SSRN-id2229290

Electronic copy available at: http://ssrn.com/abstract=2229290

Day of the Week Effect on

Gold Returns

Research Paper

Presented by: Sunita Arora Assistant Professor

Department of Commerce Government College for Women

Rohtak

Under the Guidance of: Dr. Narender Kumar Professor

Department of Commerce M D University

Rohtak

Electronic copy available at: http://ssrn.com/abstract=2229290

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Day of the Week Effect on Gold Returns

Abstract:

Day of the week effect has vastly been investigated for stock markets, but commodity markets have got less attention in this regard. Present study is an attempt to investigate the day of the week effect on gold futures return. For the purpose of study, opening and closing prices of gold futures contract, traded on Indias largest commodity exchange i.e. Multi Commodity Exchange of India Limited (MCX), have been analysed with the help of dummy variables. Daily return from close to close prices was calculated and was further decomposed into trading period return and non trading period return. Gold is traded on MCX six days a week, from Mondays through Saturdays; therefore dummies for all the six days have been applied. Period of study spans from 1st April 2006 to 31st March 2012. Data has been analysed by applying linear regression of dummy variables on return. But after applying linear regression, ARCH effect was found to be left. To account for ARCH effect, GARCH(1,1) model was applied. Extreme value analysis was also carried on with the help of chi-square test. Presence of day of the week effect was found in all the three types of return series, but the impact was more during non trading period than during trading period. Results of the study may be useful for commodity market players in timing their trading. Key Words: Commodity market, Day of the week effect, Trading Period Return, Non-Trading Period Return, Heteroskesdasticity. JEL Classification: G14. Introduction: Efficient market hypothesis implies that asset prices should move randomly and there

should not be a pattern, on the basis of which market participants are able to earn

abnormal returns. This implies that the return for an asset in an efficient market should

not be predictable and there should be no seasonality in the returns of an asset i.e. return

for different periods e.g. different periods of a day, different days of a week, different

weeks of a month and different months of a year should not be different. If return for

different trading periods is same, return generating process is said to be following trading

time model and if return is same for different time periods, whether tading or non-trading,

it is said to be following calendar time model. The present study is an attempt to

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investigate the applicability of trading time model or in other words, day of the week

effect, in commodity futures market in India and for the purpose, precious metals

segment has been chosen for the study and metal selected is gold.

Gold is traded 24 h a day and has been an important precious metal for many millennia

and almost all the gold ever mined is still in existence. Demand for gold arises from

consumers in the form of jewellery, dental fillings and other uses; from industry as one of

the most ductile metals and as an excellent conductor of heat and electricity; and from

central banks and investors. Gold also plays an important role as a store of value

especially in times of political and economic uncertainty. (Aggarwal, R. and Lucey, B.M.

2007: 218)

Multi Commodity Exchange of India Limited started operations in November 2003 and

holds a market share of over 85% of the Indian Commodity futures market.1 It offers

trading in different segments like bullion, metals, energy, oil and oilseeds, plantations,

pulses, spices etc. Gold is the highest traded metal on Multi Commodity Exchange of

India Limited (MCX). Following table shows the value of gold traded on MCX from

2003 to 2011.

Table I Volume of Gold traded on MCX (` in lakhs)

Year Gold 2003 13364 2004 4012886 2005 17920890 2006 91844784 2007 74786092 2008 184054386 2009 207797608 2010 248477853 2011 384270982

Source: http://www.mcxindia.com/SitePages/HistoricalDataForVolume.aspx, accessed on 05 January 2012. Amount rounded off to nearest lakh rupees.

1http://www.mcxindia.com/aboutus/aboutus.htm#top.accessedon21062012.

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Objective of the Study: There are many studies that investigated day of the week effect

on stock market returns of the developed countries and some attempts have been made to

investigate day of the week effect on the returns of stock markets of emerging countries.

