Dr. Saif Siddiqui Assistant Professor Centre for Management Studies Jamia Millia Islamia – A Central University, Jamia Nagar, New Delhi |
Ms Arushi Gaur Research Scholar Centre for Management Studies Jamia Millia Islamia – A Central University, Jamia Nagar, New Delhi |
Oil industry recently faced the deepest downturn. This plunging price was first observed in June 2014 and it moved further down. This change affected the investment pattern of many companies and leads to a decline in corporate margins and influence investments in stock markets. Keeping in view this important relationship, here, we propose to study the movement of crude oil prices and volatility spill over to select major stock markets of Asia.
We have taken daily data for the period from June 01, 2014 to August 31, 2016 for the major Asian countries (China, Malaysia, Indonesia, South Korea, Singapore, Japan and India). One major stock index of each country has been taken to represent its stock market. We used GARCH (1,1) model to forecast volatility. Beside, doing descriptive statistics and correlation test, we put granger causality test to identify spill over of volatility.
Preliminary results suggested that apart from different degrees of correlations, return spill overs between India and its Asian counterparts are found to be significant and bi-directional. We found that there are some markets from where there is significant flow of volatility. Affect of historic crude price movement on stock markets is also significant.
Key Words: crude oil, stock market, GARCH model, causality, correlation
HISTORICAL FALL IN CRUDE OIL PRICES AND VOLATILITY SPILL OVER TO SELECT ASIAN STOCK MARKETS
Oil is the fuel that forces world economies. The sharp increase in the price of oil and other energy products were the most severe supply shocks hitting the world economies since World War II. An oil shock may have a different impact on each of the countries due to various factors such as their relative position as oil importers or exporters, different tax structures etc.
In context to Asian countries, changes in oil prices are one of the most important factors which impact the overall inflation of the countries. The major producers of oil are Saudi Arabia, United States, Russia, China, Canada, Iran, UAE, Iran whereas the major consumers are United States, China, Japan, India, South Korea, Germany, Italy, France, Netherland and Singapore. This mismatch between the producers and consumers drives international trade in oil. Due to the rising oil demand in countries like China and India, and production cuts by OPEC countries, the price of oil rose significantly from 1999 to mid 2008 from $25 to $150 a barrel. In July 2008, it reached its peak of US $147.27 a barrel.
The financial crises of 2007-2008 affected the oil price and underwent a significant decrease after July 11, 2008. On December 23,2008, it dropped below $30.28 per barrel which is lowest since financial crises. During the economic recovery, for about three and a half year the price remained from $90 to $120 a barrel. In mid of 2014, from a peak of $115 per barrel in June 2014 oil price started declining due to a significant increase in oil production in USA, and declining demand in other countries. By February 3, 2016 the price of oil was below $30 a barrel which is almost a drop 75% since mid-2014. This change affected the world economies to great extent. Many countries faced with the problem of unemployment. In USA 250,000 oil workers- roughly half of them lost their jobs. This change was also observed in stock market. The earnings are down for companies that made record profits in recent years whereas many companies have gone bankrupt. Thus it affected the investment in stock markets. This study is in the continuation of research based on the issue of fall in oil prices and its impact on stock market returns.
