Dynamic Linkage between G old, Oil, Exchange Rate and ... · gold prices and USD exchange rate...

Dynamic Linkage between Gold, Oil, Exchange Rate and Stock

Market Returns: Evidence from India

1Dr. P. Mohanamani,

2Dr. Preethi.

3S, L.Latha,

1Assistant Professor, Department of Management, Kumaraguru College of Engineering,

Coimbatore, Tamil Nadu, India,

2Assistant Professor (Senior Grade), Department of Humanities, PSG College of Technology,

Coimbatore, Tamil Nadu ,India 3Professor, Department of Computer Science and Engineering,

Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India [email protected]

ABSTRACT

India as an emerging economy in the recent past had experienced a volatile situation in its

financial markets and ran into a massive current account deficit (CAD), in which the oil bill is

the most significant component. Foreign Exchange markets witnessed continuous weakening of

rupee against dollar, followed by falling crude oil prices, the rise in gold prices and high

volatility in Indian stock market. The complicated relationship between among the economic

variables has grasped the attention of researchers, policymakersand business people. This study

is an attempt to examine the interdependencies and to identify direct and indirect linkages

between oil, gold, exchange rate and the stock market in India. The study has taken daily data

from 2003:01 to 2017:12 constituting 3730 observations. By adopting the techniques of time

series, the study tried to capture the dynamic and stable relationship between these variables

using Cointegration test and Granger Causality techniques to establish our results.

Key Word: Unit root tests, Cointegration, Granger Causality.

1. INTRODUCTION

Everybody has some information. The function of the market is to aggregate that information,

evaluate and get it incorporated into prices – Merton Miller

International Journal of Pure and Applied MathematicsVolume 119 No. 17 2018, 2567-2580ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

2567

Stock market plays a vital role in the development of an economy. To judge the

performance of an economy, performance of stock market is used as an essential indicator.

India’s nominal GDP in 2016 at current prices was 2251 bn$, India contributes 2.99% of

world’s GDP in exchange rate basis. India shares 8.5% of total Asia’s nominal GDP, and India

shares 15.98% of the whole of Asia’s GDP regarding Purchasing Power Parity which positions

India’s nominal GDP as the seventh largest economy in the world[1]. as per the International

Monetary Fund’s World Economic Outlook

India is world’s third-largest crude oil consumer. Its oil imports hit a record of 4.3

MMbpd(million barrels per day) indicating a 1.8% increase in imports. An increase in domestic

demand led to the rise in refinery demand, which led toincrease in crude oil imports. At present

India’s crude oil refining capacity has grown to 5 MMbpd. According to the Energy Information

Administration, India’s oil consumption increased by 0.1 MMbpd to 4.9 MMbpd in December

2017,compared to the previous month. It also rose to 12% from a year ago. The EIA estimates

that India’s oil consumption could average 4.8 MMbpd in 2018. The IEA (International Energy

Agency) estimates that India’s crude oil demand growth rate will be the highest by 2040 when

compared to other oil importing countries. From the data provided it is evident that there is a

wide gap in demand and supply of oil. Every $1 per barrel rise in crude oil prices inflates India’s

import bill by $1.33 billion which puts downward pressure on the domestic currency (ET,

Nov07, 2017). Crude oil prices above $60 a barrel is a concern for India’s economy as it has a

potential bearing causing a spike in inflation, risingraw material cost and also influencing the

central bank’s interest rate policies and chances are there to alter the exchange rate dynamics

which derails India’s stock market.

