by Hamilton (1983) and Gisser and Goodwin (1986) reveal that the macroeconomy is adversely affected by oil price shocks, leading to economic recession. Unravelling the roles oil has in the economies of countries such as the US, one may infer that oil and stock prices are in a way interrelated. Provided that stock and oil markets are efficient, a contemporaneous correlation between stock and oil prices exists because of the swift reaction of markets to information shocks and the nature of investor expectations.2 Such markets are considered to be sensitive to the new information, yet a contemporaneous relationship between these markets is expected from a broader perspective. Therefore, this study aims to investigate the linkage between the stock and oil futures markets. Some of the empirical studies on the association between oil price and stock market are Chen et al. (1986), Kaul and Jones (1996), Sadorsky, 1999 and Sadorsky, 2001, Huang et al. (1996), Papapetrou (2001), Ciner (2001), El-Sharif et al. (2005), Boyer and Filion (2007) and Nandha and Faff (2008). Kaul and Jones (1996) and Sadorsky (1999) find that oil price movements affect U.S. stock returns. Huang et al. (1996) examine the dynamic linkages between the oil price and the U.S. equity market from a financial market perspective. They find that there is an only predictive power from oil futures to stocks of individual oil companies. However, the study by Ciner (2001) challenges Huang et al. (1996) referring to the further research on the international equity markets. Sadorsky (2001) asserts that oil price increases are sensitive to the stock returns of Canadian oil and gas companies. Papapetrou (2001) used impulse response functions and exhibited that the stock price movements in Greece is affected by the oil prices, and that a positive oil price shock is liable to depress the returns. El-Sharif et al. (2005) illustrated that how oil price has exerted a significantly positive impact on oil and gas returns in the UK. However, they found that the sensitivity of non-oil and gas sectors to the oil price shocks in the UK is weak. The forward and futures prices regarding contracts relating to oil have been discussed by a number of researchers, including but not limited to Bopp and Lady (1991), Farmer (1993), Moosa and AI-Loughani (1994) and Foster (1996). The part an oil price factor takes in the elucidation of the systematic impact on the price in equity markets has moreover been discussed in such empirical studies as Hamao (1989), Kaneko and Lee (1995) and Faff and Brailsford (2000). The inverse association between the oil prices and economic growth is documented effectively in the relevant literature (e.g. Gisser and Goodwin, 1986, Hamilton, 1983, Jones et al., 2004 and Zhang, 2008). Much of the research (e.g. Guidi et al., 2006, Mork, 1989, Mork et al., 1994, Mory, 1993 and Nandha and Faff, 2008) reveals that the impact of oil price changes on the macro-economy is asymmetric. This imply that oil price increases exert a negative impact on GDP, but the falling tendencies in oil prices do not necessarily result in a positive impact on the output. This suggests nonlinear linkages between oil prices and the stock market. Silvapulle and Moosa (1999), Ciner (2001), and Hammoudeh and Li (2004), noted that the results from the Granger causality tests appear to be sensitive with respect to the countries analysed, the sample period and the methodology employed. They point out that direction of causality is sensitive to the choice of the sample period. Also there are periods where no causality is found along with periods where bidirectional causality between oil futures returns and equity returns is found. Based on these findings, our strategy, then, consists of identifying the periods during which a particular type of causality holds, i.e., a period in which oil futures returns Granger causes stock market returns and vice-versa. This approach also allows us to identify the periods of non-causality and bidirectional causality. The methodology we adopt is based on a VAR model with time-varying parameters, which, given our objectives, directly reflects changes in causality. In this approach, the changes in causality are treated as random events governed by an exogenous Markov process, leading to the Markov switching VAR (MS-VAR) model. In the MS-VAR model, inferences about the changes in causality can be made on the basis of the estimated probability that each observation in the sample comes from a particular causality regime.3 The main purpose of this paper is to investigate the regime switching causal nexus between oil and equity markets for the US. The data used are the log returns of monthly crude oil futures contracts traded on the New York Mercantile Exchange and a sub-grouping of S&P500 index. This subgrouping includes the Industry Sector, Energy Sector, Energy Equipment & Services, Oil & Gas & Consumable fuels, Oil & Gas Exploration & Production and Oil & Gas Storage & Transportation indexes. The sample starts on 1995:01 and ends on 2011:07. However, for the Oil & Gas Exploration & Production Index and Oil & Gas Storage & Transportation Index the sample period starts on 1990:02 and ends on 2005:05. Our sample period covers at least five events4 that had large impacts on stock markets. In practice, contractions and expansions do take time. Empirically we would then expect switches between a normal regime and a transition regime. The causal links between the oil and the stock markets are likely to change and be asymmetric, which are better captured by nonlinear models, such as the MS. For that reason, we use the Markov-switching model partly motivated by the recent success of Markov switching (MS) models in describing time series properties of oil and stock market data. Both the oil and the stock markets frequently switch between contractions and expansions. MS-VAR models are, therefore, natural tools to study whether such switches may have occurred, and if they did, whether the causal links between the series have changed. The type of MS-VAR model we use not only indicates whether there are regime shifts and whether the causal links have changed over the sample period, it also allows us to make formal tests of mean-variance Granger causality within this time-varying parameter approach. The results obtained from the MS-VAR model clearly show that oil futures price has strongly positive predictive content for each of S&P 500 sub-index during various sub-periods in the sample, while there is weak evidence on the case of vice-versa. Moreover, the results of the empirical analysis suggest that there seems to be an asymmetric relation from oil futures price to each of S&P 500 sub-index returns during various sub-periods in the sample. The tests based on the MS-VAR model rejects the conditional Granger causality in both directions. The regime-prediction causality tests show that while both variables are useful in making inference about the regimes there is support only for regime-prediction Granger causality from oil price shocks to stock market. The rest of the paper is organised as follows. The next section presents the methodology. Section 3 estimates the model and evaluates the empirical results. The final section concludes.
نتیجه گیری انگلیسی
In this study, using a VAR model with time-varying parameters we carry out Granger causality analysis in situations where causality is non-permanent. We examine the causal relationship between oil prices and the U.S. equity market. The data used in the study are the monthly data of the crude oil futures contracts and a sub-grouping of S&P500 index, namely the Industry Sector, Energy Sector, Energy Equipment & Services, Oil & Gas & Consumable fuels, Oil & Gas Exploration & Production and Oil & Gas Storage & Transportation indexes. The findings from the switching model indicate that oil futures returns have a significant predictive power for each of the S&P 500 sub-index returns considered during various sub-periods in the sample, while there is a weak predictive content from each of the S&P 500 sub-index returns to the oil futures price. In addition, the findings reveal that there is no lead–lag relation between the oil futures price and each of the S&P 500 sub-index returns during various sub-periods in the sample, but each market has predictive power to infer the regime of the other. The regime predictive power of the oil market for the stock market is much stronger than the regime predictive power of the stock market for the oil market. The empirical results from the present study overwhelmingly support a nonlinear relationship between the oil price and economy, as suggested by Hamilton (2001). The paper uses a novel approach, based on an MS-VAR model that uniquely identifies all possible causality directions, to test the conditional lead–lag and the regime-prediction Granger causality between oil and equity markets. We obtain strong evidence against the existence of lead–lag relationships. However, the regime-prediction Granger causality tests obtain overwhelming evidence that although both the oil and stock market returns are informative about the regime, there is only full sample causality evidence from the oil market to the stock market. The results of the study point to the following two directions. One is that as far as the changes in the oil market and the developments in financial sector are considered, the findings on the time-varying nature of the predictive power of the financial variables are not unexpected. The second is that the causal relationship between the oil futures returns and the stock markets implies predictive power for both markets. The economic implications of the causal relationship with predictive content should be put under close investigation for efficient markets.