Scholarly article on topic 'An Investigation into the Crude Oil Price Pass-Through to the Macroeconomic Activities of Malaysia'

An Investigation into the Crude Oil Price Pass-Through to the Macroeconomic Activities of Malaysia Academic research paper on "Economics and business"

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Abstract of research paper on Economics and business, author of scientific article — Fardous Alom

Abstract This study examines the pass-through of crude oil prices (CP) into economic activities of Malaysia including industrial production index (IP), consumer price index (CPI), real effective exchange rate (REER), interest rate (IR) and stock price index (SPI) within the framework of hidden cointegration technique over the quarterly data ranging from 1987 to 2013. The estimated results suggest that positive and negative changes of IP, CPI, REER, IR and SPI do not maintain a long-run association with positive as well as negative changes of real CP. Although the negative changes in CPI, negative changes in IP and negative changes in REER are found to be cointegrated with the positive changes of CP the estimated signs of the error correcting terms do not provide enough evidence to support this provision.

Academic research paper on topic "An Investigation into the Crude Oil Price Pass-Through to the Macroeconomic Activities of Malaysia"

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Energy Procedia 79 (2015) 542 - 548

2015 International Conference on Alternative Energy in Developing Countries and Emerging

Economies

An Investigation into the Crude Oil Price Pass-Through to the

Macroeconomic Activities of Malaysia

Fardous Alom

Department of Economics, International Islamic University Malaysia, 50728 Kuala Lumpur, Malaysia

Abstract

This study examines the pass-through of crude oil prices (CP) into economic activities of Malaysia including industrial production index (IP), consumer price index (CPI), real effective exchange rate (REER), interest rate (IR) and stock price index (SPI) within the framework of hidden cointegration technique over the quarterly data ranging from 1987 to 2013. The estimated results suggest that positive and negative changes of IP, CPI, REER, IR and SPI do not maintain a long-run association with positive as well as negative changes of real CP. Although the negative changes in CPI, negative changes in IP and negative changes in REER are found to be cointegrated with the positive changes of CP the estimated signs of the error correcting terms do not provide enough evidence to support this provision.

© 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of the Organizing Committee of 2015 AEDCEE Keywords: crude oil price; economic activities; hidden cointegration; Malaysia

1. Introduction

Volatility in the commodity prices particularly CP is a matter of worldwide concern because the volatility adds different types of costs such as production cost, opportunity costs and search costs [1] and consequently generates tensions and uncertainty [2]. The CP shocks that have started in the 1970s attracted the attention of many researchers and it has been observed as one of the many reasons for global economic slowdowns, especially for net oil importing countries [3-5]. A general consensus has been emerged, although not perfectly beyond criticisms, indicating that increases in CP support declining economic activities in the oil importing countries. Converted crude oil or energy regards as an engine for economic activities [6], and so increases in its prices have direct impacts on many economic activities.

A significant body of literature has contributed to the study of oil price-macroeconomic relationships. In wider sense, there are two categories of studies on the impacts of CP shocks on economic activities such as economic growth and inflation in the case of the USA or Western Europe: studies that document evidence of negative impacts of CP shocks and those which report little or no evidence of impacts of the shocks. The first group begins with the pioneering works of Hamilton [3]. Applying Sims' [7] VAR approach to the US data for the period 1948-1980, the author shows that CP and the USA's gross national product (GNP) growth exhibit a strong correlation. The author also reports that oil price increased sharply prior to every recession in the US after World War II. Following Hamilton, a number of studies document the negative impacts CP had on the gross domestic product (GDP) of the USA [5, 8-12]. Negative impacts of CP shocks are reported under different market structures as well [13, 14]. Some studies

Corresponding author. Tel.: +603 6196 4635; fax: +603 61964850. E-mail address: fardous@iium.edu.my; fardousalom@gmail.com

1876-6102 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of the Organizing Committee of 2015 AEDCEE doi: 10.1016/j.egypro.2015.11.531

focus on the factor market and industry levels, recording adverse impacts of oil price on employment, real wages and industry outputs [15-20]. A number of studies deal with the magnitude and strength of the impacts of oil price shocks and reach an agreement that the impacts of earlier shocks, in the 1970s, are more severe than the latest shocks, of the 1980s or 1990s [21-24]. Studies outside the USA and Western Europe also report adverse impacts of oil price shocks [25-29].

