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Referred Journal of CMR College of Engineering & Technology

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A Model Proposition for Prescreening Candidates in Recruitment Process Using Fuzzy Vikor Method

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Department of Master of Business Administration

An Integrated Marketing Communications, Media Synergies and its effect on the Consumer Decision Making Process Reshma Nikhat

Influence of Organizational Climate on Employee Turnover Intention in Information Technology Industry in Kerala Jnaneswar. K

Gayathri Ranjit

Impact of Transformational Leadership Style Dimensions on Organizational Performance: An Empirical Analysis Shruti Balhara

Harbhajan Bansal

Impact of Quality of Work Life on Organisational Commitment Indu Bala, Ramandeep Saini, B.B. Goyal

Mediating Role of Personal Accomplishment among Emotional Labour Strategies and Teaching Satisfaction among Professional College Teachers Jitha G. Nair

Testing of Long-Run Relationship between Gold Prices and Stock Market Return: An Empirical Analysis in India Tanvi Bhalala

Emerging Distribution Channel Effectiveness in Rural Jharkhand for Consumer Electronics

Punit Kumar Mishra Girish Kumar Srivastava

Changing Role of Learning and Development Methodologies Digital Age - A Comparison between Manufacturing and Service Industry S. Rajeswari, D.Raghunatha Reddy

M.Ramakrishna Reddy

Creativity and Innovation in B-Schools:Potential Areas for Development

K. Renuka Raju, Shakeel Ahmad A. Ramachandra Aryasri

Training Effectiveness on Job Performance - An Analytical Study with Reference to Dairy Industry

Menaka.Bammidi Puppala. Hyndhavi

Performance Appraisal Impact on Employee Job Satisfaction with Reference to TSSPDCL

M. Ramu Mohd. Akbar Ali Khan

Mobile Data Usage Behavior: A Study on Bottom of the Pyramid Market Leena Sharma

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ISSN: 2277-6753 (Print) ISSN: 2322-0449 (Online)

SUMEDHA-Journal of Management

Referred Journal of CMR College of Engineering & Technology

April-June2019, Volume 8, No. 2

S. No.

Title Authors Page No.

1. A Model Proposition for Prescreening Candidates in Recruitment Process Using Fuzzy Vikor Method

Murat Bolelli* 1-19

2. An Integrated Marketing Communications, Media Synergies and its effect on the Consumer Decision Making Process

Reshma Nikhat* 20-32

3. Influence of Organizational Climate on Employee Turnover Intention in Information Technology Industry in Kerala

Jnaneswar. K*, Gayathri Ranjit**

33-46

4. Impact of Transformational Leadership Style Dimensions on Organizational Performance: An Empirical Analysis

Shruti Balhara*, Harbhajan Bansal**

47-57

5. Impact of Quality of Work Life on Organisational Commitment

Indu Bala*, Ramandeep Saini**, B.B. Goyal***

58-72

6. Mediating Role of Personal Accomplishment among Emotional Labour Strategies and Teaching Satisfaction among Professional College Teachers

Jitha G. Nair* 73-82

7. Testing of Long-Run Relationship between Gold Prices and Stock Market Return: An Empirical Analysis in India

Tanvi Bhalala* 83-96

8. Emerging Distribution Channel Effectiveness in Rural Jharkhand for Consumer Electronics

Punit Kumar Mishra*, Girish Kumar Srivastava**

97-112

9. Changing Role of Learning and Development Methodologies in Digital Age - A Comparison between Manufacturing and Service Industry

S. Rajeshwari*, D.Raghunatha Reddy**, M.Ramakrishna Reddy***

113-126

10. Creativity and Innovation in B-Schools: Potential Areas for Development

K. Renuka Raju*, Shakeel Ahmad**, A. Ramachandra Aryasri***

127-133

11. Training Effectiveness on Job Performance - An Analytical Study with Reference to Dairy Industry

Menaka.Bammidi*, Puppala. Hyndhavi **

134-147

12. Performance Appraisal Impact on Employee Job Satisfaction With Reference to TSSPDCL

M. Ramu*, Mohd. Akbar Ali Khan**

148-156

13. Mobile Data Usage Behavior:

A Study on Bottom of the Pyramid Market

Leena Sharma* 157-169

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(5)

Testing of Long-Run Relationship between Gold Prices and Stock Market Return: An Empirical Analysis in India Tanvi Bhalala* SUMEDHA-Journal of Management

Referred Journal of CMR College of Engineering & Technology April-June 2019, Volume 8, No. 2, pp 83-96

ISSN: 2277-6753 (Print) ISSN: 2322-0449 (Online) http://cmrcetmba.in/sumedha/

Testing of Long-Run Relationship between Gold Prices and

Stock Market Return: An Empirical Analysis in India

Tanvi Bhalala*

Assistant Professor, Prof. V.B. Shah Institute of Management, Amroli, Surat, Gujarat.