India is the world’s second-largest consumer of gold. Gold jewelry accounts for about

50% of gold consumption. Investors consume gold in the form of gold bars and coins which

account for about quarter of the gold produced. On the other hand, Reserve Bank of India

acquires an additional of 10% of the demand to add their reserves account, andthe remaining is

consumed by industrial users. Demand for gold jewelry was 2043 tons during 2010 but declined

to 1988 tons by 2016 as per the data shared by World Gold Council. Demand by small jewelry

makers in Indiawas impacted by the introduction of Goods and Services Tax(GST) in July 2017

in turn, increased the compliance burden. The government had also brought gold purchases

under the Prevention of Money Laundering Act (PMLA) in August 2017, which too impacted

sales, especially in rural India.Central bank buying in open market to bolster their reserves has

seen an increase since 2011, which has been increasing the proportion of gold in its forex

reserves.[2] Gold occupies an essential role in the socio-economic life of both poor and rich in

India which makes India as one of the largest importers of gold which causes huge amount of US

dollar to flow out of the economy which can have an impact on the other areas of economic and

financial sectors of the economy.

India’s exchange rate is an uncertain block in rapid export growth. Two critical factors

that drive the export growth are the world growth and the real effective exchange rate(REER).

International Journal of Pure and Applied Mathematics Special Issue

2568

The higher the world growth, the higher export growth. Capital flows both direct and portfolio,

have a more significant role in determining the exchange rate than current account transactions.

Indiabeing the world’s largest recipient of remittances and being a robust exporter of services, a

large surplus in non- merchandise flows allows India to run up a massive deficit in trade in goods

which was 5% of GDP in 2016-17 when the current account deficit (CAD) was just 0.7% of

GDP. India has over $420 billion of forex reserves, but all these reserves are derived from

unabsorbed capital inflows and not from current account surpluses making India imperative to

maintain external confidence.

It is a common phenomenon that one market exercises its impact on the other market as

no market can work in isolation, but the variations in the magnitude of effectsand theco-

movement between the markets need to be examined. Gold, oil, exchange rate, stock market

returns as financial asset classes and their dependencies are of great need for two reasons. One is

that portfolio strategies are highly sensitive to the structure of market participants between

financial asset classes and second for policymakers to determine their impact on decisions based

on information asymmetry across financial asset classes and the effect of cross-market linkages

and influences. This article tries to study the causal linkages between gold, oil, exchange rates

and stock market prices in India. No doubt many researchers have examined similar question in

their prior work.

2. LITERATURE REVIEW

Economics plays a crucial role in explaining the links between gold prices, oil prices, exchange

rates and stock market prices. Evidence from the past studies shows that there is no long-run

relationship between exchange rate and oil prices in India [3]. Co-movements and linkages

among gold prices, oil prices, and Indian Rupee-Dollar exchange rates was investigated for the

time span of 12 January 2004 to 30 April 2015 and the results indicated that gold prices, oil

prices, and Rupee-Dollar exchange rates stay substantially independent from each other [4]. In a

study by Nair [5] attempted to understand the impact of the recession in 2008 on the relationship

between exchange rate between USD versus INR and gold prices in India. The study used

Johansen Cointegration test to check the long-term association between exchange rate of US

dollar in INR and gold prices in India, it further used the Granger Causality test to check the

lead-lag relationship between the variables. The study concluded that the relationship between

gold prices and USD exchange rate impacted the recession in India and that exchange value of

US Dollar is an essential factor in fluctuations in gold prices in India.In another study by Najaf

[6]showed that there is no long-run relationship between stock market of India and oil and gold

markets by using the data from 2003 to 2011. The presnece of dynamic linkages have been

analyzed using DCC-GARCH in standard, exponential and threshold variants and the lead-lag

linkages have been examined using symmetric and asymmetric non-linear Causality tests by

Jain[7]. Empirical analyses indicated that reduction in gold prices and crude oil prices also

causes fluctuations in Indian Rupee and the Sensex. Findings of this study also reveals the

emergence of gold as an attractive investment asset class among the investors. Also the study


2569

highlighted the need for dynamic policy-making in India to contain exchange rate fluctuations

and stock market volatility using gold price and oil price as instruments. Arfaoui [8] revealed oil

price had positively influenced the price of gold and USD. Oil price is also affected by oil futures

prices and by Chinese oil gross imports. Gold rate is impactedby changes in oil, USD and stock

markets. The USD is negatively affected by fluctuations in stock market and significantly by

fluctuations in oil prices and gold prices. The study also confirmed the Indirect effects always

exist which confirm the presence of global interdependencies and involve the financialization

process of commodity markets. Raheem [9] investigated the existence of relationship between oil

price, exchange rate and the stock market in Nigeria using vector autoregressive model (VAR).