Other studies focus on the relationship between oil price and exchange rates, some reporting evidence of Granger causality from oil price to exchange rates [30-33]. Yet others report that exchange rates influence oil price [34-37], while a few studies show that oil price does not have any relationship with exchange rates [38, 39].

There are also discussions on the association between oil price and stock prices. Jones and Kaul [40] for the US and Canada; Papapetrou [41] for Greece; Sadorsky, [42];[43] for the US; Basher and Sadorsky [44] for some emerging markets; and Park and Ratti [45] for the US and 13 European countries, reporting that oil price negatively affect stock prices. However, a few studies find little or no relationship between oil and stock prices [46-49]. The second group of studies that find no or weak evidence of the impacts of oil price shocks on economic activities include Hooker [50] and Segal [51].

To sum up, the literature shows that most of the studies are based on developed countries with few available outside G-7 countries. Although many studies document the impacts of CP on economic activities in developed countries, and, partially, in countries outside the USA and Western Europe, there is a dearth of studies available in the context Asia and Pacific countries, particularly in Malaysia. Malaysia is one of the net oil and gas exporter countries in the world. Its oil reserves are the fifth in the Asia Pacific region and one of the top 30 highest reserves in the world. Malaysia being an oil rich country might have different impacts of the oil price shocks. However, not much academic efforts were put forward to investigate impacts of oil prices on economic activities. With Malaysian data few studies have been conducted on oil price-macroeconomic relationship [52, 53]. Yusoff and Latif [53] employing ARDL bound testing approach explores that oil price dominantly affects energy demand and economic growth. Study conducted by Bekhet and Yusop [52] does not include recent oil price shocks of 2008 and onwards. The study includes demand side of energy along with employment and economic growth. However, inflation, interest rate, exchange rate and stock price indices are four important variables where the fluctuation of oil prices penetrates easily. Considering the above issues, it seems worthwhile to investigate the relationship within the framework of more economic activities such as inflation, interest rate, exchange rate and stock price indices including recent data of major oil shocks. Thus, the aim set out in the current study is to examine the relationships between oil prices and industrial production, consumer price index, real effective exchange rates, interest rates, and stock price index in Malaysia.

The dearth of studies in the context of Malaysia is one of the main inspirations for the completion of the current study, distinct from the existing studies in several aspects. First, data until 2013 is used which includes all the recent major oil shocks. Inclusion of these recent shocks will enhance understanding of the impacts of CP shocks on economic activities. Second, the study is implemented within the framework of a hidden cointegration and CECM model, which was rarely used in previous studies in general and not used in particular for Malaysia.

The rest of the paper is organized as follows. Section 2 introduces data and their sources; section 3 discusses the method used in the analyses of data, while section 4 reports and discusses empirical results; and section 5 draws relevant conclusions from the study.

2. Description of Data

This study utilizes quarterly CP data along with selected macroeconomic and financial variables including IP, CPI, IR, REER, and SPI for Malaysia over quarter 1, 1987 to Quarter 4, 2013 periods; making a total of 107 observations. The range of data has been chosen based on the availability of data for all required series. As a proxy for world CP, Dubai spot prices measured in US$ per barrel are used because the Dubai price is more relevant to Asian countries . The prime objective of the study is to examine the relationships between CP and IP, CPI, REER, IR and SPI. Data are collected mainly from the DataStream. The series are seasonally adjusted using US census-X12.

Real CP in domestic currencies is used. In order to transform nominal CP to real price nominal exchange rates and CPI are used. The real series are computed in the following way for CP:

RCP=CP*E/CPI Where RCP stands for real CP, used as CP throughout the text, at time t; CP represents nominal CP at time t, E stands for nominal exchange rate while CPI represents consumer price indices at time t.