Abstract

This research is carried out to investigate the relationship between gold prices and stock market return in India. This investigation is conducted to know long run integration among two investment alternatives i.e. gold and stock market. In order to evaluate the relationship, unit root test, break point unit root test and Autoregressive Distributed Lag approach of Cointegrationare implemented. The result indicated presence of unit root in Gold price time series and indicates stationarity for stock market return. The significant break point at November 2008 is identified in gold price, while none of the significant break point is identified for Sensex return. The existence of long-run relationship is found between stock market return and Gold prices in India. Thus, the movement in gold price leads to fluctuation in stock market return in India.

Key Words: Gold, Sensex, Structural Break, Unit Root, Cointegration.

JEL Classification: G1, G11, C220.

PUBLISHING CHRONOLOGY PAPER SUBMISSION DATE : JANUARY 2, 2019;

PAPERSENTBACKFOR REVISION : FEBRUARY 14, 2019;

PAPER ACCEPTANCE DATE : MARCH 2, 2019

Reference to this paper should be made as follows:

Tanvi Bhalala (2019), "Testing of Long-Run Relationship between Gold Prices and Stock Market Return: An Empirical Analysis in India" SUMEDHA Journal of

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Testing of Long-Run Relationship between Gold Prices and Stock Market Return: An Empirical Analysis in India Tanvi Bhalala*

84

-I

NTRODUCTION

A sound and effective financial system is considered as crucial element for economic growth and development. On the other hand, there is a need of well-developed and efficient financial market to boost up growth of economy. In current scenario, there are wide range of macroeconomic and financial variables that have a significant relationship with the financial market. Gold has traditionally been considered an attractive investment in India and its excellent performance in recent years. During 2018, the price of gold increased by 6 percent while the equities realized positive movements.

There are several reasons gold has high demand in India. The first reason is security; gold offers full security as long as it is retained by central banks. There is no credit risk attached to gold. Secondly, gold is able to maintain its liquidity even at times of crisis situations like high worldwide inflation or political turbulence. The third reason for holding gold is to build a diversified portfolio. Gold also has taken the role of an asset of last resort.

Gold has been utilized by people since the earliest times for making statues and icons and also for jewelry to adorn their bodies. Indian has always loved gold not just because of the adornment value, but because it is seen as an instrument of long-term investment and portfolio diversification for investors. Investors normally include gold in their portfolio due to their perception of inverse relationship between stock market and gold market. Basically, when gold price goes down, people withdraw their investment from gold and invest the same in stock market which in turn increase the value of the stock market due to heavy investment. When the economy is in a downturn and stock markets are going down, investors tend to park their funds in gold and wait out the storm.

Over the period of time, many researcher and academician studied the relationship between Indian Stock market and different variables like, Exports, Import, Foreign Direct Investment (FDI), Foreign Institutional Investment (FII), Gross Domestic Product (GDP), Oil Price, Gold Price, Exchange Rate, etc. that influence the Indian Stock market. In this study, researcher wants to examine the relationship between Gold Price and Indian stock market return. In this study, BSE Sensex is used to represent Indian stock market.

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Testing of Long-Run Relationship between Gold Prices and Stock Market Return: An Empirical Analysis in India Tanvi Bhalala*

Figure: 1 Movement of Gold Price and Stock Market in India

Sources: www.rbi.org.in

L

ITERATURE

R

EVIEW

The followings are the few studies that undertakes by different researchers in the past to study relationship between stock market and gold price as well as some other macroeconomics variables.