S.No |
Title of Paper |
Authors |
Year |
Indexes and time period* considered |
Data and Methodology Used |
Conclusions- Comments |
1 |
Oil Price Risk and the Australian Stock Market |
Faff and Brailsford, |
1999 |
24 Australian industry equity returns, 14 years |
Arbitrage Pricing Theory(APT), Capital Asset Pricing Model (CAPM) |
Findings were that the oil price factor effects the Australian industrial market |
2 |
Autoregressive conditional heteroscedasticity in commodity spot prices |
Beck |
2001 |
20 commodities, Consumer Price Index, Producer Price Index, Wholesale Price Index, 171 years |
GARCH |
Results concluded that ARCH term was significant on storable commodities. |
3 |
Modeling the conditional volatility of commodity index futures as a regime switching process. |
Fong and See |
2002 |
Future returns of Goldman Sachs Commodity Index(GSCI), 5 years |
GARCH(1,1) |
Regime shift in conditional mean and volatility |
4 |
Oil Price Shocks and Emerging Stock Markets: A Generalized VAR Approach |
Maghyereh, |
2004 |
Weighted stock market indices of Argentina, Brazil, Chile, China, Czech Republic, Egypt, Greece, India, Indonesia, Jordan, Korea, Malaysia, Mexico, Morocco, Hungary, Pakistan, Philippines, Poland, South Africa, Taiwan, Thailand, and Turkey, 6 years |
VAR model |
With VAR model, it was found that the stock market in these economies do not effect crude oil markets |
5 |
Oil Price Risk and Emerging Stock Markets |
Basher and Sadorsky, |
2006 |
Morgan Stanley Capital International (MSCI) World Index and Stock market returns of 21 countries, 11 years |
Capital Asset Pricing Model (CAPM) |
Evidences were found that shows the impact of oil price changes on stock price returns in emerging markets |
6 |
Oil Prices and the Stock Prices of Alternative Energy Companies |
Henriques and Sadorsky, |
2007 |
WilderHill Clean Energy Index (ECO), the Arca Technology Index (PSE), and oil prices , 7 years |
Vector Auto-regression (VAR) |
It was observed that the prices of stock and oil Granger cause the stock prices of alternative energy companies |
7 |
Commodity price cycles and heterogeneous speculators: A STAR–GARCH model. |
Reitz and Westerhoff |
2007 |
US-dollar market prices of commodities- cotton, lead, rice, soybeans, sugar, and zinc, 30 yrs |
STAR-GARCH |
The model indicates that their influence positively depends on the distance between the price of commodity and its long- run equilibrium |
8 |
Short-term Predictability of Crude Oil Markets: A Detrended Fluctuation Analysis Approach |
Ramirez, Alvarez and Rodriguez, |
2008 |
International crude oil prices, 20 years |
Auto-regressive Fractionally Integrated Moving Average (ARFIMA) |
In long run crude oil prices were efficient but in short run, inefficiency was found. |
9 |
Crude Oil and Stock Markets: Stability, Instability, and Bubbles |
Miller and Ratti, |
2008 |
Returns of S&P 500, oil prices., 37 years |
Vector Error Correction Model (VECM) |
There was Long run relationship between the stock prices of OECD countries and world oil prices |
10 |
Relationships between Oil Price Shocks and Stock Market: An Empirical Analysis from China |
Cong, Wei, Jiao and Fan, |
2008 |
Composite index of Shanghai stock market and Shenzhen stock market, 10 years |
Multivariate Vector Auto-regression |
It was observed that oil prices have not shown any effect on Chinese stock market |
11 |
The Impact of Oil Price Shocks on the U.S. Stock Market |
Kilian and Park, |
2009 |
US stock market return, 34 years |
VAR model |
The results proved that the US stock market return effects the oil price changes |
12 |
Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries |
Filis, Degiannakis and Floros, |
2009 |
S&P/TSX 60, MXICP 35, Bovespa Index, Dow Jones Industrial , DAX 30 and AEX General Index. 22 years |
GARCH model |
It was observed that Oil prices have significant impact on stock market prices, except 2008, year of global financial crisis, wherein oil prices showed positive correlation with stock markets |
13 |
The Effects of Crude Oil Shocks on Stock Market Shifts Behavior: A Regime Switching Approach |