In order to test the hidden cointegration, the series are decomposed into its positive and negative components and accumulate them at each time, t. The positive and negative components are defined as follows^:

T In order to calculate cumulative positive and negative components we first calculated the change in prices/variables (dp=pt -pt-1) and then took the positive components following (dp>0,dp or 0 otherwise) and the negative components following dp<0, dp or o otherwise). Having defined the positive and negative components, we cumulatively added them to get cumulative sum of positive/negative components.

IP+ IP-

: Cumulative sum of the positive components of industrial/manufacturing production index Cumulative sum of the negative components of industrial/manufacturing production index

CPI+ = Cumulative sum of the

CPI- = Cumulative sum of the

REER+ = Cumulative sum of the

REER- = Cumulative sum of the

IR+ = Cumulative sum of the

IR- = Cumulative sum of the

SPI+ = Cumulative sum of the

SPI- = Cumulative sum of the

CP+ = Cumulative sum of the

CP- = Cumulative sum of the

3. Methods and modelling framework

The central objective of this study is to identify the short-run and long-run relationship between crude oil price and macroeconomic variables. With that in mind, both the standard cointegration approach of Engle and Granger [55] and the hidden cointegration approach or Crouching Error Correction model (CECM) provided by Granger and Yoon [56] are used. Both of these methods are used in the same study for more accuracy. The later approach is considered superior to the standard approach because it can capture cointegrating relationships even in a nonlinear data generating process. And even if data series have no cointegration in conventional sense it might be possible to have hidden cointegration in them [56]. Therefore, the aim is to apply both methods to compare the findings as well as using the more sophisticated approach which will enable to identify more accurately whether any relationship exists or not.

In the standard procedure the following type of equations for examining the long-run relationship between crude oil prices and macroeconomic variables are estimated:

lllYt = «(, + 0E1hlCPt + E,. (1)

Where Yt represents the macroeconomic variables, CPt stands for real crude oil prices and st is the error term. a0 is the measure for constant and ai measures the long-run pass-through from crude oil price to macroeconomic variables.

The following Engle-Granger error correction model is estimated to examine the short-term dynamics of macroeconomic variables in response to the changes in crude oil prices:

AlnYt = ^AlnC^ + (3zEt_i + (2)

Where A is the first difference operator; ^ measures the short-term pass-through rate from crude oil prices to diesel or petrol prices; p2 is the measure for error correction adjustment speed in the case of disequilibrium which is expected to be negative in the

case of mean reversion and £t _1 represents the extent of disequilibrium at t-1 period which is the residual obtained from equation

In the CECM procedure, the following type of long-run equations without or with trend for each pair of positive and negative components of macroeconomic variables and crude oil price series are estimated:

Yt+ = + aiCPt+ + e, (3)

Yt+ = Kg + t^CP* + CE2T + Et (4)

Where Yt+ represents cumulative sum of positive components of any macroeconomic variables, CPt+ denotes cumulative sum of positive components of CP series. a0 is the measure for constant and ai measures the long-run pass-through of shocks from CP to macroeconomic variables, T measures trend. Similarly, models for all other components defined above are estimated to check conintegration. Residual series obtained from equation type (3) or (4) are examined by Augmented Dickey-Fuller (ADF) test for any unit roots. If they are found cointegrated, for example, Yt+ and CPt+ are cointegrated, then we estimate the following type of CECM model (5):

AYt+ = Yo + YiVi + J Yyt ACPt+s + Vy^AY+j + ^

L —1 k

Acpt+ = Vo + ^Vi+2_f<pyi Acpt-i+2, +^

L=1 j=l

Where J\ and are the long run speed of adjustment parameters to hidden equilibrium, parameters associated with other

lagged variables measure short-term adjustment. £t_i is the residual series obtained from equation (3) or (4).

4. Results and discussion

First of all, we check the stationary properties of all variables and their components by using ADF and Phillips-Perron (PP) unit root tests. The results of ADF and PP tests, as shown in Table 1, presents that all logarithmic transformed series and positive and negative components carry unit roots in levels whereas, they all are stationary at their first differences, implying series are integrated at order 1, I(1). This creates the premises for testing cointegration between series.