(Kaur & Kaur, 2017)haveanalyzed dynamic relationship between gold price and Indian stock market from 2007 to 2016. They have reported positive correlation between gold price and stock market as well as identified significant influence of gold price on Indian stock market. (Tripathy, 2016)has investigated dynamic relationship between gold price and stock market of India during 1990 -2016. There is no causal relationship fund between gold price and stock market price in short-run. However, long-run equilibrium relationship has been identified between variables.

(Singarimbun & Noveria, 2014)haveanalyzed relationship of oil prices, gold prices, gross domestic product and interest rate to the stock market return in Indonesia during 2005-2013. The study found that oil price and interest rate has significant influence on stock market return. (Ray, 2013)has examined that the gold price and stock price of India are cointegrated, indicating an existence of long run equilibrium relationship between the two series. The Granger causality test suggested that presence of uni-directional causality which runs from gold price to stock price.

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Testing of Long-Run Relationship between Gold Prices and Stock Market Return: An Empirical Analysis in India Tanvi Bhalala*

86

-of the analysis show that there is no causality between the gold price and Sensex.(Bhunia & Mukhuti, 2013)have examined relationship between Indian stock market and Indian gold prices. The study sis not found any cointegration between Indian stock market and gold prices.

(Wang, Wang, & Huang, 2010)revealedthat there was no co-integration exist in U. S. stock market while co-integration was exists in Germany, Japan Chine and Taiwan. They also studied that Taiwan group shows two way feedback relations between oil price and stock price as well as oil price and gold price. (Mishra, Das, & Mishra, 2010)haveanalyzed the relation between Gold price and stock market return. The study shows that gold price cause stock market return and stock market return cause gold price. (Gilmore, McManus, Sharma, & Tezel, 2009)have evaluated dynamics of gold prices, gold mining stock process and stock market process comovements. The result indicates cointegration among the variables as well as unidirectional causality found from large-cap stock process to gold mining company stock price and from gold mining company stock process to gold prices.

(Liao & Chen, 2008)have examined that the gold price fluctuation do not effect volatility in oil price but volatility of oil price influence gold price volatility. The volatility of electrical and rubber sub-indices were influenced by volatility of oil price and volatility of chemical, cement, automobile, food and textile sub-indices were affected by volatility of gold price. (Chakravarty, 2006)examined the relation between stock price and key macroeconomics variables. The study found that stock price and money supply has unidirectional relation there is and no relation between stock price exchange price as well as gold price and stock price.

Considering divergence views of various researchers on relationship between gold price and stock market return, the present study evaluate long-run relationship between gold price and stock market return of India.

O

BJECTIVEOF

T

HE

S

TUDY

The main objective is to study the long-run relationship between Indian stock market return and Domestic gold price in India.

D

ATAAND

M

ETHODOLOGY

The monthly closing price of BSE Sensex and monthly domestic gold price in Rs. per 10 grams has been used for the study. The data of 28 years is collected from January 1991 to September 2018. Both time series contains total of 333 observations. The data are collected from database of Reserve Bank of India and Bombay Stock Exchange.

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Testing of Long-Run Relationship between Gold Prices and Stock Market Return: An Empirical Analysis in India Tanvi Bhalala*

applying this test, it is essential to check stationarity for both variables. The ADF test is used to check stationarity of both time series. With that the Unit root break point test has been conducted to find out break point in time series.

AUGMENTED DICKEY-FULLER TEST

ADF test is an augmented form of the Dickey-Fuller test for a large and more complicated set of time series models. It is necessary to first focus on Dickey-Fuller test as ADF test drive from DF test. The DF test is estimated in three different forms as given:

Yt is a random walk: Yt = Yt–1 + ut (1)

Yt is a random walk with drift: Yt = 1 + Yt–1 + ut (2) Yt is a random walk with drift

around a deterministic trend: Yt = 1 + 2t + Yt–1 + ut (3) Where t is the time or trend variable. In each of the form of DF test, the hypothesis is: H0:  = 0 (i.e. there is unit root or the time series is non-stationary). If  = 0, null hypothesis is accepted; i.e. time series variable is non-stationary or there is unit root. While if < 0, null hypothesis is rejected; i.e. time series variable is stationary. ADF test is conducted by "augmenting" the above three equations by including the lagged values of the dependent variable Yt. The ADF test consist of estimating the following regression:

Yt = 1 + .Yt–1 + 2.t + m

(t=1)iYt–i + t (4)

Where ?tis a pure white noise error term,  is the coefficient of lagged Yt–1 and Yt–1 is equal to (Yt–1 – Yt–2), Yt–2 = (Yt–2 – Yt–3), etc. ADF test will still evaluates the same thing as in DF test whether ? = 0. In short, both the tests have same critical value.