But commodity markets got less attention in this regard. There are very few studies

which investigated the day of the week effect on the commodity markets of emerging

countries. This stimulated us to study the day of the week effect on the return of highly

traded commodity futures contract in India i.e. gold contract.

Review of Previous Studies: Review of the studies already done in the field is necessary

to understand the methodology to be applied for the purpose. Review of some studies

consulted by us is:

French, K.R. 1980, analysed closing prices of S&P portfolio from 1953 through 1977, for

the purpose of testing- whether the return generating process follows trading time

hypothesis or calendar time hypothesis. Period of study was divided into five sub periods

and the results of the study show that return for Monday was negative and lower than the

average return for any other day for each of the five sub periods. Neither the trading time

nor the calendar time model was found to be an accurate description of return generating

process.

Ball, C.A., Torous, W.N. and Tschoegl, A.E. 1982, analysed morning and afternoon

fixing prices of gold in London from January 2, 1975 to June 30, 1979 and generated four

series of return- morning to morning change, afternoon to afternoon change, overnight

change and the within day change. Results of the study show that overnight changes are

less variable than within the day changes, daily variances are not equal, weekend

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variances are not very different from daily variances. With regard to return variances,

trading time hypothesis was found to be better fit on data than a calendar time hypothesis.

Ma, C.K. 1986, analysed daily gold afternoon fixings from January 1972 to June 1985 by

dividing the period of study into two parts before October 1981 and after October 1981,

on the basis of implementation of same day settlement procedure. The study concluded

that high Wednesday effect before 1981 was due to settlement effect; and the return

generating process in the gold market is more consistent with the calendar time

hypothesis. Thus a higher than usual return on the day following a non trading period

may be expected in a bull market and a lower return in a bear market period.

Dyl, E.A. and Maberly, E.D. 1986, analysed daily opening and closing prices of S&P 500

from June 1, 1982 to May 17, 1985 and studied separately trading and non trading period

returns. Study did not observe the presence of day of the week effect for close to close

return but observed the same for the non trading period.

Gay, G.D. and Kim, T.H. 1987, analysed data on Commodity Research Bureau Index

from September 1956 to March 1985 and divided the period of study into two sub

periods, on the basis of enactment of Economic Recovery Act, 1981 which ends any

motivation for tax loss selling in the market. The study observed significant returns for

Wednesdays and Fridays, highest Friday return than all other days return with lowest

variance and for Mondays, lowest and negative return with highest variance before 1981.

The pattern disappeared during the post 1981 time period.

Agrawal, A. 1994, studied indices data for 18 countries. Time period for 12 countries out

of these 18 countries spans from 1971 to 1987 and for the remaining 6 countries time

period varied. Results for USA were reported from other studies for comparison. The

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study observed that Wednesday and Friday returns were large and significantly positive

in most of the countries. For Monday, seven countries exhibit significant negative return.

Strong negative Tuesday effect was found in several countries examined.

Kamath, R.R., Chakornpipat, R. and Chatrath, A. 1998, analysed closing prices index of

stock exchange of Thailand and its ten industry classified indices from January 1980

through December 1994 and observed negative Monday and positive Friday returns.

Berument, H. and Kiyamaz, H. 2001, analysed closing prices of S&P 500 from January

1973 to October 1997. Highest return was observed on Wednesdays and lowest return

was observed on Mondays; highest volatility was observed on Fridays and lowest on

Wednesdays.

Sarma, S.N. 2004, analysed daily return generated by SENSEX, NATEX AND BSE 200,

from January 1, 1996 to August 10, 2002. Results of the study show that there is

seasonality in Indian stock markets returns pattern and a trading strategy of buying on

Mondays and selling on Fridays may be designed by the investors of SENSEX; and for

other two indices a passive buy and hold strategy was found to be more effective.

