examines how oil price fluctuations impact Indian economy through various channels, viz. real sector .... 9 Reserve Bank of India database: http://dbie.rbi.org.in ...
Impact of oil price fluctuations on Indian economy Abstract: Indian economy has been facing the twin issues of mounting trade imbalance and persisting inflation. Oil constitutes one-third of the country’s total imports and is considered to have wide ranging impact on its economy. This paper empirically examines how oil price fluctuations impact Indian economy through various channels, viz. real sector, monetary policy, external trade, exchange rate and investment. The results of cyclical correlation analysis suggest that oil is pro-cyclical to output, price level, stock market, gold, interest rate and foreign exchange reserves, while it is counter cyclical to money supply, net exports, and exchange rate. Also, it is found that oil Granger-causes output, general price level and net exports. The study employs VAR analysis and examines Variance Decomposition to capture the linear interdependencies among the variables. The structural stability tests demonstrate that there is no evidence of structural break in the VAR model, confirming the reliability of estimated relationships under the VAR model. 1. Introduction An OECD research paper1 released in the first quarter of 2013 predicted that oil prices could rise to between $150 and $270 a barrel by 2020, driven by projected demand growth in markets like India and China2. Oil has always been one of the most dynamic and influential commodities. Hamilton (1983) demonstrated that not all but one of the US recessions in the post-war period have been Granger-preceded by a dramatic oil price increase. Recently, it has become increasingly important to study the oil-macroeconomic dynamics in the context of developing nations, especially India, due to three-fold
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Fournier, J., et al. (2013). The Price of Oil – Will it Start Rising Again?. OECD Economics Department Working Papers, No. 1031, OECD Publishing. 2 Gross, J., 2013. OECD: Oil Prices Could Reach $150-$270 By 2020. [Online] Available at: http://online.wsj.com/news/articles/SB10001424127887324582804578344203184816238 [Accessed 02 October 2014]. 1
reasons: a) India is one of the countries that are being projected for fastest growth in fuel consumption corresponding to their growth in GDP 3; b) Given the drastic policy change in India with the deregulation of oil pricing, it is critical to understand the impact of oil price shocks on economic and investment activities in the country; and c) Oil constitutes more than one-third of the total imports value4 in India, which is struggling with high current account deficit (CAD). A Value-at-risk analysis was reported to suggest that with every $10 increase in oil prices, CAD would rise by 0.4 percentage points.5 This paper empirically examines how oil price fluctuations impact India’s economy through various channels: a) Impact on the real sector variables of growth and inflation; b) Monetary policy responses in the form of interest rates and money supply to gauge the role of monetary policy in the overall oil dynamics; c) Impact on the external sector variables, i.e. net exports, exchange rates and foreign exchange reserves; and d) Impact on the investment front – both financial, i.e. stock exchange and the popular alternative investment instrument in India, i.e. gold. The theoretical framework of the study is captured in Figure 1. The flow chart depicts how an oil price shock affects the overall economy through the various macroeconomic channels, monetary policy and investment variables. [Insert Figure 1 about here]
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Sieminski, A., 2013. International Energy Outlook 2013, s.l.: U.S. Energy Information Administration. 4 International Trade (2013). In The Economic Survey of India 2012-13 (pp. 149-172). Ministry of Finance, Government of India. 5 Asia Economics Analyst (April, 2011). Goldman Sachs Economic Research. 2
The remainder of this paper is organized as follows. Section 2 briefly reviews the existing literature; Section 3 lays down the data description and methodology; Section 4 presents the empirical results of the study and Section 5 puts forward the conclusion and documents policy implications. 2. Literature Review Ahmad (2013) documented that seven main channels of transmission between oil and economic activity have been identified in theory, namely, the classic supply-side effect, the demand side effect, the wealth transfer effect, the real balance effect, the inflation effect, the sector adjustment effect and the unexpected effect. Brown and Yucel (2002) maintain that the supply-side channel best explains the inverse relationship between oil and output and the positive relationship between oil and inflation. The linear negative relationship between oil price shocks and output which was established in literature in the aftermath of oil shocks of early 1970s, collapsed in the 1980s amidst a new regime of extremely volatile movement in oil prices. Several researchers argued that the economy reacts asymmetrically and nonlinearly to crude oil price shocks and thus, asymmetric and nonlinear transformations of oil prices such as asymmetric specification (Mork, 1989), scaled specification (Lee et al., 1995) and net specification (Hamilton, 1996) have gained wide acceptance. Barsky and Kilian (2002) and Gordon (1984) also show that positive oil price shocks are inflationary in nature. Ewing and Thompson (2007) investigated the oil-macroeconomic dynamics by examining the cyclical co-movements of crude oil prices with consumer prices, 3
unemployment, output and stock prices using dissimilar time series filtering methods. The results obtained in their study highlight significant cyclical relationships between oil prices and economic cycles in both the labour and financial markets. In their study on six Asian countries, Cunado and Perez de Gracia (2005) found a relatively more significant and more general oil prices-consumer prices relationship than the oil prices-economic activity relationship for those countries. There is a prominent school of thought that recognises that inflation is ultimately a monetary phenomenon and that the impact of oil price shocks on the real economy is attributable to the tightening monetary policy in response to adverse oil price shocks (Bernanke, et al., 1997). Leduc and Sill (2004) maintain that a monetary policy targeting an overall price stability substantially alleviates the impact of oil price shocks, while easy-inflation policies amplify the negative output response. Recently, in their study on G-7 countries, Cologni and Manera (2008) suggested that unexpected oil price shocks influences inflation which gets transmitted into higher interest rates. In addition to interest rates, monetary aggregates are considered to form important information variables in inflation dynamics (Kapur, 2012). Ou, et al. (2012) analysed the monetary policy variables in China’s context and showed that as inflation follows a WTI crude oil price shock, interest rates and interbank rates rise immediately, while the growth rate of Money supply (M1) begins to decrease after a lag of 3 months. On the external sector front, Ozlale and Pekkurnaz (2010) found that in the short term, an unexpected increase in oil prices causes the change in the current account ratio to fall in the Turkish economy even after controlling for output gap and exchange rate. Hassan and Zaman (2012) documented a negative and significant relation between oil 4
prices and trade balance both in short run and long run in the context of Pakistan’s economy. There is a broad consensus that higher oil prices affect financial markets and gold prices (Brown and Yucel, 2002; Jones, et al., 2004). Jones and Kaul (1996), Sardosky (1999) documented a negative impact of oil prices on stock returns. Recently, Lee, et al. (2012) showed that in the context of G-7 countries, the oil shocks exert significant influences on sector indices for some of the sample countries. Zhang and Wei (2010) show that there is a long-term equilibrium between oil and gold prices and the crude oil price change linearly Granger causes gold price volatility, but not vice versa. India being an oil-importing economy, an increase in international oil prices can lead to inflation and exchange rate shocks. The investors may use precious metals to hedge against inflation and currency risk. Consequently, long term dependence of gold prices on oil prices is also expected (Jain and Ghosh, 2013). Our study analyses the impact of oil price shocks on a major oil importing nation, India. We expand the existing research by including investment variables as part of oil-macroeconomy dynamics, apart from studying the oil price effect on trade balance and money supply. In addition, our study takes a more holistic approach by investigating cyclical co-movements of oil to complement the VAR framework analysis. 3. Data and Methodology 3.1.