Phillip-Perron Test

The DF test has been modified by (Phillips & Perron, 1988)and introduced PP test. The PP test can be applied when error terms are not uncorrelated, homoscedastic as well as identically and independently distributed (iid). Phillips- Perron (PP) has introduced a nonparametric method of adjusting serial correlation in the error term using the following regression, which is estimated by using the ordinary least squares (OLS) method:

Yt = 1 + .Yt–1+ t (5)

Where, t is I(0) and may be heteroskedastic. The benefit of using PP test is that it can be applicable for frequency domain approach. The PP test is follows the critical value similar as DF test. However, the PP test has more power of rejecting the null hypothesis of unit toot.

BEAK POINT UNIT ROOT TEST

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Testing of Long-Run Relationship between Gold Prices and Stock Market Return: An Empirical Analysis in India Tanvi Bhalala*

88

-2007). The structural breaks occurs due to economic changes such as; financial crisis, institutional changes, policy changes, regime changes or random shocks at domestic and international level in long run time series variables. The ADF and other stationarity tests do not normally include a structural break term. Thus, unit root test with structural break has become more suitable. In this study (Perron, 1997) Innovational Outlier and Additive Outlier model has been used for determining structural break point with evaluation of stationarity.

P

ERRON

I

NNOVATIONAL

O

UTLIER

M

ODEL

Perron (1997) re-examine his 1989 results with modification by introducing unknown break point. He represented statistical procedure which is used to test unit root with unknown structural break in trend function. According to Perron (1997), the Innovational outlier (IO) model evaluates break point considering gradual changes in intercept of time series (IO1) as well as gradual changes in both intercept and the slope (IO2) of the trend function as follows:

IO1: Y

t

= β

1

+ γ.DU

t

+ β

2

.

t + θ.D(T

b

)

t

+ α

.Y

t-1

+

= 1 i

ΔY

t-i

+ ε

t

(6)

IO2: Y

t

= β

1

+ γ.DU

t

+ β

2

.

t + δDT

t

+ θ.D(T

b

)

t

+ α

.Y

t-1

+

= 1 i

ΔY

t-i

+ ε

t

(7)

Where Tb stands for the time of break (1<Tb<T) which is unknown, DUt = 1 if t > Tb and zero otherwise, DTt = Tt if t > Tb and zero otherwise, D(Tb)t = 1 if t = Tb+1 and zero otherwise, Yt is any general ARMA process and t is the white noise residual term. If the absolute value of the t-statistic for  = 1 is greater than critical value, the null hypothesis of unit root is rejected. Other than this, Perron (1997) has given suggestions regarding determination of Tb (break point) by using two methods. First, the IO1 and IO2 equations are estimated sequentially, assuming different Tb. Afterwards Tb is selected where the t-ratio for  = 1 remains minimum. In the second method, Tb is selected from amongst all the possible break point values to minimise the t-ratio on the estimated slope coefficient ().

PERRON ADDITIVE OUTLIER MODEL

In contrary to the evaluation of gradual change with the help of IO model, the immediate structural changes in time series allows in Additive Outlier model (AO). The two-step procedure is given for evaluating stationarity under the AO framework. In first step, the trend is removed from the time series:

Yt = 1 + 2.t + DTt * + ~

y

t (8)

Where, ~

y

t is detrended series. The reason for using detrended time series is that the AO framework assumes that only slope coefficient is influenced by the structural break. Thus, the second step evaluates change in the slop coefficient as follows:

m

t=1 t 1 t

t t-1

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Testing of Long-Run Relationship between Gold Prices and Stock Market Return: An Empirical Analysis in India Tanvi Bhalala*

By following similar method as follow in IO methodology, the value of Tb as well as the lag length is determined, while the break date is unknown and determined endogenously. The IO model and AO model has been applied on time series variables included in the present study.