Kaur, H. 2004, studied day of the week effect in return as well as in variance by including

dummies in both the equations of GARCH model for a period from 1993 to 2003. The

study was divided into two sub periods, one before the introduction of the rolling

settlement and the other after the introduction of rolling settlement by Bombay Stock

Exchange and National Stock Exchange. The results of the study indicate the presence of

the day of the week effect in returns as well as in volatility with Mondays, Tuesdays and

Fridays having lower return and Wednesdays having higher return; and lower volatility

was observed for Tuesday.

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Aly, H., Mehdian, S. and Perry, M.J. 2004, analysed closing prices of Egyptian stock

market index from April 26, 1998 to June 6, 2001. The study observed Monday positive

and significant return but not significantly different from other days of the week.

Variance for Monday was found to be significantly higher than the variance for rest of

the week.

Lucey, B.M. and Tully, E. 2006, analysed cash and futures series of gold and silver,

traded on COMEX from January 1982 to November 2002. For unconditional mean,

variance; seasonality in the mean, if present, was found to be weak and not statistically

significant; and seasonality was stronger for variance than for the mean. For conditional

mean and variance in gold and silver cash, negative Monday effect was observed but the

same was not observed for gold and silver futures market.

Singhal, A. and Bahure, V. 2009, analysed daily closing values of BSE SENSEX, BSE

200 and Nifty form April 2003 to April 2008 and observed lower Monday and higher

Friday return than the return of rest of the week. The same was existent even after some

adjustments related to investors expectations were made by the researchers.

Rahman, M.L. 2009, analysed daily closing prices of 3 indices of Dhaka Stock Exchange

from 04-09-2005 to 08-10-2008 and observed Thursday (last trading day of the week at

Dhaka Stock Exchange during the period of study) mean return for all the three indices

was positive and significant; whereas for Sundays and Mondays, mean return was found

to be negative and significant.

Puja, P. 2010, tested the presence of day of the week effect in aggregate indices, BSE 100

Index, BSE 500 Index, BSE-Sensitive Index, S&P CNX 500 and Nifty, for a period from

January 1, 1990 to November 30, 2004. The results of the study show that average return

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for the Mondays, Tuesdays and Thursdays- returns for Nifty, S&P CNX 500 are negative

and significant; and for Fridays- Nifty, S&P CNX 500 and BSE 500 have significant

negative return.

Suman and Chahal, S.S. 2011 examined the day of the week effect on BSE Sensex from

January 1, 1999 to May 31, 2010. No day of the week effect is observed on returns of

BSE Sensex and same is found in volatility, the highest volatility being occurring on

Mondays.

On the basis of above literature review it can be said that no set theory can be developed

for the presence of day of the week effect in stock as well in commodity markets and a lot

of research is needed in this area. Present study is an attempt in this regard.

Data for the Study: For the purpose of present study, daily closing prices of near month

futures contract for gold, traded on Multi Commodity Exchange of India Limited, have

been collected from the website of the exchange (www.mcxindia.com). Near month

futures contract has been considered on the assumption that it is the most traded futures

contract for any underlying asset. Following- French. 1980, Ball, et al. 1982, Dyl, et al.

1994, Kamath, et al. 1998, we exclude the return for the days following holidays during

the week. The data has been analysed with the help of MS Excel 2007, SPSS 16 and

Eviews-6.

Period of Study: Period of study spans from 1 April 2006 to 31 March 2012.

Analysis of Data: Data has been analysed by applying following statistical and

econometric techniques:

Return: Series of daily closing prices has been converted in to continuous daily

percentage return series by applying the following formula:

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R ln P ln P 100

WhereR is the return for day t, lnis the natural log, Pt and Pt-1 are the closing prices for

day t and its previous trading day. Daily return has further been decomposed into trading

period return and non trading period return with the help of following formulae:

Trading Period Return = lnclose ln 100

Where ln is the natural log, close is the closing price for day t and is the open

price for day t.

Non trading Period Return = ln ln 100

Where ln is the natural log, is the open price for day t and is the close

price for day t-1. Thus for non trading period return, the impact of period from previous

close to present open is considered.