Data and Data Sources
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This paper empirically investigates the interaction of oil prices with real sector variables of output growth (IIP) and inflation (WPI); monetary policy variablesinterest rates (INT_RATE) and money supply (MS); external sector variables-net exports (NX), exchange rate (EX) and foreign exchange reserves (FX) and behaviour of the investment variables-stocks (SI) and gold (GOLD). We make use of monthly data for India over the period April 1991 to January 2013. A brief description of data is in order. The data for OIL is derived from the website of EIA.6 As the Indian refiners prefer to purchase oil at Brent linked prices (Mishra, 2011), we have used BrentEurope monthly crude oil prices. Index of Industrial Production (IIP) is used to proxy for the aggregate economic activity since the GDP series is available only on quarterly frequency. IIP shows significant correlation with GDP (0.97) and it is found to be a reliable indicator of business cycle in India (Sethi, 2008). WPI is the measure of headline inflation in India and is closely watched by policy makers including the Reserve Bank of India. Moreover, the fuel group has a higher weightage in the WPI basket (about 14 per cent) in than the CPIs (about 9 per cent). Thus, movement in international crude prices has a greater bearing on WPI than on the CPI.7 The data for both IIP and WPI have been extracted from website of MOSPI.8 Further, BSE 100 index is used as proxy for overall stock market (SI). It represents over 70 per cent of market capitalisation on the Bombay Stock Exchange (BSE). The weighted average
EIA website: http://www.eia.gov/dnav/pet/pet_pri_spt_s1_m.htm Mohanty, D., 2010. Reserve Bank of India. [Online] Available at: http://www.rbi.org.in/scripts/BS_SpeechesView.aspx?Id=457 [Accessed 04 October 2014]. 8 Ministry of Statistics and Programme Implementation, Government of India: http://mospi.nic.in. In order to maintain continuity in the time series data on IIP and WPI, the financial year 2004-05 was fixed as base year and both the series are accordingly adjusted using linking factors as recommended by the office of the Economic Adviser of India. 6 6
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call money (WACM) rate has been used as the proxy for policy rates (INT_RATES) as WACM is reported to be highly correlated to policy rates (Mohanty, 2012). Data on WACM, M3 as a proxy for Money Supply (MS), Foreign Exchange Reserve (FX), net exports (NX) and Gold Prices (GOLD) were taken from the Reserve Bank of India (RBI) database.9 We used the monthly USD-INR exchange rate data as available on the website of Federal Reserve Bank of St. Louis. For the purposes of this study, all the variables except for interest rates (INT_RATE) and net exports (NX) were converted to logarithmic form. Thus, the variables LOIL, LIIP, LWPI, LMS, LEX, LFX, LSI and LGOLD are the oil price, output, price level, money supply, exchange rate, foreign exchange reserve, stock index level and gold prices respectively after logarithmic transformation. 3.2. Measures of Oil Price Shock For our linear model, following Hamilton (1983), we employ a linear benchmark (DOIL), which is computed as the percentage change in the log price of crude oil (OIL) which is defined as follows: 𝐷𝑂𝐼𝐿 = 𝐿𝑂𝐼𝐿𝑡 − 𝐿𝑂𝐼𝐿𝑡−1 where, LOIL is the log transformation of oil price data. In order to analyse asymmetric impact of oil price shocks, we employ three non-linear transformations :1) asymmetric specification, in which increases and decreases in the price of oil are considered as separate variables (Mork, 1989); 2) scaled specification
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Reserve Bank of India database: http://dbie.rbi.org.in 7
(Lee, et al., 1995), which takes the volatility of oil prices into account; and 3) net specification (Hamilton, 1996), where the relevant oil price variable is defined to be the net amount by which these prices in month t exceed the maximum value reached in the previous 12 months. Mork (1989) allowed for asymmetries in the price of oil and specified separate measures for positive and negative oil price shocks. Oil price change is defined as follows: 𝐷𝑂𝐼𝐿𝑡 𝑖𝑓 𝐷𝑂𝐼𝐿𝑡 > 0 𝑂𝐼𝐿+𝑡 = { } 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 𝐷𝑂𝐼𝐿𝑡 𝑖𝑓 𝐷𝑂𝐼𝐿𝑡 < 0 𝑂𝐼𝐿−𝑡 = { } 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 where DOIL is the change in price of oil at time t from period t-1, OIL+ is the oil price increase, and OIL- the oil price decrease. Lee, et al., (1995) used a GARCH model to calculate oil price volatility and arrived at an oil shock variable, which reflects both the unanticipated component of real oil price movement and the time varying conditional variance of oil price change forecasts. Lee, et al. (1995) used the following GARCH (1, 1) model to capture oil shocks: 12
𝐷𝑂𝐼𝐿𝑡 = 𝑐 + ∑ 𝛽𝑖 𝐷𝑂𝐼𝐿𝑡−𝑖 + 𝜀𝑡 𝑖=1
𝜀𝑡 = 𝑣𝑡 √ℎ𝑡 , 𝑣𝑡 ~ 𝑁(0,1) 2 ℎ𝑡 = 𝛾0 + 𝛾1 𝜀𝑡−1 + 𝛾2 ℎ𝑡−1
𝑆𝑂𝐼𝐿+𝑡 = max(0, 𝜀̂𝑡 / √ℎ𝑡 ) 𝑆𝑂𝐼𝐿−𝑡 = min(0, 𝜀̂𝑡 / √ℎ𝑡 )