The results revealed that oil price, exchange rate, and stock market index are not co-integrated.

The results of Granger Causality test show that there is bidirectional causality between exchange

rate and stock market. Also, there is bidirectional causality between the stock market and oil

price but unidirectional causality run from oil price to exchange rate. In another study by Chang

et al.,[10] examined the correlations of oil prices, gold prices and the exchange rate and found

that the oil price, gold price and exchange rate remain considerably independent from one

another, which implies policymakers should consider the separation of energy and financial

policies.Jin [11] compared the effects of oil price and real effect exchange rate on the real

economic activity in Russia, Japan, and China, respectively. The main findings indicate that the

oil price increases give a negative impact on economic growth in Japan and China and a positive

effect on economic growth of Russia.More precisely, a 10 percent permanent increase in

international oil prices is associated with a 5.16 percent growth in Russian GDP and a 1.07

percent decrease in Japanese GDP. On the one hand, an appreciation of the real exchange rate

leads to positive GDP growth in Russia and a negative GDP growth in Japan and China. Rahman

[12] explored the effects of changes in crude oil and gold prices on US Stock market movement.

ARDL Bounds Testing was applied for co-integration. The ARDL-Bounds testing confirmed co-

integration among the variables. There is evidence of long-run convergence among all these

variables with very tepid adjustment towards the equilibrium. The result is statistically

significant from gold price changes but insignificant from crude oil price changes.The same

finding is also supported by another study Beckmann et al.,[13] found substantial evidence that

oil prices and exchange rates are related over the long-run. On the other hand, Zhang [14]

however found that the co-integration between the oil price and the value of US dollar does not

significantly exist. Results reveal that rise in oil prices lead to a significant depreciation of the

USD against other currencies, such as Canada, Mexico, and Russia. On the other hand, Japanese

currency depreciates relatively to USD when oil prices rise up[15]. Volkov [16] investigated the

effects of oil price shocks on exchange rate movements in five major oil-exporting countries:

Russia, Brazil, Mexico, Canada, and Norway. The volatility of oil price shocks upon exchange

rates is significant in Russia, Brazil, and Mexico, but found to be weak in other countries such as

Norway and Canada. The asymmetric behavior of exchange rate volatility among nations seems

to be related to the efficiency of financial markets rather than to the importance of oil revenues in

the economy. Kim [17] in another study investigated the relationship between daily crude oil


2570

prices and exchange rates and the empirical results showed that the rise in the West Texas

Intermediate (WTI) oil price returns is linked with a depreciation of the US dollar. Degiannakis

et al., [18] reviewed on the complicated relationship between oil prices and stock market activity.

They found that most studies showed that oil price volatility is transmitted to stock market

volatility, indicating measures of stock market performance is improved based on the forecasts

of oil prices and oil price volatility.Olufisayo[19], studied the relationship between changes in oil

prices and stock market growth over the period 1981-2011 using vector error correction model.

The results suggest the existence of long-run relationship between oil price, exchange rate,and

stock market growth and also unidirectional causality runs from oil price change to stock market

development. In another study attempted by Huang [20] attempted to explore the effects of oil

price returns and oil price volatility on the Greek, the US, the UK and the German stock markets.

The results obtained revealed that the Greek stock market index returns and the US stock market

index returns are both sensitive to the oil price returns movements while the German and the UK

stock market returns are not affected at all.

Given with India’s global connectivity to other countries since post liberalization era

from 1991 onwards and Indian Government easing out norms for Foreign Portfolio Investors

huge amount of money is poured into the stock market which has increased the volatility in the

stock market.This study is an attempt to find out, is Indian stock market is integrated to the

global market in terms of gold market, oil market and exchange rate implications because of the

increased openness of Indian economy? If there are linkages, to identify the nature of linkages

whether it is long term or short term linkage between the markets?