Aloui and Jammazi, |
2009 |
Stock returns of Nikkei225, FTSE100 and CAC40, 19 years |
Markov-switching EGARCH model |
It was observed that rises in oil price had significant role in determining both ie in probability of transition across regimes and the volatility of stock returns. |
14 |
Exploring Autocorrelation in NSE and NASDAQ during the Recent Financial Crisis Period |
Siddiqui and Seth |
2011 |
NSE and NASDAQ, 4 years |
VAR Model |
It was found that there is no long term integration between oil prices and exchange rate prices |
15 |
Crude oil shocks and stock markets: A panel threshold co-integration approach |
Zhu, Li and Yu, |
2011 |
Norway, Sweden, Poland, Turkey, Brazil, India, Chile, China, Israel, Slovenia and South Africa, USA, UK, Mexico, 14 years |
Threshold co-integration, threshold VAR and Granger Causality model |
It was found that there was Co-integration, error correction and bidirectional causality between crude oil prices and stock returns |
16 |
Does crude oil move stock markets in Europe? A sector investigation |
Arouri, |
2011 |
DJ Stoxx 600 and European sector indices-Automobile & Parts,Financials,Food & Beverages,Oil & Gas,Health Care,Industrials,Basic Materials,Personal & Household Goods,Consumer Services,Technology,Telecommunications, andUtilities,12 years |
GARCH model and the quasi-maximum likelihood (QML) method |
The results concluded that the strength of relationship between oil and stock prices varies across different sectors |
17 |
Association between Crude Price and Stock Indices: Empirical Evidence from Bombay Stock Exchange |
Bhunia, |
2012 |
BSE 500, BSE 200, BSE 100, 10 years |
Johansen’s Co-integration test and VECM |
It was observed that the three indexes from BSE and crude oil prices are co-integrated but having only one way causality from all indexes to crude oil prices. |
18 |
Crude Oil Price Velocity and Stock Market Ripple: A Comparative Study Of BSE With NYSE and LSE |
Sharma and Khanna, |
2012 |
Sensex, DJIA and FTSE 100, spot prices of oil , 3 years |
correlation, regression and coefficient of determination |
It was found that the changes in oil price have significant effect on performance of stock returns. |
19 |
How does oil price volatility affect non-energy commodity markets? |
Ji and Fan |
(2012) |
US dollar index, crude oil prices, 2 yrs |
Bivariate EGARCH |
It was observed that significant volatility spillover effect was there of crude oil on non energy commodity market. |
20 |
Nonlinear Analysis among Crude Oil Prices, Stock Markets' Return and Macroeconomic Variables |
Naifar and Dohaiman, |
2013 |
OPEC Oil spot markets and Gulf Cooperation Council (GCC),S&P 500, 7 Years |
Markov Switching Models and Copula Models |
The relationship between Gulf Corporation Council stock market returns and OPEC oil market volatility was found to be regime dependent. It was also observed that inflation rate and short term interest rates were also dependent on crude oil prices |
21 |
On the links between stock and commodity markets' volatility. |
Creti, Joëts and Mignon |
(2013) |
Aggregate commodity price index, Commodity Research Bureau (CRB) index. Regarding the equity market, S&P 500 index. 25 commodities divided into sectors -energy, precious metals, non-ferrous metals, food, oleaginous, exotic , agriculture and livestock,10 yrs |
GARCH (DCC) |
There exist a correlation between commodity market and stock market. It was observed Stock Market as highly volatile since the financial crises of 2007-2008 |
22 |
The Impact of Oil Price Shocks on the Stock Market Return and Volatility Relationship |
Kang, Ratti and Yoon, |
2014 |
Weighted average of NYSE, AMEX, and Nasdaq stocks and oil prices, 14 years |
GARCH (1,1) model and structural VAR model |
Oil prices were found to be associated with the stock market volatility and returns |
23 |
Modelling dynamic dependence between crude oil prices and Asia-Pacific stock market returns. |
Zhu, Li and Li, |
2014 |
S&P/ASX 200, Shanghai composite,Hang Seng, BSE National, Jakarta SE composite,Nikkei 225, Kospi, Kuala Lumpur Composite, Strait Times, SE weighted, 12 years |