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To model the asymmetric effects of oil shocks, we follow Lee, et al.(1995) in defining the oil volatility measure SOIL for positive (SOIL+) and negative (SOIL-) oil shocks, where SOIL+ contains all positive values of SOIL and zero replaces negative values and SOIL-contains all negative values of SOIL with positive values replaced by zero. Hamilton (1996) defined the net oil price increase (NOPI) as the percentage increase in the current price of oil over the price in the previous twelve months if it is positive, and zero otherwise. 𝑁𝑂𝑃𝐼𝑡 = max{0, (ln(𝑂𝐼𝐿𝑡 ) − ln(max( 𝑂𝐼𝐿𝑡−1 , … , 𝑂𝐼𝐿𝑡−12 )))} 3.3. Methodology In this paper, we conduct the analysis in a four-stage process: a. Cyclical Co-movement of oil prices with all variables under consideration. b. Granger Causality tests c. Vector Auto Regression (VAR) Analysis d. Stability Test The Cyclical Co-movement Analysis (CCA) involves estimating the dynamic correlations among the business cycle series to measure the degree of co-movement. The objective of such analysis is to assess how and to what extent the cycle of crude oil prices is leading, is synchronous, or is lagging the cycles of the macroeconomic variables under consideration. We employ two-sided linear Hodrick and Prescott (1980) filter (HP), the fixed-length symmetric band-pass Baxter and King (1999) filter (BK), and the asymmetric band-pass filter proposed by Christiano and Fitzgerald (2003) (ACF). 9
The business cycle is defined as consisting of j number of periods where, j = 0, ±1, ±2, ±3… For the purpose of this study, we have taken j = 12.10 Two types of inferences are drawn by co-cyclical movement analysis: a) Degree of cross-correlation: the absolute value of correlation coefficients suggest if the crude oil can be strongly or weakly contemporaneously correlated, or contemporaneously uncorrelated with respect to other variables of the cycle (Serletis and Shahmoradi , 2005). According to Serletis and Shahmoradi (2005), if 0.23 ≤ |p| < 1.0, it is believed to be strongly related, for 0.1 ≤ |p| < 0.23 it is weakly correlated, and uncorrelated if 0 ≤ |p| < 0.1. b) Lead/lag relation of oil price cycle with the business-cycle: If the cross-correlation coefficient |ρ(j)| is maximum for a positive, zero, or negative j, then the cycle of crude oil prices is leading the cycle by j periods, is synchronous, or is lagging the cycle by j periods, respectively (Ewing and Thompson, 2007). The correlation results obtained from the CCA are augmented using Granger causality test which is used examine the direction of causal relationship between oil and the variables under consideration. Following the examination of the order of integration of variables, unrestricted VAR in levels is estimated to analyse the impact of oil shocks on macroeconomic variables. VAR model has become one of the widely used methods to assess the effects of a particular variable on other variables of an economic system wherein, all the variables are considered as endogenous variables. In this study, we consider the following vector autoregression model of order p (VAR (p)):
Ahmad and Kamaiah (2010) suggest the average length of one phase of business cycle in India is 12-13 months. As the study focuses on India, we have also taken j=12 months as the average length of business cycle in India. 10
10
𝑝
𝑦𝑡 = 𝑐 + ∑ 𝜑𝑖 𝑦𝑡−1 + 𝜀𝑖 𝑖=1
where, 𝑦𝑡 = (𝑦1𝑡 , … . , 𝑦𝑛𝑡 ) is a (n x 1) vector of endogenous variables, 𝑦𝑡−𝑖 is the corresponding lag terms of order i. c = (𝑐1 , … . 𝑐𝑛 ) is the (n x 1) intercept vector of the VAR model, 𝜑𝑖 is the ith (n x n) matrix of autoregressive coefficients for i = 1,2,…,p, and 𝜀𝑖 = (𝜀1𝑡 , … . , 𝜀𝑛𝑡 ) is the (n x 1) generalization of a white noise process. This study uses a monthly VAR model including the following ten endogenous variables (n=10): OIL, IIP, WPI, SI, GOLD, NX, EX, FX, INT_RATE and MS. Thereafter, Variance Decomposition Analysis is undertaken to examine the relative importance of oil price shocks as the source of volatility for other variables. Finally, structural stability tests are used to establish the reliability of estimated relationship. 4. Results and their Discussion 4.1. Descriptive and Graphical Analysis Figure 2 shows the graphical plot of all the variables, i.e., oil prices (OIL), level of output (IIP), price level (WPI), policy rate (INT_RATE), money supply (MS), net exports (NX), exchange rate (EX), foreign exchange reserves (FX), stock index level (SI) and gold prices (GOLD). It can be seen that oil prices, stock prices and foreign exchange reserves tend to move in tandem, however, the exchange rate has mostly moved in opposite direction to oil prices. Net Exports series exhibit mirror image of oil prices which is expected as oil constitutes the single major component of total imports in India. [insert Figure 2 about here] 11
The descriptive statistics of all the raw variables is presented in Table 1. The average oil price during the sample period is $44.05 with a maximum of $132.72 reported in the mid of 2008. The coefficient of variation shows the highest variability for FX followed by MS and INT_RATE. Further, the skewness and kurtosis statistic suggest that variables are not normal, which is further supported by the Jarque-Bera test statistics as the null hypothesis of normality is strongly rejected for all the variables. However, VARs are generally quite robust to such non-normalities (Basher, et al., 2012). [insert Table 1 about here] 4.2.Unit Root Tests We check the stationarity using the Augmented Dickey–Fuller (ADF) unit root test and the Phillips-Perron (PP) test. The results of these tests are summarised in Table 2. Both the ADF and PP tests agree on the order of stationarity for all variables except for the output variable (LIIP) and exchange rate (EX) for which the results are less clear-cut. Irrespective of the orders of integration, it is desirable to use VAR in levels instead of VECM or VAR in first difference (Basher, et al., 2012). [insert Table 2 about here] 4.3.Cyclical Co-movement Analysis The results obtained from cross-correlation analysis are shown in Table 3. The use of different filtering methodologies to smoothen out the data allows for a better comprehension of the nature of relationship between crude oil price cycles and the economic cycles of selected variables. We computed the cross correlations for 𝑗 = ± 12 months period. However, we have reported only for 𝑗 = ± 6 period for the sake 12