3. METHODOLOGY

The primarypurpose of this article is to study the dynamic relationship between oil, gold,

exchange rate and stock market prices. Data used in the study is collected from daily spot prices

from world gold council, daily spot oil prices from OPEC, the daily exchange rate between INR

and USD from Reserve Bank of India and BSE Sensex returns are used as a proxy to measure the

Indian stock market prices. All the time series data employed in the study covers a period from

2003:2017.E-views 9.0 was used to analyze the data and the results are interpreted based on the

output. Studying stationarity properties of time series is the first step in the analysis. So as to

identify the variables are stationary or non-stationary. [21]In this study, Augmented Dickey-

Fuller test (ADF) (Dickey & Fuller, 1981) is used to test presence or absence of unit root among

the chosen variables for the study. There are three different models to conduct the ADF unit root

test:

Model I: Without intercept and trend

tqtpttttZZZZZ

....

22111

Model II: With intercept and without trend


2571

tqtpttttZZZZZ

..........

221110

Model III: With intercept and trend

tqtptttttZZZZZ

........

221110

Ho = =0 data to be differenced to become stationary, and Ha: < 0 meaning the data remains

stationary. 0

is intercept, t

is the trend and q denotes the lags determined by Akaike

Information Criterion or Schwarz Bayesian Information Criterion.

Johansen Cointegration Test

The purpose of using this test is to reveal the existence or absence of co-integration among the

chosen variables, provided all variables are non-stationary at level, meaning to say they are

integrated at same order. This method is also otherwise known as the maximum likelihood

method, which aids in testing the complete system of equations for the existence of

Cointegration among variables. This is also written as vector autoregressive equation of order p

as,

Xt = A0 + ∑ jXt-1 +εt

Xt denotes n x1 vector of non-stationarity variables integrated of I (1) order, p denotes the lag

length, A0 denotesnx1 vector of constants, ,Bj isnxn co-efficientmatrix and εt is white noise

error terms. Under Johansen approach there are two test statistics for Cointegration:

λmax (r, r+1) = - Tln(1-λr+1)

Ho: r= number of Cointegrating vectors, Ha: r+1 = number of co-integrating vectors, where λmax

conductsdistinct tests oneigenvalues for the above-stated hypothesis.

Two likelihood ratios, the trace and maximum Eigenvalue (Johansen, 1988) indicate the co-

integratingrank and are used to determine the number of co-integrating vectors.

λtrace = -T ∑ + (1-λj )

Number of co-integrating vectors are identified using the test results obtained from λtrace and

λmax test, T denotes number of observations and λj indicatesan estimated value for the jth

ordered

characteristic roots or the Eigenvalue. Eigenvalue greater than zero indicates the existence of co-

integrating vector. Trace statistics is used as a combined test to test the null hypothesis about the

presence of number of co-integrating vectors which is less than or equal to r, against the general

choice that the presence of co integrating vectors are additional to r. The maximum Eigen value

tests the presence of co-integrating vectors is equalto or less than r against the option of r+1 co

integrating vectors in the null hypothesis.

Granger Causality Test

Granger during 1969 developed this causality test. X is said to Grangercause another variable Y if

the past and present values of X help to predict the values of Y. To examine whether X Granger


2572

causes Y or Y granger causes X the following forms of bivariate regressions are runt to test the

Granger causality:

tntntnttnxxyyy

.................

11110

tntntnttnyyxxx

............