AR(p)-GARCH (1, 1)-t model |
It was concluded that there was a weak relation between crude oil prices and Asia-pacific stock markets |
24 |
Co-movement of International Crude Oil Price and Indian Stock Market: Evidences from Nonlinear Cointegration Tests |
Ghosh and Kanjilal, |
2014 |
SENSEX, exchange rate and international crude oil price , 8 years |
VAR model |
It was observed that the movement of international crude oil prices had an impact on stock prices |
25 |
Forecasting excess Stock Returns with Crude Oil Market Data |
Liu, Ma and Wang, |
2014 |
Return of S&P 500 and oil price, 37 years |
Time-varying Parameter (TVP) |
Apart from traditional predictors, oil prices effects the forecasting of stock market prices |
26 |
The Impact of Oil Prices on the Exchange Rate in South Africa. |
Kin and Courage |
(2014) |
Nominal exchange rate against the US dollar, Brent crude oil prices and South African interest rate, 10 yrs |
GARCH, EGARCH, and CGARCH |
The results concluded that there is a high persistence of volatility among the indices whereas Leverage Effect is there in Energy Spot, Agricultural Spot and Metal future. |
27 |
Forecasting Volatility in Commodity Market: Application of Select GARCH Models. |
Siddiqui and Siddiqui |
2015 |
Indian Metal, Energy and Agriculture index, 10 years |
GARCH, EGARCH, and CGARCH |
It was observed that there was a high persistence of volatility among the indices. Leverage Effect was there in Energy Spot, Agricultural Spot and Metal future |
Research Methodology is presented as under:
4.1 Objectives
Objectives are put as follows:
4.2 Data
We have taken daily data for the period from June 01, 2014 to August 31, 2016 for the major Asian countries (China, Malaysia, Indonesia, South Korea, Singapore, Japan and India). One major stock index of each country has been taken to represent its stock market i.e. for China(SSE COMPOSIE), Malaysia(FTSE ),Indonesia (JKSE), South Korea(KOSPI), Singapore(STI index), Japan(NIKKI 225) and India (S&P BSE). This data were taken from Yahoo Finance. We have also taken historical crude oil prices from Investing.com.
4.3 Tools
We used GARCH (1,1) model to forecast volatility and to develop residual series. Beside, doing descriptive statistics and correlation test, we put granger causality test to identify spill over of volatility.
4.4 Hypotheses
In order to meet the objectives following Null Hypotheses are proposed:
H01: There is no correlation among oil price and other indices
H02: There is no causality between price and other indices.
H03: There is no volatility persistence in oil price and other indices
5.Analysis
Analysis is presented as under:
Descriptive Statistics
With the help of descriptive statistics we are describing the various features of the oil price and other indices. Here, we have taken indices of China(SSE COMPOSIE), Malaysia(FTSE ),Indonesia(JKSE), South Korea(KOSPI), Singapore(STI index), Japan(NIKKI 225) and India (S&P BSE). It helps in summarizing a sample’s detail. Following table shows the result of descriptive statistics of the variables.
Table 01 About here
Descriptive Statistics means describing the data in quantitative terms. It summaries about the sample and the observation we have made. Here there are 4440 observations (555*8) of China, India, Indonesia, Japan, Malaysia, Singapore, South Korea and crude oil prices. FTSE is least volatile as compared to other indices as the standard deviation is least with .639 per cent and crude oil price is considered to be highest volatile as its standard deviation is 2.979 per cent. As Skewness measures the asymmetry of the probability distribution of variables. Here all variables are negatively skewed. Jarque- bera test is used to check the normality of the distribution. Hypothesis of normality is rejected here, in all the cases.
Correlation Test
In statistical terms, correlation measures how two variables move in relation with each other. Table 3 provides summary of the correlation among China(SSE COMPOSIE),Malaysia(FTSE ),Indonesia(JKSE), South Korea(KOSPI), Singapore(STI index), Japan(NIKKI 225) and India (S&P BSE).