of brevity and also because most of the maximal absolute values occurred within this range with the exception of interest rate and foreign exchange reserves, both of which suggest that oil leads interest rate and foreign exchange reserves by 12 months under BK and ACF methods. [insert Table 3 about here] In Table 3, panel A reports the cross correlation coefficients between the cyclical component of crude oil prices and the cyclical components of output, price level, stock index level, gold prices, net exports, interest rate, money supply, foreign exchange reserve and exchange rate for lead and lag up to 6 months. The results of HP method show positive correlation coefficient for oil prices and industrial output implying that oil is procyclical to output. Similarly, the results of BK method and ACF method in panel B and panel C respectively also establish the procyclicity of oil prices with output. BK and ACF methods show stronger correlation coefficients than HP method. This allows us to conclude that oil prices rise or fall with a lag of 2 months following an increase or decrease in output in India, thus suggesting that as the Indian economy is expanding, and India is becoming one of the leading consumer of oil, the output in Indian economy has a bearing on oil prices. It can be observed that the oil prices are also procyclical with the price level and the results suggest that oil prices lead the price level in economy by 2 months (HP) to 4 months (BK and ACF). Thus, as an oil price increase leads to increase in the costs of inputs, it takes 2-4 months for the shock to get fully absorbed in the general price level. Accordingly, the monetary policy makers can target the inflation rate following an oil price hike. The results show a rather strong positive correlation between oil price and stock market indicating their procyclical nature. The oil prices lag the stock market by two months as per HP method and three 13
months as per BK and ACF result. This relation between SI and oil price is similar to the relation between oil and industrial output. Industrial output is closely followed by stock markets (the rank correlation between stock index and IIP stands over 93%). Stock market may provide useful information regarding the reallocation of capital across different sectors which may signal future production needs such as oil (Ewing and Thompson, 2007). For gold and oil price, all the three methods suggest procyclicity of oil with gold oil leading by 0-1 month. This can be understood in terms of inflation hedging dynamics. Oil price increase is inflationary and gold is considered to be a store of value. Oil shows strong counter-cyclical relation with exchange rate, money supply and net exports. Oil prices lag exchange rate by 2-4 months. This can be understood through careful consideration of the oil-USD relationship. As oil is priced in terms of USD, an appreciation in USD (and resulting depreciation in the Indian Rupee) leads to a decline in oil prices. Further, the results strongly suggest that oil prices lead net exports by 23 months. As the oil price rise or fall, trade balance improves or deteriorates. This is expected given the high oil imports in Indian economy. An oil price hike leads to a change in monetary policy in terms of reduced credit availability and thus money supply is tightened by the monetary authorities, post 2-5 months in the wake of inflationary pressure. However, results suggest a rather delayed monetary policy response in terms of interest rate. While HP suggests that oil leads interest rate by 6 months, BK and ACF suggest a 12-month time gap. These results suggest that in the wake of inflationary pressure caused by an oil shock, the central back prefers to tighten the monetary policy by reducing the excess credit availability with the banks (and thus money supply) before choosing to increase the interest rates as it has more direct 14
implications for exchange rates and output. For foreign exchange reserves and oil, the results fail to indicate any clear relationship. 4.4.Short Run Relationships We perform Granger Causality test to investigate the significant short-run causal relationships between the variables. The results are summarised in Table 4. We focus on the causal relationships between oil and rest of the variables. A perusal of results suggest that for all measures of oil shocks, the null hypothesis that oil shocks do not Granger-cause the variables under consideration cannot be rejected for stock index, gold, exchange rate, foreign exchange reserves, money supply and interest rate. These results conform to the findings of other studies, which found that an oil price shock does not significantly impact macroeconomic variables (Hamilton, 1996; Lorde et al., 2009). However, all the measures of oil price shock significantly Granger-cause price level and net exports. Oil imports constitute the most significant part of Indian exports. Thus any oil shock has important impact on India’s trade balance. In addition, the impact on price-level can be explained by the fact that oil is an important input across all industries and transportation. Thus an increase in oil price leads to a hike in input costs which in turn results in higher price level. [insert Table 4 about here] An interesting result emerges regarding the asymmetric effect of oil shocks on output. For the asymmetric oil shock variables, OIL+ and SOIL+, we fail to reject the null hypothesis that oil shocks do not Granger-cause output. This implies that positive oil shocks impact output. However, no such evidence is found for negative oil shocks.