Table 1 Results of unit root tests

Variables

ADF test Level First difference

PP test

First difference

CPI 0.646 -8.100a 0.690 -8.234a

CPI+ 0.723 -7.528a 1.002b -7.528a

CPI- -0.211 -7.046a -0.301 -5.943 a

IP -1.109 -8.891a -1.109 -8.805a

IP+ 1.273 -5.832a 0.438 -9.330a

IP- 1.289 -8.783a 1.193 -8.783a

REER -2.603 -7.589a -2.467 -7.659a

REER+ -0.854 -9.977a -0.851 -9.977a

REER- -1.767 -6.646a -2.000 -6.738a

SPI -0.831 -8.015a -0.482 -8.026a

SPI+ 0.590 -9.612a 0.666 -9.632a

SPI- -0.791 -6.780a -0.677 -6.914a

IR -1.351 -6.393a -1.412 -6.124a

IR+ -1.376 -5.012a -1.294 -5.010a

IR- -0.798 -6.187a -0.925 -6.251a

CP -0.726 -11.105a -1.103 -15.556a

CP+ 2.566 -10.317a 2.766 -10.442a

CP- 1.021 -7.560a 1.050 -7.414a

Note: The values are of t statistics and a indicates 1 percent level of significance. ADF refers to Augmented Dickey Fuller; PP refers to Phillips-Perron.

Table 2 exhibits Engle-Granger cointegration results. Cointegration tests failed to identify any statistically significant longrun relationships between CP and macroeconomic variables excepting IR. IR is found to maintain a long-run relationship with CP. Since CP and IR are found to have linear relationship we estimate ECM for them. Table 3 presents ECM results for IR and CP estimated as per equation (2). Table 3 shows that the short-term pass through (P^ is not statistically significant and the error correcting speed parameter (p2) do not have appropriate sign indicating no short or long-run adjustment to equilibrium.

Table 2 Results of cointegration tests between oil price and macroeconomic variables Dependent variable Independent variable tau-statistics

CPI CP -2.334

IP CP -3.102

REER CP -2.594

IR CP -3.542b

SPI CP -2.694

Note: The values are of tau statistics and b indicates 5 per cent level of significance.

Table 3 Results of Engle Granger ECM

Pi 3? Adj. R2

IR -0.049(-0.480) 0.625(2.297) -0.013 Note: Values in parentheses are t-statistics and b indicates 5 per cent level of significance.

As the standard cointegration procedure failed to identify long-run relations between CP and macroeconomic variables, we proceed with the hidden cointegration approach. We first estimate Model (3) or (4) using the OLS method and then estimate cointegration models according to the Engle-Granger approach. The hidden cointegration is similar to the standard cointegration except that we estimate models based on the decomposed positive and negative components [57]. Table 4 displays results of the tau-statistics for the hidden cointegration tests. The results show that there are statistically significant relationships, at 10 percent level of significance at most cases, between negative components of CPI and positive components of CP, negative components of IP and positive components of CP and negative components of REER and positive components of CP.

Table 4 Hidden cointegration between oil price and macroeconomic variables Dependent variable CP+ CP-

Without trend tau-statistics With trend tau-statistics Without trend tau-statistics With trend tau-statistics

CCPI+ -1.291 -2.498 -1.543 -2.542

CCPI- -2.743 -3.541c -2.215 -2.896

CIP+ -1.405 -2.126 -1.839 -2.187

CIP- -3.204c -3.957b -2.286 -3.074

CREER+ --1.388 -2.446 -1.626 -2.228

CREER- -1.686 -3.573c -1.438 -3.053

CLSPI+ -1.392 -2.989 -1.883 -3.186

CLSPI- -0.552 -2.256 -0.753 -2.379

CIR+ -1.000 -2.431 -1.172 -2.528

CIR- -0.985 -2.357 -1.185 -2.671

Note: The values are of tau statistics and c indicate 1, 5, and 10 per cent level of significance respectively.

