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Editorial Board A Refereed Monthly International Journal of Management
Prof. B. P. Sharma
(Editor in Chief)
Prof. Mahima Birla
(Group Editor)
Dr. Khushbu Agarwal
(Editor)
Ms. Asha Galundia
(Circulation Manager)

 Editorial Team

Dr. Devendra Shrimali
Dr. Dharmesh Motwani
Mr. Jinendra Vyas
 

HISTORICAL FALL IN CRUDE OIL PRICES AND

VOLATILITY SPILL OVER TO SELECT

ASIAN STOCK MARKETS

           

Dr. Saif Siddiqui

Assistant Professor

Centre for Management Studies

Jamia Millia Islamia – A Central University,

Jamia Nagar, New Delhi

drsaifsiddiqui@gmail.com

and

Ms Arushi Gaur

Research Scholar

Centre for Management Studies

Jamia Millia Islamia – A Central University,

Jamia Nagar, New Delhi

Abstract

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

 

1. Introduction

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.

2. Conceptual Framework

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.

  1. Review of Literature

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 & PartsFinancialsFood & BeveragesOil & GasHealth CareIndustrialsBasic MaterialsPersonal & Household GoodsConsumer ServicesTechnologyTelecommunications, and Utilities,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


  1. Research Methodology

Research Methodology is presented as under:

4.1 Objectives

Objectives are put as follows:

  1. To ascertain the correlation among oil price and other indices
  2. To assess the direction of causality between oil price and other indices
  3. To forecast volatility oil price and other indices

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.  

 

  1. Conclusion

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

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  2. Arouri, M.E.H. (2011).Does Crude Oil Move Stock Markets in Europe? A Sector Investigation. Economic Modelling, 28(4),1716-1725.
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  4. Beck, S. (2001). Autoregressive conditional heteroscedasticity in commodity spot prices. Journal of Applied Econometrics, 16(2), 115-132
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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

 


 

 


 

 

 
 

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