A

UTOREGRESSIVE

D

ISTRIBUTED

L

AG

(ADRL) A

PPROACHOF

C

OINTEGRATION In order to examine the long-run relationship and dynamic interactions among the variables of interest, the Autoregressive Distributed Lag approach of cointegration can be useful. The ARDL bounds cointegration approach has been developed by (Pesaran and Shin, 1998), (Pesaran and Smith, 1998) and (Pesaran, Shin and Smith, 1996, 2001).

The ARDL approach of cointegration has been implemented in the present study to relationship between gold prices and Indian stock market. According to Pesaran and Pesaran (1997) and Pesaran et al. (2001); the augmented ARDL (p, q1, q2,…..,qk) is given by the following equation:

α (L, p) y

t

= α

0 +

= 1 i (L, qi) xit

+ λ΄w

t

+ ε

t

(10)

Where

α (L, p) = 1

α

1

L –

α

2

L

2

– ….. –

α

p

L

p

and

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βi

(L, q

i

) =

βi0

+

βi1

L +

βi2

L

2

+……+

βiqi

L

qi

, i = 1,2,…., k

(12)

where yt is the dependent variables, 0 is the constant term, L is the lag operator such that Lyt = yt–1; and wt is a s × 1 vector of deterministic variables employed such as intercept term, dummy variables, time trends and other exogenous variables with fixed lags. The xit in equation (10) is the i independent variable where i = 1, 2,…., k. In the long-run estimation, the yt = yt–1 = ….=yt–p and xit= xi,t–1 =….=xi, t–q in that xi,t–q represents the qth lag of the ith variable. The equation for estimating long-run coefficients with respect to the constant term can be written as follows:

y = α

0

+

= 1 i

x

i

+ λ΄w

t

+ ν

t

,

α =

0

( 1, ) (13)

Where represents the OLS estimates of  in equation (10) for the selected ARDL model. The error correction representation of the ARDL model can be obtained by writing equation (10) in terms of lagged levels and the first difference of yt, x1t, x2t, …., xkt and wt:

=

0

( 1,

̂

) −1+

= 1 0

+ ′

∆ − ∑

1

= 1

∗ ∆

−1

= 1

∗ ∆

,−

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Testing of Long-Run Relationship between Gold Prices and Stock Market Return: An Empirical Analysis in India Tanvi Bhalala*

90

-Here, ECt is the error correction term defined as follows:

EC

t

=

− −

= 1

(15)

And  is the first difference operator, *, ' and ij* are the coefficients relating to the short-run dynamics of the model's disequilibrium to equilibrium while  (1, ) represents the speed of adjustment from short-run disequilibrium to long-run equilibrium.

This study uses the following error correction regressions, taking SensexR as a dependent variables:

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The ARDL bound test approach includes two steps for evaluating the long-run relationship. First, the long-run relationship among the variables will estimated by using Wald test (F-statistics). For evaluating long-run relationship, (Pesaran, Shin, & Smith, 2001)has developed two sets of asymptotic critical values. All variables are assume as I(0) under the first set (lower bound). While all the variables are assumed as I(1) under the second set (upper bound). If the calculated F-statistic is greater than upper bound critical values, there is rejection of null hypothesis. But the null hypothesis cannot be reject, if the computed F-statistics is smaller than the lower bound critical value. Finally, the result is inconclusive if the calculated F-statistics resulted in between lower and upper bound critical values. Second, if the long-run cointegration relationship exists, the both long-run coefficient and short-run coefficient estimated. The short-run coefficient helps the testing of short-run effect of independent variables on dependent variable. Similarly, significant long-run coefficients helps us to test long term effect of variables. The estimated error correction term also provides valuable information regarding the short-run adjustment to its long-run stability.

All the above mention techniques of econometrics have been performed by using EViews 9.

E

MPIRICAL

A

NALYSISAND

R

ESEARCH

F

INDINGS

First, descriptive statistics like skewness, Kurtosis, Jarque-Bera statistic and Probability values are calculated for both the variables. The results of same are presented in Table 1.

Table - 1 Descriptive Statistics

LGold SensexR

Skewness 0.4525 0.5634

Kurtosis 1.4934 7.2944

Jarque-Bera 42.8596 273.50

Probability 0.0000 0.0000

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Testing of Long-Run Relationship between Gold Prices and Stock Market Return: An Empirical Analysis in India Tanvi Bhalala*

From Table 1, it is clear that both the variables are positively skewed. Kurtosis values reveals that LGold and SensexR follow Platykurtic distribution. Jarque-Bera statistic tests the null hypothesis that data follow normal distribution. By using probability values of Jarque-Bera statistics, null hypothesis is rejected for both the variables at 1 percent level of significance. This indicates randomness and inefficiency of the market.