The results in Table 4 suggest that there is indication of possible long-run relationships between negative components of CPI and positive components of CP, negative components of IP and positive components of CP and negative components of REER and positive components of CP. To reveal exact long-run relationships between CP and these stated variables we estimate CECM based on equation (5). The estimated CECM results are shown in Table 5. Since negative components of CPI seem to be cointegrated with positive components of CP with trend in the equation, we estimate the CECM for this pair of variables. This result is shown under Sys.1 with trend in Table 5. The error correcting terms are statistically significant; however, the estimated signs are not appropriate indicating that neither positive components of CP nor negative components of CPI are responsible for the long-run relationships between these two variables. Next, we estimate CECM for negative components of IP and positive components of CP without and with trend. In this case also, although the error correcting terms are statistically significant none of them have right sign suggesting the non-responsiveness for the long-run relationships. Same goes with the negative components of REER and positive components of CP. For the first component, the error correcting term is statistically significant with wrong sign and for the last part the error correcting term has the right sign but not statistically significant. The above discussion reveals that the changes in CP and the changes in macroeconomic variables do not maintain a long-run relationship.

Table 5 Cointegration vectors and CECM results

Models Dependent variable Intercept if-1 ACPIf_1 1 iREERJ_1 Adj.R2

Sys.1 with trend ACPI'' 0.0660a (3.6291) 0.1034b (2.5391) -0.4418" (-6.661) -0.4825" (-6.1202) 0.424

ACPI* 0.1204a 0.2831a -0.0510 -0.2312b

(5.3495) (4.6868) (-0.562) (-2.0461)

AIP' 0.1884 0.2294a -2.5597" 0.0318

Sys.2 (1.1036) (4.0370) (-4.153) (0.3441) 0.292

ACP+ -.0966a -0.0021 0.0148 -0.0333b

(-3.744) (-0.240) (0.1538) (-2.319)

AIP' ACP+ -0.1117 0.1943" -2.6794" 0.1023

Sys.2 with trend (-0.6475) 0.1172" (3.5597) 0.0278" (-4.261) -0.0494 (1.1055) -0.0517" - 0.269

(4.5015) (3.3789) (-0.5204) (-4.131)

A REER' ACP+ -1.4941a 0.0911b 0.7601 0.3768"

Sys.3 with trend (-4.9799) 0.1306" (2.255) -0.00518 (0.6963) -0.0268 (3.8856) 0.044 0.0068

(4.8507) (-1.3292) (-0.2712) (0.7426)

Note: The values in parentheses are of t-statistics and respectively.

indicate 1, 5, and 10 per cent level of significance

5. Conclusion

The objective of this paper was to examine the relationship between CP and the economic activities of Malaysia including IP, CPI, REER, IR and SPI within the framework of hidden cointegration technique over the quarterly data ranging from 1987 to 2013. The estimated results suggest that positive and negative changes of IP, CPI, REER, IR and SPI do not maintain a long-run association with positive as well as negative changes of real crude oil prices. Although the negative changes in CPI, negative changes in IP and negative changes in REER are found to be cointegrated with the positive changes of crude oil price the estimated signs of the error correcting terms do not support this provision.

Acknowledgements

The author gratefully acknowledges the financial grant from International Islamic University Malaysia, Grant No. EDW A14-117-

1544. The author also gratefully acknowledges the valuable review comments from the anonymous reviewers.

References

[1] Pindyck RS. The dynamics of commodity spot and futures markets: a primer. Energy Journal 2001;22:1-29.

[2] Blein R, Longo R. Food price volatility-how to help smallholder farmers manage risk and uncertainty. Discussion Paper. Rome: IFAD; 2009.

[3] Hamilton JD. Oil and the macroeconomy since World war II. Journal of Political Economy 1983;91:228-48.

[4] Hamilton JD. This is what happened to the oil price-macroeconomy relationship. Journal of Monetary Economics 1996;38:215-20.

[5] Hamilton JD. What Is an oil shock? Journal of Econometrics 2003;113:363-98.

[6] Paul S, Bhattacharya RN. Causality between energy consumption and economic growth in India: a note on conflicting results. Energy Economics 2004;26:977-83.

[7] Sims CA. Macroeconomics and reality. Econometrica 1980;48:1-48.