11110

4. Results and Discussion

Table 1: Unit Root Test Results

Augmented Dickey-Fuller

Test

Intercept P_Value Trend and Intercept P_Value

Level -0.310909 0.9210 -2.309272 0.4282

LGold -1.869791 0.3469 -1.163430 0.9165

LOilprices -1.754320 0.3143 -1.54376 0.4534

LBSE_Sensex -2.120158 0.2368 -2.485152 0.3356

First

Differences

LExchange Rate -44.53057 0.0000 -44.54484 0.0000

LGold -62.28248 0.0001 -62.31038 0.0000

LOilprices -61.71776 0.0001 -61.71032 0.0000

LBSE_Sensex -43.71316 0.0000 -4373305 0.0000

Augmented Dickey-Fullerunit root test was employed to determine whether variables become

stationary after taking the first difference. Table 1 presents the results of the Unit root test. The

estimations from the test result show that all the variables considered for study are stationary

after first order differentiation. [22]To use Johansen cointegration test all the variables chosen for

study should be integrated of order(I) and determining optimal lag length is another important

criterion.

Selection of Optimal Lag Length

Table 2: Lag order Selection

Lag LogL LR FPE AIC SC HQ Lag Length

selected for

the study

0 -113349.9 NA 1.76e+22 62.57460 62.58144 62.57703

1 -77643.37 71314.46 4.89e+13 42.87241 42.90661* 42.88460* 1

2 -77627.25 32.17080 4.89e+13 42.87234 42.93390 42.89427

3 -77610.67 33.03494 4.89e+13* 42.87202* 42.96094 42.90370 3

4 -77605.10 11.08562 4.92e+13 42.87778 42.99406 42.91921


2573

5 -77586.62 36.75648 4.91e+13 42.87641 43.02004 42.92758

6 -77569.92 33.17276 4.91e+13 42.87602 43.04702 42.93694

7 -77551.13 37.26704* 4.90e+13 42.87449 43.07284 42.94515 7

8 -77541.55 18.98147 4.92e+13 42.87803 43.10374 42.95844

Included observations: 3628 LR: Sequential modified LR test statistics (each at 5% level) FPE:

Final Prediction Error, AIC: Akaike Information Criterion, SC: Schwarz information criterion,

HQ: Hannan- Quinn information criterion.

Choosing appropriate lag length is essential before applying Johansen co-integration test

and Granger Causality Test. In this case we have used Multivariate Information criteria such as

LR: Sequential modified LR test statistics (each at 5% level) FPE: Final Prediction Error, AIC:

Akaike Information Criterion, SC: Schwarz information criterion, HQ: Hannan- Quinn

information criterion for all the included observations to determine the optimal lag length. Based

on the test results shown Table 3, optimal lag length chosen is 3.

Johansen Cointegration test

Table 3: Johansen’s Cointegration test results

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.

None * 0.165068 674.6564 47.85613 0.0001

At most 1 0.003156 20.50955 29.79707 0.3889

At most 2 0.002434 9.046003 15.49471 0.3611

At most 3 5.78E-05 0.209700 3.841466 0.6470

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.

None * 0.165068 654.1468 27.58434 0.0001

At most 1 0.003156 11.46355 21.13162 0.6010

At most 2 0.002434 8.836303 14.26460 0.3000

At most 3 5.78E-05 0.209700 3.841466 0.6470

Max-eigenvalue test indicates 1 Cointegration eqn(s) at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

The results of Johansen co-integration test results are presented in table 3 exhibits that the trace

statistic for calculated Max Eigenvalue (674.6564) is greater than the critical value of (47.85) at

5% critical value indicating the presence of co-integration between variables. Also, the max


2574

Eigen statistic value (654.14) is greater than its critical value (27.58) at 5% level of significance.

The Eigenvalue statistics reveals that there exists a strongCointegration between the chosen

variables such as BSE Sensex, crude oil, exchange rate and gold. Results of Johansen

Cointegration test denote that the null hypothesis (Ho) stating there is no Cointegration between

variables is rejected at 5% L level of significance. Test results in turn,leads to inference about the

existence of atleast three Cointegrating equations. After confirming the presence of co-

integrating vectors based on Johansen co-integration test results, the short run and longrun

interaction of the underlying variable are examined by fitting them within Vector Auto

Regression(VAR) and Vector ErrorCorrection model(VECM).