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Thus, while an increase in oil price may adversely impact the economic output, a decrease in oil price leaves the output level untouched. 4.5.Variance Decompositions We undertake Variance Decomposition analysis to understand the proportion of the movements in the dependent variables that are due to their ‘own’ shocks, versus shocks to other variables (Brooks, 2008). We are essentially interested in how much of the forecasted variance in different variables is explained by oil price shocks. Table 5 presents the results of variance decompositions. [insert Table 5 about here] The estimated decomposition results suggest that oil price shocks account for a small variations in output across all measures of oil shocks. The linear measure shows that oil price shock explains 0% variation in output which increases to 2.72% in the sixth months before declining to 2.52% in the twelfth month. It can be observed that for most of the variables, this measure of oil price shock accounts for a low variation of 0-2%. However, price level reports a variation of 12% in the sixth period on account of oil price shock. Significant variation can also be observed in case of net exports which persists even upto twelfth month. On the other hand, the oil price shock causes variation in foreign exchange reserves and money supply only after a lag of twelve months. The results of variance decompositions for other measures are similar to the linear measure of oil shock. A mention of impact of SOIL- on stock index and interest rate, however, is in order. SOIL- is a measure of negative oil price volatility and the results show 5% variation in stock prices on account of this volatility in the first period itself 16
which then declines subsequently. In case of interest rates also, out of all measures of oil price shock, only SOIL- causes a significant variation of 7% at the lag of twelve months. The contribution of oil in explaining the variation in money supply and interest rate increases with time, indicating that the impact of oil price shocks gets seeped into the monetary policy decisions after a considerable lag. Overall, out of all variables, general price level and net exports experience the maximum variation on account of oil price stocks. There is variation in the impact of positive oil price measure from that of negative measure with the negative measures accounting for more variation in price level and net exports more than the positive measures. 4.6.Structural Stability Test A model estimated over a long period of time gives rise to apprehensions about the presence of structural breaks making the model unstable and the results unreliable, more so while working in the context of a developing economy like India, which experienced a plethora of economic policy changes over the past two decades. The presence of structural breaks in the data is tested using the methods developed by Andrews and Ploberger (1994). We test the stability of the oil price coefficients in the output and inflation equation of the VAR model. Andrews and Ploberger (1994) introduced three test statistics: the Sup or MaxF statistic, AvgF statistic and ExpF statistic. Hansen (1997) provided approximate asymptotic p-values for these test statistics and our results report the same. The Maximum or Sup statistic is simply the maximum of the individual Chow Fstatistics: 17
𝑀𝑎𝑥𝐹 = max (𝐹(𝜏)) τ1 ≤ τ ≤τ2
The Exp statistic takes the form: τ2
1 1 𝐸𝑥𝑝𝐹 = ln( ∑ exp( F(τ))) k 2 τ= τ1
The Avg statistic is the simple average of the individual F-statistics: τ2
1 𝐴𝑣𝑔𝐹 = ∑ F(τ) k τ= τ1
The structure stability test is used to compare the estimation results of the different parts of the sample. First, sample is divided into two parts: the first part of the sample being T1 = [ПT] and the second part being T2 = (1-П)T with the break date t є[ПT,(1П)T], where П є (0,1) is some arbitrary fraction (following the standard practice, in this paper, we set П equal to 15%), then a series of F-statistics F(t) is calculated according to the variation of the break date t. If the F-statistics are greater than the critical value, then we reject the null hypothesis of structure stability, and date t is taken to be the break point. The results are presented in Table 6. When checked using Hansen (1997) approximate asymptotic p-values, all three of the summary statistic measures fail to reject the null hypothesis of no structural breaks within the possible dates tested, even at 10% level of significance. We can conclude that there is no evidence of instability in the coefficient of oil price changes in any of the four models, indicating that the models, and consequently the estimated relationships, are reliable. [insert Table 6 about here] 5. Conclusions
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As India is redeveloping11, its energy needs are growing at an exponential rate. This dependency on oil means that, theoretically, oil price fluctuations are bound to have far-reaching and intense impact on the Indian economy. Given these considerations, this paper is an attempt to empirically investigate how the oil price fluctuations impacts Indian economy through various channels. The main results may be summarised as follows. First, the cyclical co-movement analysis reports that oil is procyclical to output, price level, stock index, gold, and interest rate; and is counter-cyclical to exchange rate, net exports, and money supply. The results suggest that while India’s industrial output seems to have an effect on oil prices; oil is clearly inflationary for the economy which also explains the results that oil leads money supply and interest rate. Additionally, oil leads gold prices implying that as oil price shock leads to inflation, gold’s demand as an inflation-hedging tool rises. Oil understandably leads net exports, given the high level of oil imports by India. Second, the analysis of short term relationship reveals that oil prices Granger-cause price level and net exports. While positive measures of oil shock Granger-cause output, no such evidence is found for negative oil shocks, implying that while an increase in oil price may adversely impact the output, a decrease in oil price leaves it unaffected. Overall, our results suggest that the maximum impact of oil price fluctuations is felt on the price level and net exports. Given India’s high dependence on oil imports, India faces the impact of imported inflation, which is the general price level rise in a country
Frank, A. G. (1998). ReORIENT: Global Economy in the Asian Age. University of California Press, Berkeley, California. 11
19