Before proceeding with ARDL bound test, it is necessary to determine the order of integration for time series. The ADF and PP test is conducted for log of gold price and return of BSE Sensex to evaluate stationarity property of time series. The result of ADF and PP test is reported in Table 2.

Table 2: Result of Augmented Dickey-Fuller test and Phillip-Perron test

Variables ADF test PP test

Level 1st Difference Level 1st Difference

LGold -1.4028

(0.8587)

-16.6240 (0.0000)

-1.4828 (0.8338)

-16.6065 (0.0000)

SensexR -13.6817

(0.0000)

-13.7073 (0.0000)

Source: Complied Data

The result of ADF and PP test is reported in Table 2. The null hypothesis of unit root can rejected for SensexR at level. This indicates that SensexR is I(0). However, the null hypothesis cannot be rejected for LGold, indicating that log of gold contain unit root at the level. After taking the first difference again, ADF and PP test are conducted and found that null hypothesis of unit root can be rejected for LGold. Thus, LGold is stationary and integrated of first order, i.e., I(1).

As ARDL model of cointegration can be applied for time series with mixed order integration, the time series included in study has been not going to create any issue. But to make result of order of integration more clear and to identify structural break point, Perron (1997) Innovational Outlier (IO) and Additive Outlier (AO) model has been used. The result of IO and AO models are represented in table - 3:

Table 3: Result of Perron Innovational Outlier and Additive Outlier Model

IO Model AO Model

ADF test Break Point ADF test Break Point

LGold -1.4818

(0.9291) 2008M11*

-1.4276

(0.9250) 2015M12

SensexR -13.7155

(<0.01) 2009M03

-13.7244

(<0.01) 2006M04

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Testing of Long-Run Relationship between Gold Prices and Stock Market Return: An Empirical Analysis in India Tanvi Bhalala*

92

-The result of IO and AO model represented in table 3 indicates that SensexR is stationary at level while LGold contain unit root with structural break. Thus, the result of IO model indicates that LGold have unit root with significant structural break at November 2008. While Sensex return is stationary at level and break point identified at March 2009 but structural break is not statistically significant. The result of AO model also indicates that SensexR is stationary with structural break point at April 2006 while LGold contain a unit root with structural break point at December 2015, but none of these structural breaksare statistically significant. Thus, the estimation of cointegration relationship between gold prices and Indian stock market return has been done by including structural break of 2008M11.

As the objective of the study is to evaluate relationship between gold price and Indianstock market return, LGold and SensexR both are considered as dependent variables one by one. Thus, the bidirectional relationship can be evaluated. The ARDL bound test is conducted as the first step of the ARDL analysis, to evaluate existence of long-run equilibrium relationship among the variables. Since as the study has covered 333 observations, the maximum lag length of four has been chosen. Further, this study has uses the Akaike Information Criteria (AIC) for selection of model with appropriate lag length. The table 4shows the result of ARDL bound test:

Table 4: ARDL Bound Test: F-statistic for SensexR and LGold

Equation Calculated F- statistic

F (LGold / SensexR, D1) 1.5947

F (SensexR / LGold) 62.4740

The relevant critical value bounds are obtained from Table CI (iv). Case iv: unrestricted intercept and restricted trend (Pesaran et al. 2001). The critical values are 4.68 (lower bound) and 5.15 (upper bound) at the five percent as well as 4.05 (lower bound) and 4.49 (upper bound) at ten percent significance level.

Source : Complied Data

The table 4 shows computed F-statistic for LGold is less than lower bound critical value at five percent level of significance. This indicates that null hypothesis of Non- existence of long-run relationship cannot be rejected. On the other hand, when SensexR is used as dependent variable, the computed F-statistic for SensexR is more than upper bound critical value at five percent level of significance. This indicates that null hypothesis of Non- existence of long-run relationship can be rejected. Thus, it can be said that there is long-run relationship exist among SensexR and LGold.