Table 3.a: Vector Error Correction Estimates

BSE_SENSEX(-1) EXCHANGE_RATE(-1) GOLD(-1) OIL_PRICES(-1) C

1.000000 80.18140 -61.29445 163.8353 -1.18E+08

(4093.44) (84.6844) (4.61697)

[ 0.01959] [-0.72380] [ 35.4855]

The test results show that a long-run equilibrium relationship exists between stock market

indices and the other variables taken for study namely crude oil, gold and exchange rate. The

estimated co-integrating Coefficients for the BSE Sensex based on the first normalizedEigen

vector, derived from the results presented in table 3.a are as follows,

BSE_ Sensex = 80.18 Exchange rate – 61.29 gold + 163.83 oil prices – 1.18.

The results reveal that coefficients are positive with the exchange rate and oil prices and

negative with gold prices. The results reveals the existence of fluctuation in exchange rate

because of fluctuations in crude oil prices. On the other hand, the fluctuations in gold prices

hasnegative impact on BSE Sensex. The reason attributed is that Indian investorshave the age-

old habit of investing in gold and Investment in financial assets is very much limited. The

negative coefficient value of gold price towards the BSE Sensex indicates that as the gold price

rises, Indian investors tend to invest less in stocks, causing stock prices to fall and vice versa.

Panel A: Normalised Co-integrating coefficients

BSE_SENSEX(-

1) EXCHANGE_RATE(-1)

GOLD(-1) OIL_PRICES(-

1)

C

1.000000 80.18140 -61.29445 163.8353 -1.18E+08

(4093.44) (84.6844) (4.61697)

[ 0.01959] [-0.72380] [ 35.4855]

Panel B

D(BSE_SENSEX) D(EXCHANGE_RATE) D(GOLD) D(OIL_PRICES)

3.81E-06 4.09E-10 1.47E-09 -0.006139

(3.5E-06) (4.0E-09) (2.1E-07) (0.00017)

[ 1.07952] [ 0.10250] [ 0.00707] [-35.4094]


2575

Standard errors in () and t-statistics in []

The coefficient of the Error Correction Term as given in the above table is (3.81E-06) is positive

andis statistically significantat 5% level. This reveals that BSE Sensex responds to establish

equilibrium with exchange rates whenever deviation occurs in exchange rates. Likewise, gold

prices tries to establish equilibrium whenever there is fluctuations in crude oil prices. The Vector

Error Model also indicates that exchange rate is another influential factor in Indian Economy.

India being import dominant country, appreciation of rupee against foreign currencies reduces

the import bill which causes higher cash flows leading to better profit and better stock prices.

Granger Causality Test

Table 4 Granger Causality Test Results, Lags:3

S. No Causality among variables F statistic P Value Existence of

Causality

1 LEXCHANGE_RATE Causes

LBSE_SENSEX

0.58248 0.5586 No

2 LBSE_SENSEX Causes

LEXCHANGE_RATE

3.16036 0.0154 Yes

3 LGOLDCauses LBSE_SENSEX 1.85323 0.1569 No

4 LBSE_SENSEX CausesLGOLD 3.96849 0.0398 Yes

5 LOIL_PRICES Causes LBSE_SENSEX 0.10949 0.8963 No

6 LBSE_SENSEX Causes LOIL_PRICES 0.25605 0.7741 No

7 LGOLD Causes LEXCHANGE_RATE 4.58301 0.0103 Yes

8 LEXCHANGE_RATE Causes LGOLD 1.83065 0.1605 No

9 LOIL_PRICES Causes

LEXCHANGE_RATE 3.79697 0.0165 Yes

10 LEXCHANGE_RATE Causes

LOIL_PRICES 0.58691 0.5561

No

11 LOIL_PRICES Causes LGOLD 0.33089 0.7183 No

12 LGOLD Causes LOIL_PRICES 0.06892 0.9334 No

Granger causality test is used to test whether the lags of one variable influence the lags of the

other variable. Granger causality test results reveal that changes in BSE Sensex influence

exchange rate as well the gold prices. On the other hand, exchange rate is also influenced by

changes in gold prices and oil prices. Wealth is transformedfrom oil importing countries to oil

exporting countries. India is the major importer of crude oil, fluctuations in exchange rate

between Indian Rupee and US Dollar will have an impact on account imbalances in oil price

settlement. Another point of observation is whenever oil price increases chances are there that

our trade balance worsens leading to depreciation of Indian Rupee.