because of rise in prices of imported commodities (Kumar, R., 2013). For an expanding economy like India, such vulnerability to oil price shocks is not sustainable and thus it becomes crucial to come up with efforts to expedite the process of exploring domestic avenues and diversify its sources of oil supply. Further, there is an urgent need for development of non-conventional (including renewable) sources as a substitute for conventional sources to meet the energy needs. Energy subsidy reforms along with regulations, standards, and targets directing the efficient level of utilization of oil as a fuel are important to reduce dependence on oil imports. This applies to developed and developing nations alike. References Ahmad, F. (2013) ‘The Effect of Oil Prices on Unemployment: Evidence from Pakistan’ Business and Economics Research Journal, Vol. 4 No.1, pp. 43-57. Ahmad, W. and Kamaiah, B. (2010) ‘Modeling Business Cycles in India: A Markov Switching Approach’, The Asian Economic Review, Vol. 52, No. 2. Andrews, W. D. and Ploberger, W. (1994) ‘Optimal tests when a nuisance parameter is present only under the alternative’ Econometrica, Vol. 62 No. 6, pp. 1383– 1414. Barsky, R. and Kilian, L. (2002) ‘Do We Really Know that Oil Caused the Great Stagflation? A Monetary Alternative. In: NBER Macroeconomics Annual 2001, 16. s.l.:National Bureau of Economic Research, Inc., 137-198. Basher, S. A., Haug, A. A. and Sadorsky, P. (2012), ‘Oil prices, exchange rates and emerging stock markets’ Energy Economics Vol. 34, pp. 227–240. Baxter, M. and King, R. (1999), ‘Measuring business cycles: approximate band-pass filters for economic time series’, Review of Economics and Statistics, Issue 81, pp. 575-593. Bernanke, B. S., Gertler, M. and Watson, M. (1997), ‘Systematic Monetary Policy and the Effects of Oil price Shocks’, Brookings Papers on Economic Activity, Vol. 1, pp. 91-142. Brooks, C. (2008) Introductory Econometrics for Finance, Cambridge University Press, Cambridge. Brown, S. P. A. and Yucel, M. K. (2002) ‘Energy prices and aggregate economic activity: an interpretative survey’, The Quarterly Review of Economics and Finance, Vol.42, No. 2, pp. 193-208. Christiano, L. and Fitzgerald, T. (2003) ‘The band pass filter’, International Economic Review, Vol. 44, pp. 435-465. Cologni, A. and Manera, M. (2008) ‘Oil prices, inflation and interest rates in a structural cointegrated VAR model for the G-7 countries’, Energy Economics, Vol. 30, pp. 856-888. 20
Cunado, J. and Perez de Gracia, F. (2005) ‘Oil prices, economic activity and inflation: evidence for some Asian countries’, The Quarterly Review of Economics and Finance, Vol. 45, pp. 65-83. Ewing, B. T. and Thompson, M. A. (2007) ‘Dynamic cyclical comovements of oil prices with industrial production, consumer prices, unemployment, and stock prices’, Energy Policy, Vol. 35, pp. 5535-5540. Gordon, R. J. (1984) ‘Supply shocks and monetary policy revisited’, American Economic Review, Vol. 74, pp. 38-43. Hamilton, J. (1983) ‘Oil and the Macroeconomy Since World War II’, Journal of Political Economy, Vol. 91, pp. 228-248. Hamilton, J. (1996) ‘This is what happened to the oil price-macroeconomy relationship’, Journal of Monetary Economics, Vol. 38, No. 2, pp. 215-220. Hansen, B. (1997) ‘Approximate asymptoticp-values for structural change tests’, Journal of Business and Economic Statistics, Vol. 15, pp. 60-67. Hassan, S. A. and Zaman, K. (2012) ‘Effect of oil prices on trade balance: New insights into the cointegration relationship from Pakistan’, Economic Modelling, Vol. 29, pp. 2125-2143. Hodrick, R. and Prescott, E. (1980) ‘Post-war US business cycles: an empirical investigation’, Working Paper, Carnegie Mellon University. Jain, A. and Ghosh, S. (2013) ‘Dynamics of global oil prices, exchange rate and precious metal prices in India’, Resources Policy, Vol. 38, pp. 88-93. Jones, C.M., Kaul, G. (1996) ‘Oil and the stock markets’, Journal of Finance, Vol. 51, pp. 463–491. Jones, D. W., Leiby, P. N. and Paik, I. K. (2004) ‘Oil Price Shocks and the Macroeconomy: What Has Been Learned Since 1996’, The Energy Journal, Vol. 2, pp. 1-32. Kapur, M. (2012) ‘Inflation Forecasting: Issues and Challenges in India’, RBI Working Paper Series, Issue 01/12. Kumar, R. (2013) De-jargoned: Imported inflation. http://www.livemint.com/Money/70gI0wXKhDUJys90sKqK3I/DejargonedImported-inflation.html (Accessed 29 October 2013) Leduc, Sylvain and Sill, Keith (2004) ‘A quantitative analysis of oil-price shocks, systematic monetary policy, and economic downturns’, Journal of Monetary Economics, Vol. 51, No.4, pp. 781-808. Lee, B.-J., Yang, C. W. and Huang, B.-N. (2012) ‘Oil price movements and stock markets revisited: A case of sector stock price indexes in the G-7 countries’, Energy Economics, Vol. 34, pp. 1284-1300. Lee, K., Ni, S. and Ratti, R. A. (1995) ‘Oil Shocks and the Macroeconomy: the Role of Price Variability’, Energy Journal, Vol. 16, pp. 39-56. Lorde, T., Jackman, M. and Thomas, C. (2009), ‘The macroeconomic effects of oil price fluctuations on a small open oil-producing country: The case of Trinidad and Tobago’, Energy Policy, Vol. 37, No. 7, pp. 2708-2716. Mishra, R. (2011) Why India prefers Brent to cheaper US crude oil WTI. http://www.thehindubusinessline.com/industry-and-economy/why-indiaprefers-brent-to-cheaper-us-crude-oil-wti/article2359323.ece. (Accessed 29 October 2013). Mohanty, D. (2012) ‘Evidence of Interest Rate Channel of Monetary Policy Transmission In India’, RBI Working Paper Series. 21
Mork, A. K. (1989) ‘Oil and the macroeconomy when prices go up and down: an extension of Hamilton’s results’, Journal of Political Economy, Vol. 97, No. 3, pp. 740-744. Ou, B., Zhang, X. & Wang, S. (2012) ‘How does China’s macro-economy response to the world crude oil price shock: A structural dynamic factor model approach’, Computers & Industrial Engineering, Vol. 63, pp. 634-640. Ozlale, U. and Pekkurnaz, D. (2010) ‘Oil prices and current account: A structural analysis for the Turkish economy’, Energy Policy, Vol. 38, pp. 4489–4496. Sardosky, P. (1999) ‘Oil price shocks and stock market activity’, Energy Economics, Vol. 21, pp. 449–469. Serletis, A. and Shahmoradi, A. (2005) ‘Business cycles and natural gas prices’, OPEC Review, Vol. 29, pp. 74-84. Sethi, N. (2008) ‘Economic Reforms, Capital Flows and Macroeconomic Impact of India’, in Rethinking India's Growth Strategy Services Vs. Manufacturing. Concept Publishing Company, New Delhi, pp. 560-580. Zhang, Y.-J. and Wei, Y.-M. (2010) ‘The crude oil market and the gold market: Evidence for cointegration, causality and price discovery’, Resources Policy, Vol. 35, No.3, pp. 168-177.
22
Table 1 Descriptive statistics of the variables OIL
IIP
WPI
INT_RATE
MS
NX
EX
FX
SI
GOLD
Mean
44.050
95.890
93.620
8.250
23,696.880
-3,819.990
41.340
115,555.400
2,269.490
8,786.010
Median
27.520
81.410
86.650
6.980
14,863.700
-1,007.500
43.640
52,441.000
1,205.320
5,195.580
Std. Dev.