[8] Mork KA. Oil and macroeconomy when prices go up and down: an extension of Hamilton's results. Journal of Political Economy 1989;97:740-

[9] Lee K, Ni S, Ratti RA. Oil shocks and the macroeconomy: the role of price variability. Energy Journal 1995; 16:39-56.

[10] Gisser M, Goodwin HT. Crude oil and macroeconomy: tests for some popular notions. Journal of Money, Credit and Banking 1986;18:95-103.

[11] Bjornland HC. Oil price shocks and stock market booms in an oil exporting country. Scottish Journal of Political Economy 2009;56:232-54.

[12] Cunado J, Gracia FP. Do oil price shocks matter? evidence for some European countries. Energy Economics 2003;25:137-54.

[13] Rotemberg JJ, Woodford M. Imperfect competition and the effects of energy prices increases on economic activity. Journal of Money, Credit and Banking 1996;1:549-77.

[14] Finn MG. Perfect competition and the effects of energy price increases on economic activity. Journal of Money, Credit and banking 2000;32:400-16.

[15] Keane MP, Prasad ES. The employment and wage effects of oil price chnages: a sectoral analysis. Review of Economics and Statistics 1996;78:389-400.

[16] Davis SJ, Haltiwanger J. Sectoral job creation and destruction responses to oil price changes. Journal of Monetary Economics 2001;48:465-512.

[17] Davis SJ, Loungani P, Mahidhara R. Regional labour fluctuations: Oil shocks, military spending and other driving forces. Board governors of the Federal Reserve System international finance discussion paper 1997;No. 578.

[18] Lee K, Ni S. On the dynamic effects of oil price shocks: a study using industry level data. Journal of Monetary Economics 2002;49:823-52.

[19] Lippi F, Nobili A. Oil and the macroeconomy: a quantitative structural analysis. Bank of Italy working paper 2009;No. 704.

[20] Francesco G. The economic effects of oil price shocks on the UK manufacturing and services sector. MPRA 2009;paper no. 16171.

[21] Burbidge J, Harrison A. Testing for the effects of oil-price rises using vector autoregressions. International Economic Review 1984;25:459-84.

[22] Blanchard OJ, Gali J. The macroeconomic effects of oil price shocks: why are the 2000s so different from the 1970s? : Department of Economics and Business, Universitat Pompeu Fabra, Economics Working Papers; 2007.

[23] Raymond JE, Rich RW. Oil and the macroeconomy: a Markov state-switching approach. Journal of Money, Credit, and Banking 1997;29:193-213.

[24] Bohi DR. On the macroeconomic effects of energy price shocks. Resources and Energy 1991;13:145-62.

[25] Lescaroux F, Mignon V. The symposium on 'China's impact on the global economy': measuring the effects of oil Prices on China's economy: a factor-augmented vector autoregressive approach. Pacific Economic Review 2009;14:410-25.

[26] Tang W, Wu L, Zhang Z. Oil price shocks and their short- and long-term effects on the Chinese economy. Energy Economics 2010;32:S3-14.

[27] Zhang Q, Reed M. Examining the impact of the world crude oil prices on China's Agricultural commodity prices: the case of corn, soybean and pork The Southern Agricultural Economics Association Annual Meetings, Dallas, TX, February 2-5, 2008 2008.

[28] Cologni A, Manera M. The Asymmetric Effects of Oil Shocks on Output Growth: A Markov-Switching Analysis for the G-7 Countries. Economic Modelling 2009;26:1-29.

[29] Huang B-N, Hwang MJ, Peng H-P. The asymmetry of the impact of oil price shocks on economic activities: an application of the multivariate threshold model. Energy Economics 2005;27:455-76.

[30] Akram QF. Oil Prices and exchange Rates: Norwegian evidence. Econometrics Journal 2004;7:476-504.

[31] Amano RA, van Norden S. Oil Prices and the rise and fall of the US real exchange rate. Journal of International Money and Finance 1998;17:299-316.

[32] Lizardo RA, Mollick AV. Oil price fluctuations and U.S. dollar exchange rates. Energy Economics 2010;32:399-408.

[33] Benassy-Quere A, Mignon V, Penot A. China and the relationship between the oil price and the dollar. CEPII research center, Working Papers; 2005.