5. CONCLUSION

The main purpose of this paper is to study the dynamic linkage between stock prices identified in

the form of BSE Sensex, Exchange rate between USD and Indian Rupee, Gold and Crude oil

prices for the period of 2003:01 to 2017:12. To assess the dynamic linkages, Johansen


2576

Cointegration test and Granger Causality Tests were used. The findings of Augmented Dickey-

Fuller Unit root test show that the variables get stationary in first-order differentiation and are

integrated oforder I(1). Johansen Cointegration test reveals the existence of Cointegration

between the variables chosen for study. After confirming for the existenceof Cointegration then

Vector Error Correction Model was tried to find out the existence of long-run relationship among

the variables.The results reveal that coefficients are positive with exchange rate and oil prices

and negative with gold prices. The results reveal that the increase in oil prices leads to increase in

exchange rate fluctuation and inturn has a long-term impact on the movements of stock markets.

Granger causality test results reveal that changes in BSE Sensex influence exchange rate as well

the gold prices. On the other hand, the exchange rate is highly influenced by changes in gold

prices and oil prices. The findings of the paper have important implications for policymakers and

academicians. India was categorized as one of the ―Fragile Five‖ economies when crude oil

prices were at their peak that lead to a current account deficit(CAD) of 4.8% of GDP during

2013-14. Crude oil price crash brought down the CAD to 0.7% of GDP during 2016-17. At

present CAD during 2018-19 isforecasted to be in the range of 2.5 to 2.9% of GDP was the

estimate. Over a period USD is strengthening on expectations of higher interest rates which has

made imports costlier for India. At the same time, India’s foreign Exchange reserves had touched

a lifetime high of USD 424.86 billion in the first weekend of April 2018 aided by an increase in

foreign currency assets. To this end, it implies that all the variables are independent from each

other but likely to exercise its influence on another variable. Finally, to gain control over

policymaking, it is better to detach policies for energy from that of policies for finance.

6. References

[1] Developments, R. (2018). World Economic Outlook, April 2018: Cyclical Upswing,

Structural Change; April 17, 2018; Chapter 1: Global Prospects and Policies, (April).

[2] Sujit, K. S., Kumar, B., & Rajesh Kumar, B. (2011). Study on dynamic relationship among

gold price, oil price, exchange rate and stock market returns. Journal of Applied Business and

Economic …, 9(2), 145–165.

[3]Ciner, C. (2001). Energy Shocks and Financial Markets: Nonlinear Linkages. Studies in

Nonlinear Dynamics and Econometrics, 5(3), 203–212.

https://doi.org/10.1162/10811820160080095

[4] Seyyedi, Seyyedsajjad (2017), ―Analysis of Interactive Linkages Between Gold Prices, Oil

Prices, and Exchange Rate in India‖,Global Economic Review, Vol. 46, pp. 65-79.

[5] Nair, Girish Karunakaran, Nidhi Choudhary and Harsh Purohit (2015), ―The Relationship

Between Gold Prices and Exchange Value of US Dollar in India‖, Emerging Markets Journal,

Vol. 5, No. 1, pp. 17-25.

[6] Najaf. R and Najaf. K (2016), ―Impact of Crude Oil Prices on the Bombay Stock Exchange‖,

Journal of Business & Financial Affairs, Vol. 5, No. 4, pp. 1-3.


2577

[7] Jain, Anshul and P.C. Biswal (2016), ―Dynamic Linkages among Oil Price, Gold Price,

Exchange Rate, and Stock Market in India‖, Resources Policy, Vol. 49, (September), pp. 179-

185.