Table 02 About here
Correlation is a statistical tool which measures the fluctuations between two or more variables. The value of correlation can be positive or negative. There is a positive correlation when an increase in one variable, increases the other variable. Here, values of correlation are ranging from -0.0921 to 1 which means they are negatively and positively correlated with each other.
GARCH Model
Past variances are considered to explain the future variances under this model. The result of GARCH model reflected by mean and variance equation are presented in Table 3
Table 03 About here
In the table 3 Alpha (α) indicates the ARCH affect and Beta (β) indicates the GARCH affect.
In all cases ie oil prices and other indices, the value of probability of GARCH coefficient (β) is 0.000, which is less than the critical value 0.05. Thus GARCH is significant for oil prices and other indices which mean that past deviation in values can affect the values in future.
Granger Causality Test
This test involves examining whether lagged values of one series have significant explanatory power for another series. They have null hypotheses of no granger causality. The results of this test are summarized in Table 4, and it indicates whether there exists significant Granger Causality and if it exists, then in which direction such causality exists between oil returns and stock returns
Table 04 About here
The results of tables 4 indicates that null hypothesis is rejected for oil and other indices as all indices and oil does not Granger Cause each other, that is even short-term causality does not exist between oil and index series.
This study is in the continuation of research based on the issue of fall in oil prices and its impact on stock market returns. For depicting the issue of interrelation and interdependency between the indices, we used Descriptive Statistics, Correlation Analysis. We used GARCH (1,1) model to forecast volatility and to develop residual series. We put granger causality test to identify spill over of volatility.
The key findings of the study are –
FTSE is least volatile as compared to other variables as the standard deviation is least with .639 per cent and crude oil price is considered to be highest volatile as its standard deviation is 2.979 per cent. As values of correlation are ranging from -0.0921 to 1 which means they are negatively and positively correlated with each other. GARCH is significant for oil prices and other indices which mean that past deviation in values can affect the values in future. The results of granger causality
This study is helpful to all individual/ institutional investors, portfolio managers, corporate executives, policy makers and practitioners may draw meaningful conclusions from the findings of this study while operating in stock markets. Our research may help stakeholders in management of their existing portfolios as their portfolio management strategies may be, up to some extent, dependent upon such research work.