33.190
42.660
33.390
5.060
21,974.050
5,138.650
7.460
110,713.600
1,833.180
7,093.230
CV
3.380
1.060
0.800
6.930
8.070
-0.250
0.370
23.940
5.040
1.980
Skewness
1.050
0.560
0.540
2.820
1.130
-1.480
-0.630
0.700
0.890
1.830
Kurtosis
2.830
1.980
2.400
13.310
3.100
4.100
2.640
1.850
2.190
5.340
Jarque-Bera
48.660
25.030
16.540
1,507.770
56.130
108.670
18.930
35.930
42.120
205.550
Probability
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
Obs.
262
262
262
262
262
262
262
262
262
262
Note: values in parentheses are p-values
23
Table 2 Unit Root Tests. Variables
LIIP
ADF
PP
Level
First Difference
Level
-2.397
-3.369 c
-8.216 a
-3.370
c
INT_RATE(i)
-4.752
a
LMS
-2.169
-3.321 c
0.1432
a
LWPI
NX LEX
-3.827
-11.580
-9.208
b
a
-3.381
c
-6.387
a
-3.640
b
-4.010
a
Decision I(0)/I(1)
-11.570
a
I(1) I(0)
-2.456
-16.346 a
I(1)
-35.600
a
I(1)
a
I(1)
-10.633
a a
-1.825
-13.378
-2.479
-11.720 a
I(1)
a
I(1)
LFX
-2.662
-12.756
LSI
-2.764
-11.874 a
-0.155
a
LGOLD
First Difference
-15.299
I(0)/I(1)
1.983
-14.983
-13.333
a
OIL (i)
-15.351
a
OIL-(i)
-11.755 a
-11.771 a
I(0)
-17.646
a
-17.586
a
I(0)
SOIL (i)
-14.265
a
-14.264
a
I(0)
NOPI(i)
-12.098 a
-12.546 a
I(0)
DOIL(i) +
+
SOIL (i) -
Note: a Significance at 1% level. b Significance at 5% level. c Significance at 10% level.
24
-13.333
a
I(0)
-15.407
a
I(0)
Table 3 Cyclical correlations of crude oil prices with other variables: p(LOIL,yt+j) j=-6
j=-5
j=-4
j=-3
j=-2
j=-1
j=0
j=1
j=2
j=3
j=4
j=5
j=6
(A) Hodrick Prescott Filter IIP
0.219
0.234
0.225
0.199
0.161
0.123
0.073
0.037
0.078
0.085
0.117
0.109
0.058
WPI
-0.291
-0.259
-0.177
-0.061
0.071
0.211
0.344
0.434
0.451
0.430
0.360
0.284
0.213
INT_RATE
0.013
0.020
0.025
0.008
0.018
0.056
0.076
0.075
0.080
0.077
0.049
0.069
0.089
MS
0.019
0.059
0.080
0.057
0.025
-0.002
-0.047
-0.118
-0.194
-0.238
-0.286
-0.289
-0.238
NX
0.209
0.202
0.159
0.072
-0.054
-0.160
-0.301
-0.378
-0.394
-0.328
-0.247
-0.145
-0.072
EX
-0.372
-0.415
-0.428
-0.423
-0.421
-0.423
-0.396
-0.341
-0.275
-0.205
-0.123
-0.035
0.048
FX
0.111
0.171
0.222
0.249
0.266
0.262
0.227
0.167
0.109
0.042
-0.020
-0.073
-0.119
SI
0.437
0.479
0.499
0.500
0.506
0.484
0.427
0.331
0.235
0.147
0.055
-0.033
-0.116
GOLD
0.070
0.096
0.131
0.144
0.132
0.132
0.132
0.098
0.058
0.043
0.044
0.050
0.063
(B) Baxter-King Filter IIP
0.425
0.476
0.516
0.544
0.557
0.555
0.539
0.506
0.461
0.405
0.340
0.271
0.200
WPI
-0.057
-0.016
0.037
0.099
0.166
0.234
0.296
0.350
0.391
0.416
0.422
0.410
0.379
INT_RATE
0.150
0.145
0.146
0.153
0.163
0.177
0.191
0.194
0.195
0.191
0.182
0.168
0.146
MS
-0.201
-0.218
-0.235
-0.254
-0.274
-0.294
-0.312
-0.325
-0.333
-0.333
-0.325
-0.308
-0.282
NX
0.045
-0.038
-0.129
-0.224
-0.316
-0.399
-0.465
-0.511
-0.532
-0.526
-0.493
-0.433
-0.352
EX
-0.478
-0.536
-0.577
-0.599
-0.601
-0.584
-0.547
-0.493
-0.425
-0.347
-0.263
-0.177
-0.093
FX
0.051
0.083
0.108
0.126
0.134
0.131
0.117
0.100
0.076
0.045
0.010
-0.028
-0.066
SI
0.611
0.658
0.687
0.695
0.681
0.646
0.591
0.525
0.446
0.357
0.262
0.165
0.071
GOLD
0.127
0.160
0.192
0.220
0.242
0.255
0.259
0.254
0.237
0.210
0.173
0.129
0.081
25
(C) Christiano and Fitzgerald Asymmetric Filter IIP
0.504
0.546
0.576
0.593
0.596
0.584
0.558
0.517
0.464
0.399
0.326
0.248
0.167
WPI
0.075
0.114
0.162
0.218
0.278
0.339
0.396
0.446
0.484
0.508
0.517
0.509
0.484
INT_RATE
0.236
0.243
0.252
0.262
0.272
0.281
0.288
0.292
0.292
0.288
0.279
0.264
0.244
MS
-0.051
-0.052
-0.057
-0.065
-0.073
-0.083
-0.090
-0.096
-0.098
-0.094
-0.084
-0.068
-0.045
NX
0.077
0.002
-0.083
-0.173
-0.263
-0.345
-0.415
-0.464
-0.491
-0.494
-0.474
-0.432
-0.372
EX
-0.397
-0.431
-0.451
-0.455
-0.443
-0.414
-0.370
-0.312
-0.242
-0.165
-0.082
0.002
0.084
FX
0.012
0.038
0.057
0.067
0.068
0.059