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Testing of Long-Run Relationship between Gold Prices and Stock Market Return: An Empirical Analysis in India Tanvi Bhalala*

Table 5: Estimated Long-Run Coefficients and Short-Run Error Correction Model (ECM)Dependent Variable: SensexR

The long-run Coefficients estimates based on ARDL (1,0) selected lags based on AIC

ECM- ARDL: dependent variable: ? SensexR based on ARDL (1,0) selected lags based on AIC

Regressors Coefficient t-ratio Regressors Coefficient t-ratio

LGold 1.0045 0.5675 ? LGold -10.0002 -0.9363

@trend -0.0121 -0.8314 Constant -4.1184 -8.5979***

ecm(-1) -0.7165 -13.6872***

Note: ***significant at 1% level, ** significant at 5% level and * significant at 10% level.

Source: Complied Data

The result of long-run and short-run estimates indicate that log of gold prices have positive impact on Sensex return in long-run and have negative impact on Sensex return in short-run. A one percent change in log of gold prices will have a positive impact on Sensex return by 1.01 percent in long-run, but not statistically significant. Similarly, log of gold prices have negative impact on Sensex return by 10 percent in short run, but not statistically significant. The trend variable is negative and not significant in long run. In short-run, the constant is also negative and significant at the one percent level.

The error correction model with selected ARDL is (1, 0) as shown in Table 5 is significant at the one percent level with the expected negative sign. The ecm (-1) indicated the speed of adjustment of ? SensexR to its long-run equilibrium following a shock. The ecm(-1) of -0.72 suggests that a deviation from the long-term path of gross domestic product in this period is corrected by 72 percent over the following year.

Table 6: ARDL Diagnosis test

Test F-statistics P- Value

Serial Correlation LM Test 2.8188 0.0611

Heteroskedasticity Test 8.2629 0.0000

Functional Form 1.9733 0.1610

Source : Complied Data

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Testing of Long-Run Relationship between Gold Prices and Stock Market Return: An Empirical Analysis in India Tanvi Bhalala*

94

-R

ESEARCH

I

MPLICATIONS

Stock market performance is considers as important parameter for investors of capital market. There are many factors with contributing in fluctuations in stock market prices. The Gold price is considered as one of the important factors which also attract investor to make investment. The study has reported that long run relationship present between stock market return and gold price. Thus, investors should considered this relationship between two variables while making decision regarding investment in stock market.

L

IMITATIONSOF

T

HE

S

TUDY

The study has been conducted by using secondary data. Thus, the accuracy of the findings is restricted to the authenticity of the data. In the present study, the long-run relationship of stock market return with only one variable has been evaluated. The result may be change if more variables are introduce to evaluate relationship.

C

ONCLUSION

This paper examines the existence of long-run relationship between gold prices and stock market return in India. This study has used ADF test and PP test to examine unit toot for Gold price and stock market return. The Perron Innovational Outlier and Additive Outlier model has been used to identify structural break point along with evaluation of stationarity of both time series. The result identified significant break point in gold price at November 2008, during the period of global financial crisis. The existence of long-run relationship has been evaluated by using autoregressive distributed lag approach of cointegration. Under that, the ARDL bound test conducted first, which indicate existence of long run relationship between stock market return and Gold price in India. Theestimation of long-run and short-run coefficients indicate that gold prices not have significant effect on stock market return during long-run and short-run. However, error correction term indicating stability of relationship between both the variables in long-run. Thus, this study suggests that there is long-run relationship exists between stock market return and Gold prices in India during 1991 to 2018. This indicates that there is an integration between gold price and stock market return which help the investors to make diversification of their portfolio and to make better investment decision.

S

COPEFOR

F

URTHER

R

ESEARCH

The study offers few directions for further research:

The present study has not considered other variables which may influence performance of stock market. Hence, the future studied can conducted covering other macroeconomic variables such as GDP, Crude Oil price, Silver Price, IIP, WPI, etc…

The study conducted using monthly data. The future studies can done by using quarterly data.

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Testing of Long-Run Relationship between Gold Prices and Stock Market Return: An Empirical Analysis in India Tanvi Bhalala*

R

EFERENCES

[1]. Bhunia, A., & Mukhuti, S. (2013). The Impact of Domestic Gold Price on Stock Price Indices - An Empirical Study of Indian Stock Market. Universal Journal of Marketing and Business Research, 2(2), 35-43.