References
Other Sources
TABLE 01
Descriptive Statistics
CHINA |
INDIA |
INDONESIA |
JAPAN |
MALAYSIA |
SINGAPORE |
SOUTH KOREA |
OIL |
|
Mean |
0.00077 |
0.00023 |
0.00020 |
0.00018 |
-0.00022 |
0.00023 |
2.31E-05 |
-0.00148 |
Std. Dev. |
0.02026 |
0.00958 |
0.00934 |
0.01498 |
0.00639 |
0.00958 |
0.00757 |
0.029798 |
Skewness |
-1.05922 |
-0.6612 |
-0.35327 |
-0.17323 |
-0.20914 |
-0.66116 |
-0.25359 |
-0.61007 |
Jarque-Bera |
452.325 |
286.597 |
165.970 |
411.093 |
68.0276 |
286.597 |
74.1507 |
2261.178 |
Probability |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
Observation |
555 |
555 |
555 |
555 |
555 |
555 |
555 |
555 |
TABLE 02
CORRELATION
CHINA |
INDIA |
INDONESIA |
JAPAN |
MALAYSIA |
OIL |
SINGAPORE |
SOUTH K0REA |
|
CHINA |
1.0000 |
0.0447 |
-0.0064 |
-0.0921 |
-0.0352 |
0.0334 |
0.0447 |
0.0501 |
INDIA |
0.0447 |
1.0000 |
0.0145 |
0.2094 |
0.0710 |
-0.0623 |
1.0000 |
0.1415 |
INDONESIA |
-0.0064 |
0.0145 |
1.0000 |
0.0857 |
0.0170 |
-0.0384 |
0.0145 |
-0.0492 |
JAPAN |
-0.0921 |
0.2094 |
0.0857 |
1.0000 |
0.0544 |
0.0925 |
0.2094 |
0.0274 |
MALAYSIA |
-0.0352 |
0.0710 |
0.0169 |
0.0544 |
1.0000 |
-0.0114 |
0.0710 |
0.2033 |
OIL |
0.0334 |
-0.0623 |
-0.0384 |
0.0925 |
-0.0114 |
1.0000 |
-0.0623 |
-0.0089 |
SINGAPORE |
0.0447 |
1.0000 |
0.0145 |
0.2094 |
0.0710 |
-0.0623 |
1.0000 |
0.1415 |
SOUTH KOREA |
0.0501 |
0.1415 |
-0.0492 |
0.0274 |
0.2033 |
-0.0090 |
0.1415 |
1.0000 |
TABLE 03
GARCH MODEL
CHINA |
INDIA |
JAPAN |
INDONESIA |
SINGAPORE |
MALAYSIA |
SOUTH KORIA |
OIL |
|
GARCH |
||||||||
C |
0.001237 (0.0276) |
0.000319 (0.4530) |
0.000656 (0.2391) |
0.000334 (0.3978) |
0.000319 (0.4530) |
-0.000158 (0.5047) |
8.84E-05 (0.7810) |
0.001410 (0.1299) |
Variance Equation |
||||||||
C |
1.44E-06 (0.0784) |
6.64E-06 (0.1439) |
7.34E-06 (0.0006) |
3.46E-06 (0.0049) |
6.64E-06 (0.1439) |
1.41E-06 (0.0037) |
3.33E-06 (0.0169) |
5.11E-06 0.0351 |
Α |
0.080304 (0.0000) |
0.039828 (0.1126) |
0.153157 (0.0000) |
0.070067 (0.0001) |
0.039828 (0.1126) |
0.118201 (0.0003) |
0.075258 (0.0052) |
0.095205 (0.0000) |
Β |
0.921710 (0.0000) |
0.887800 (0.0000) |
0.827264 (0.0000) |
0.889909 (0.0000) |
0.887800 (0.0000) |
0.849882 (0.0000) |
0.867303 (0.0000) |
0.909729 (0.0000) |
TABLE 4
Granger Causality Test
Basis |
Null Hypothesis |
Obs |
F-Statistic |
Prob. |
CHINA |
INDIA does not Granger Cause CHINA |
555 |
0.06688 |
0.7960 |
INDONESIA does not Granger Cause CHINA |
1.85951 |
0.1733 |
||
JAPAN does not Granger Cause CHINA |
6.92928 |
0.0087 |
||
MALAYSIA does not Granger Cause CHINA |
0.03612 |
0.8493 |
||
SINGAPORE does not Granger Cause CHINA |
0.06688 |
0.7960 |
||
SOUTH KOREA does not Granger Cause CHINA |
0.08496 |
0.7708 |
||
OIL does not Granger Cause CHINA |
1.52164 |
0.2179 |
||
INDIA |
CHINA does not Granger Cause INDIA |
0.00058 |
0.9809 |
|
INDONESIA does not Granger Cause INDIA |
4.30056 |