[34] Cooper RL. Changes in exchange rates and oil prices for Saudi Arabia and other OPEC member countries. The journal of energy and development 1994;20:109-28.

[35] Brown SPA, Phillips KR. Exchange rates and World oil prices. Federal Reserve Bank of Dallas Economic Review 1986:1-10.

[36] Yousefi A, Wirjanto TS. The empirical role of the exchange rate on the crude-oil price formation. Energy Economics 2004;26:783-99.

[37] Zhang Y-J, Fan Y, Tsai H-T, Wei Y-M. Spillover effect of US dollar exchange rate on oil prices. Journal of Policy Modeling 2008;30:973-91.

[38] Aleisa EA, Dibooglu S. Sources of real exchange rate movements in Saudi Arabia. Journal of Economics and Finance 2002;26:101-10.

[39] Breitenfeller A, Cuaresma JC. Crude oil prices and USD/EUR exchange rate. Monetary Policy and the Economy 2008;Q4/08.

[40] Jones CM, Kaul G. Oil and stock markets. The Journal of Finance 1996;51:463-91.

[41] Papapetrou E. Oil price shocks, stock market, economic activity and employment in Greece. Energy Economics 2001;23:511-32.

[42] Sadorsky P. Oil price shocks and stock market activity. Energy Economics 1999;21:449-69.

[43] Sadorsky P. The macroeconomic determinants of technology stock price volatility. Review of Financial Economics 2003;12:191-205.

[44] Basher SA, Sadorsky P. Oil price risk and emerging stock markets. Global Finance Journal 2006; 17:224-51.

[45] Park J, Ratti RA. Oil price shocks and stock markets in the U.S. and 13 European countries. Energy Economics 2008;30:2587-608.

[46] Chen N-F, Roll R, Ross SA, Lo AW. Economic forces and the stock market. Static Asset-Pricing Models: Elgar Reference Collection. International Library of Financial Econometrics series, vol. 2. Cheltenham, U.K. and Northampton, Mass.: Elgar; 2007. p. 365-85.

[47] Huang RD, Masulis RW, Stoll HR. Energy shocks and financial markets. Journal of Futures Markets 1996;16:1 -27.

[48] Cong R-G, Wei Y-M, Jiao J-L, Fan Y. Relationships between oil price shocks and stock market: An empirical analysis from China. Energy Policy 2008;36:3544-53.

[49] Apergis N, Miller SM. Do structural oil shocks affect stock prices. Working paper, University of Nevada, Las Vegas, Department of Economics 2009;0917.

[50] Hooker MA. What happened to the oil price-macroeconomy relationship? Journal of Monetary Economics 1996;38:195-213.

[51] Segal P. Why do oil price shocks no longer shock? Oxford Institute of Energy Studies, Working Paper No 35 2007.

[52] Bekhet HA, Yusop NYM. Assessing the Relationship between Oil Prices, Energy Consumption and Macroeconomic Performance in Malaysia: Co-integration and Vector Error Correction Model (VECM) Approach. International Business Research 2009;2:p152.

[53] Yusoff NYBM, Latif NWBA. Measuring the Effects of World Oil Price Change on Economic Growth and Energy Demand in Malaysia: An ARDL Bound Testing Approach. International Journal of Trade, Economics & Finance 2013;4.

[54] Liu M-H, Margaritis D, Tourani-Rad A. Is there an asymmetry in the response of diesel and petrol prices to crude oil price changes? Evidence from New Zealand. Energy Economics 2010;32:926-32.

[55] Engle RF, Granger CWJ, Engle RF, Granger CWJ. Cointegration and Error Correction: Representation, Estimation, and Testing. Long-run economic relationships: Readings in cointegration: Advanced Texts in Econometrics Oxford; New York; Toronto and Melbourne: Oxford University Press; 1991. p. 81-111.

[56] Granger CW, Yoon G. Hidden cointgration. Department of Economics Working paper, University of California, Sandiego 2002.

[57] Honarvar A. Asymmetry in retail gasoline and crude oil price movements in the United States: An application of hidden cointegration technique. Energy Economics 2009;31:395-402.