[8] Arfaoui, Mongi and Aymen Ben Rejeb (2017), ―Oil, Gold, US Dollar and Stock Market

Interdependencies: A Global Analytical Insight‖, European Journal of Management and Business

Economics, Vol. 26, No. 3, pp. 278-293.

[9] Raheem, Aremu Idowu and Musa Adebiyi Ayodeji (2016), ―Analysis of the Relationship

between Oil Price, Exchange Rate and Stock Market in Nigeria‖, MPRA Paper No. 73549,

(September) accessed at https://mpra.ub.uni-muenchen.de/73549/

[10] Chang, Hsiao-Fen, Liang-Chou Huang and Ming-Chin Chin (2013), ―Interactive

Relationships Between Crude Oil Prices, Gold Prices, and the NT–US Dollar Exchange Rate—A

Taiwan Study‖, Energy Policy, Vol 63, pp. 441–448.

[11] Jin, Guo (2008), ―The Impact of Oil Price Shock and Exchange Rate Volatility on

Economic Growth: A Comparative Analysis for Russia Japan and China‖, Research Journal of

International Studies, Vol. 8.

[12] Rahman, Matiur and Muhammad Mustafa (2018), ―Effects of Crude Oil and Gold Prices on

US Stock Market: Evidence for USA from ARDL Bounds Testing‖, Finance and Market, Vol. 3,

No. 1, pp. 1-9.

[13] Beckmann, J. Berger, T., Czudaj, R. (2015). Does gold act as a hedge or a safe haven for

stocks? A smooth transition approach. Econ. Model., 48, 16–24.

[14] Zhang, Yi (2013), ―The Links Between the Price of Oil and the Value of US Dollar‖,

International Journal of Energy Economics and Policy, Vol. 3, No. 4, pp. 341-351.

[15] Novotný, F. (2012). The Link Between the Brent Crude Oil Price and the US Dollar

Exchange Rate. Prague Economic Papers, 21(2), 220–232.

https://doi.org/10.18267/j.pep.420

[16] Volkov, I. Nikanor and Ky-hyang Yuhn (2016), ―Oil Price Shocks and Exchange Rate

Movements‖, Global Finance Journal, Vol. 31, (Nov), pp. 18-30.

[17] Kim, Jong-Min and Hojin Jung (2018), ―Relationship Between Oil Price and Exchange Rate

by FDA and Copula‖, Applied Economics, Vol. 50, No. 22, pp. 2486-2499.

[18] Degiannakis, Stavros., George Filis and Vipin Arora (2017), ―Oil Prices and Stock

Markets‖, Working Paper Series accessed at

https://www.eia.gov/workingpapers/pdf/oil_prices_stockmarkets.pdf

[19] Olufisayo, Akinlo Olayinka (2014), ―Oil Price and Stock Market: Empirical Evidence from

Nigeria‖, European Journal of Sustainable Development, Vol. 3, No. 2, pp. 33-40.

[20] m.mathankumar1, t.viswanathan2 and t.dineshkumar3,‖( 2017) implementation of data

gathering system using mobile relay node in wireless sensor network‖, international journal of

pure and applied mathematics (ijpam), volume 116 no. 11, pp no. 111-119,

[21]Irfan Ahmed Mohammed Saleem, Dr. S. Jaisankar (2018), A Study On Kaizen Based Soft-

Computing In Electric Vehicle Manufacturing Processes, International Journal Of Innovations In

ScientificAnd EngineeringResearch, Vol5Issue5,.31-39.


2578

[22] Huang, R. D., Masulis, R. W. and Stoll, H. R. (1996). Energy shocks and financial markets,

Journal of Futures Markets, 16, 1-27.


2579

Dynamic Linkage between G old, Oil, Exchange Rate and ... · gold prices and USD exchange rate...

Documents

Transcript of Dynamic Linkage between G old, Oil, Exchange Rate and ... · gold prices and USD exchange rate...