[2]. Chakravarty, S. (2006). Stock Market and Macroeconomic Behaviour in India. Discussion Paper- 106,

Institute of Economic Growth , New Delhi.

[3]. Cheung, Y.-w., & Fares, H. (1995, July). Lag Order and Critical Values of Augemented Dickey Fuller Test. Journal of Business & Economic Statistics, 12(3), 277-280.

[4]. Dempster, N. (2006). The Role of Gold In India. Gold Report, World Gold Council.

[5]. Dickey, D. A., & Fuller, W. A. (1979). Distribution of teh estimates for autoregressive time series with a unit root. Journal of American Statistical Association, 74(366), 427-431.

[6]. Dickey, D., & Fuller, W. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74, 427-431.

[7]. Enders, W. (2004). Applied Econometric Time Series. John Wiley & Sons.

[8]. Gilmore, C. G., McManus, G. M., Sharma, R., & Tezel, A. (2009). The Dynamics of Gold Prices, Gold Mining Stock Prices and Stock Market Prices Comovements. Reserach in Applied Economics, 1(1), 1-19.

[9]. Gujarati, D. (2007). Basic Econometrics. New Delhi: Tata McGraw-Hill.

[10]. Gujarati, D. (2011). Econometrics By Example. New York: Palgrave Macmillan.

[11]. Kaur, S., & Kaur, D. (2017). Dynamic Relationship Btween Gold Prices and Indian Stock Market - An Empirical Analysis. International Conference on Recent Innovations in Science, Agriculture, Engineering & Management, (pp. 454 - 460). Bathinda, Punjab.

[12]. Liao, S. J., & Chen, J. T. (2008, July 7th & 8th). The Relationship among Oil Prices, Gold Prices and the Individual Inustrial Sub-Indices in Taiwan. International Conference on Business and Information, (pp. 401-409). Seoul.

[13]. Mishra, P. K., Das, J. R., & Mishra, S. K. (2010). Gold Price and Stock Markets Retruns In India. American Journal of Scientific Research(9), 47-55.

[14]. Narang , S. P., & Singh, R. (2013). Causal Relationship between Gold Price and Sensex: A Study in Indian Context. Vivekananda Journal of Research, 33-37.

[15]. Patel, S. (2012, August). The Effect of Macroeconomic Determinants on Performance of the Indian Stock Market. NMIMS Management Review, 117-127.

[16]. Perron , P. (1989). The Great Crash, the Oil Orice Shock and the Unit root Hypothesis. Econometrica, 57, 1361-1401.

[17]. Perron , P. (1994). Trend, Unit Root and Structural Change in Macroeconomic Time Series. In B. B. Rao, Cointegration for the Applied Economist (pp. 113-146). London: Macmillan Press.

[18]. Pesaran, M. H., & Shin, Y. (1998). An Autoregressive Distributed Lag Modelling Approach to Cointegration Analysis. In S. Storm, A. Holly, & P. Diamond, Econometrics and Economic Theory in the 20th Century: The Ranger Frisch Centennial Symposium. Cambridge: Cambridge University Press.

[19]. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bound testing approaches to the analysis of level relationships. Journal of applied Econometrics, 16, 289-326.

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Emerging Distribution Channel Effectiveness in Rural Jharkhand for Consumer Electronics Punit Kumar Mishra*, Girish Kumar Srivastava**

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-[21]. Ray, S. S. (2013). Causal Nexus beteen Gold Price movement and Stock Market: Evidence from Indian

Stock Market. Econometrics, 1(1), 12-19.

[22]. Singarimbun , C. M., & Noveria, A. (2014). The Relationship among Oil Prices, Gold Prices, Gross Domestic Product and Interest rate to the Stock Market Retrun of Basic Industry and Chemincal Sector in Indonesia in 2005-2013. Journal of Business and Management, 3(4), 401-409.

[23]. Tripathy, N. (2016, Auguest). A Study on Dynamic Relationship between Gold Price and Stock Market

Price in India. European Journal of Economics, Finance and Administrative Science(88).

Figure

Table - 1 Descriptive Statistics
Table 2: Result of Augmented Dickey-Fuller test and Phillip-Perron test
Table 5: Estimated Long-Run Coefficients and Short-Run Error Correction Model

References

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