0.0386 |
||
JAPAN does not Granger Cause INDIA |
2.89037 |
0.0897 |
||
MALAYSIA does not Granger Cause INDIA |
21.4221 |
5.E-06 |
||
SINGAPORE does not Granger Cause INDIA |
na |
|||
SOUTHK does not Granger Cause INDIA |
4.11469 |
0.0430 |
||
OIL does not Granger Cause INDIA |
4.45484 |
0.0353 |
||
INDONESIA |
CHINA does not Granger Cause INDONESIA |
0.11557 |
0.7340 |
|
INDIA does not Granger Cause INDONESIA |
2.74654 |
0.0981 |
||
JAPAN does not Granger Cause INDONESIA |
1.88193 |
0.1707 |
||
MALAYSIA does not Granger Cause INDONESIA |
0.01860 |
0.8916 |
||
SINGAPORE does not Granger Cause INDONESIA |
2.74654 |
0.0981 |
||
SOUTHK does not Granger Cause INDONESIA |
1.03023 |
0.3106 |
||
OIL does not Granger Cause INDONESIA |
0.00161 |
0.9680 |
||
JAPAN |
CHINA does not Granger Cause JAPAN |
0.51011 |
0.4754 |
|
INDIA does not Granger Cause JAPAN |
11.9310 |
0.0006 |
||
INDONESIA does not Granger Cause JAPAN |
0.75902 |
0.3840 |
||
MALAYSIA does not Granger Cause JAPAN |
0.05684 |
0.8117 |
||
SINGAPORE does not Granger Cause JAPAN |
11.9310 |
0.0006 |
||
SOUTHK does not Granger Cause JAPAN |
5.49902 |
0.0194 |
||
OIL does not Granger Cause JAPAN |
4.33665 |
0.0378 |
||
MALAYSIA |
CHINA does not Granger Cause MALAYSIA |
1.07574 |
0.3001 |
|
INDIA does not Granger Cause MALAYSIA |
0.86460 |
0.3529 |
||
INDONESIA does not Granger Cause MALAYSIA |
1.99501 |
0.1584 |
||
JAPAN does not Granger Cause MALAYSIA |
0.27766 |
0.5985 |
||
SINGAPORE does not Granger Cause MALAYSIA |
0.86460 |
0.3529 |
||
SOUTHK does not Granger Cause MALAYSIA |
14.2092 |
0.0002 |
||
OIL does not Granger Cause MALAYSIA |
4.40239 |
0.0364 |
||
SINGAPORE |
CHINA does not Granger Cause SINGAPORE |
0.00058 |
0.9809 |
|
INDIA does not Granger Cause SINGAPORE |
Na |
|||
INDONESIA does not Granger Cause SINGAPORE |
4.30056 |
0.0386 |
||
JAPAN does not Granger Cause SINGAPORE |
2.89037 |
0.0897 |
||
MALAYSIA does not Granger Cause SINGAPORE |
21.4221 |
5.E-06 |
||
SOUTHK does not Granger Cause SINGAPORE |
4.11469 |
0.0430 |
||
OIL does not Granger Cause SINGAPORE |
4.45484 |
0.0353 |
||
SOUTH KOREA |
CHINA does not Granger Cause SOUTHK |
0.32018 |
0.5717 |
|
INDIA does not Granger Cause SOUTHK |
0.48508 |
0.4864 |
||
INDONESIA does not Granger Cause SOUTHK |
1.00569 |
0.3164 |
||
JAPAN does not Granger Cause SOUTHK |
4.79247 |
0.0290 |
||
MALAYSIA does not Granger Cause SOUTHK |
14.8580 |
0.0001 |
||
SINGAPORE does not Granger Cause SOUTHK |
0.48508 |
0.4864 |
||
OIL does not Granger Cause SOUTHK |
2.01632 |
0.1562 |
||
OIL |
CHINA does not Granger Cause OIL |
0.17780 |
0.6734 |
|
INDIA does not Granger Cause OIL |
1.04198 |
0.3078 |
||
INDONESIA does not Granger Cause OIL |
3.75555 |
0.0532 |
||
JAPAN does not Granger Cause OIL |
0.10664 |
0.7441 |
||
MALAYSIA does not Granger Cause OIL |
5.22161 |
0.0227 |
||
SINGAPORE does not Granger Cause OIL |
1.04198 |
0.3078 |
||
SOUTHK does not Granger Cause OIL |
0.51781 |
0.4721 |