0.041
0.015
-0.019
-0.057
-0.099
-0.142
-0.182
SI
0.583
0.613
0.627
0.623
0.602
0.563
0.508
0.440
0.361
0.274
0.183
0.092
0.005
GOLD
0.139
0.172
0.206
0.240
0.270
0.295
0.312
0.320
0.318
0.307
0.286
0.257
0.223
26
Table 4 Granger causality tests. Null hypothesis: oil price shocks do not Granger-cause: Variables DOIL Output Price level Interest Rate Money Supply Net Exports Exchange Rate Foreign Exchange Reserves Stock Index Gold
b
Oil Shock measures OIL OILSOIL+ +
a
b
SOIL-
NOPI
3.646 (0.028) 10.081b (0.000) 0.0153 (0.985) 1.116 (0.329) 8.680b (0.000) 0.122 (0.885)
4.804 (0.009) 4.010b (0.019) 0.982 (0.376) 0.476 (0.622) 3.965b (0.020) 0.215 (0.806)
1.401 (0.248) 12.816a (0.000) 0.695 (0.500) 1.344 (0.263) 8.237a (0.000) 0.034 (0.966)
4.214 (0.016) 2.852c (0.058) 0.789 (0.456) 0.812 (0.445) 6.430a (0.002) 0.160 (0.853)
1.773 (0.172) 7.884a (0.000) 0.585 (0.558) 1.703 (0.184) 5.252a (0.006) 0.073 (0.930)
0.947 (0.390) 3.858b (0.022) 0.142 (0.868) 0.938 (0.393) 3.318b (0.038) 0.316 (0.729)
0.727 (0.485)
0.261 (0.770)
0.903 (0.407)
0.034 (0.966)
1.732 (0.180)
0.374 (0.688)
0.248 (0.781) 0.986 (0.375)
0.255 (0.775) 1.342 (0.263)
0.192 (0.825) 0.636 (0.531)
0.241 (0.786) 1.480 (0.230)
0.357 (0.700) 0.200 (0.819)
0.194 (0.824) 1.506 (0.224)
The values are chi-square (Wald) statistics and values in () are p-values a Significance at 1% level; b Significant at 5% level; c Significant at 10% level
27
Table 5 Variance Decomposition Analysis Dependent Variable Output
Price Level
Interest Rates
Money Supply
Net Exports
Exchange Rates
Forex Reserves
Stock Index
Gold
Period
OIL
ROIL+
ROIL -
OT+
OT-
NOPI
1 6 12
0.014 2.729 2.517
0.060 2.276 2.060
0.005 1.849 1.747
0.428 1.922 1.746
0.283 1.098 0.951
0.001 0.949 0.907
1
0.890
0.802
0.351
1.132
0.028
2.045
6
12.613
3.834
14.909
2.257
6.575
7.407
12
11.977
3.324
14.874
1.725
5.859
6.359
1
0.577
0.674
0.225
1.348
0.756
0.398
6
1.144
0.622
1.381
1.132
2.295
0.774
12
1.606
0.793
1.872
1.180
3.160
1.461
1
0.012
0.000
0.082
0.006
0.007
0.016
6
3.676
2.986
2.533
3.300
1.432
5.415
12
5.643
3.915
4.509
3.947
2.205
6.625
1
0.014
0.000
0.063
0.080
0.102
0.166
6
6.450
2.974
6.477
3.049
3.024
3.403
12
6.412
3.043
6.485
3.080
3.290
3.345
1
0.388
0.076
0.594
0.373
1.918
0.162
6
0.284
0.111
0.315
0.118
0.722
0.405
12
1.366
0.238
1.869
0.147
0.844
1.461
1
0.043
0.025
0.077
0.190
0.824
0.016
6
1.531
0.730
1.234
0.153
0.857
1.685
12
4.710
1.785
4.750
0.465
2.530
3.458
1
2.218
0.612
2.919
0.083
2.803
0.276
6
0.667
0.125
1.130
0.109
0.685
0.079
12
1.252
0.245
1.763
0.180
0.755
0.385
1
1.383
1.112
1.219
2.093
1.845
0.604
6
0.842
1.282
0.458
3.487
1.167
0.490
12
0.618
1.099
0.282
2.948
0.984
0.317
28
Table 6 Stability Tests. Output VAR Model Linear
DOIL
Price Level
MaxF
ExeF
AveF
MaxF
ExeF
AveF
2.909
1.063
2.106
2.567
0.906
1.774
+
2.957
1.016
2.005
2.873
1.017
2.016
-
OIL Mork(1989)
OIL
3.078
1.129
2.236
2.603
0.833
1.607
Hamilton (1996)
NOPI
2.204
0.848
1.688
2.808
0.876
1.719
SOIL+
1.857
0.731
1.456
2.929
0.964
1.900
-
2.077
0.808
1.607
2.056
0.643
1.259
Lee et al. (1995)
SOIL
29
` Figure 1 Impact of Oil Price Shock on Indian Economy
30
Figure 2 Variable Plots Apr. 1991 to Jan. 2013 OIL
IIP
160
WPI
200 160
120
INT_RATE
200
40
160
30
120
20
80
10
120 80 80 40
40
0
0 1995
2000
2005
2010
40 1995
2000
MS
2005
2010
0 1995
2000
NX
2010
5,000
60
80,000
0
50
-5,000
2000
2005
2010
2000
2005
2010
GOLD 30,000 25,000 20,000
4,000
15,000 10,000
2,000
5,000 0
0 1995
2000
2005
2010
100,000
10 1995
35,000
6,000
2010
300,000
20
SI 8,000
2005
FX
200,000
-25,000 1995
2010
30
-20,000
0
2005
40
-15,000
20,000
2000
400,000
-10,000 40,000
1995
EX
100,000
60,000
2005
1995
2000
2005
2010
31
0 1995
2000
2005
2010
1995
2000