Basic Statistics: Before applying any statistical or econometric technique on the data,

basic statistics of the data should be studied for the purpose of knowing statistical

properties of the data. In the present study basic statistics for data have been studied and

the results are shown in Table II, Table III and Table IV.

Stationarity of Data: Basic requirement for analyzing any time series is that the data

must be stationary. Because all the three return series in the present study are time series,

so these have been tested for stationarity on the basis of graphical presentation as well as

with the help of the Augmented Dickey-Fuller (ADF) test.

The Augmented Dickey-Fuller (ADF) test consists of estimating the following regression

(Gujarati, D.N. and Sangeetha. 2007: 817).

Y t Y

Y

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If on the basis of this test, a time series is found to be non stationary, it is transformed, to

make it stationary before applying econometric techniques on it.

Day of the Week Effect: Day of the week effect on gold return has been studied by

introducing dummies for different trading days of the week in the regression equation. As

trading on Multi Commodity Exchange of India Limited is carried on six days a week,

from Mondays through Saturdays, dummies for all the six days were introduced. First of

all ordinary least square regression was applied with the help of following equation:

Where 1...6 are coefficients to be estimated and M, Tu, W, Th, F and S are dummies for

the trading days of the week. M=1 if the day is a Monday and 0 otherwise. Same process

is followed for other trading days. The hypothesis to be tested is:

Ordinary least square equation assumes the existence of constant variance, but if the

variance is time varying results of the equation may be misleading. So the results of the

ordinary least square equation were tested for ARCH effect left in the residuals. If the

same was found to be there, GARCH (1.1) model was applied on the data to account for

ARCH effect. In GARCH (1,1) model, the return equation described above was followed

but variance equation was:

Where is previous period error term, also known as ARCH term and h is

previous period variance, known as GARCH term.

Extreme Value Analysis: To investigate the effect of the day of the week on extreme

returns, 10% minimum values and 10% maximum values of return were considered and

days for the same were observed. Non-parametric Chi-square test was applied to test

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whether there is any significant difference in days of the week for the same. This

procedure was applied only for daily return series.

Empirical Results: Table II, Table III and Table IV show the Descriptive Statistics for

daily return series, trading period return series and non-trading period return series. 5%

trimmed mean has been calculated with the help of SPSS 16. 5% trimmed mean is the

simple average for the 95% values- after deleting extreme 2.5% both from lower area and

from upper area. Thus for 5% trimmed mean outliers are excluded from both the sides.

Table II Descriptive Statistics for Daily Return

Statistic Overall Return

Monday Return

Tuesday return

Wednesday Return

Thursday Return

Friday Return

Saturday Return

Mean 0.067429 0.015691 0.075405 0.073069 -0.001750 0.117426 0.126942 Trimmed Mean 0.084588 0.067804 0.092849 0.098615 -0.007256 0.128034 0.103208 Median 0.081086 0.026450 0.067692 0.114561 0.050355 0.113148 0.087474 Std. Dev. 1.109455 1.124558 1.170264 1.254630 1.190188 1.209932 0.511579 Skewness -0.321837 -0.907212 -0.471834 0.015007 0.172630 -0.455469 0.233846 Kurtosis 8.787535 6.122517 7.245348 10.76070 5.183417 8.237026 21.57338

Jarque-Bera 2547.479 160.3109 240.3589 770.4339 62.30303 352.0258 4185.418 Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

Observations 1803 295 305 307 306 299 291

Table III Descriptive Statistics for Trading Period Return


Monday Trading Period Return

Tuesday Trading Period Return

Wednesday Trading Period Return

Thursday Trading Period Return

Friday Trading Period Return

Saturday Trading Period Return

Mean 0.050414 -0.020089 0.069356 0.051941 -0.014466 0.125498 0.091359 Trimmed Mean 0.066473 0.015394 0.097867 0.070358 -0.011160 0.136461 0.068886 Median 0.081429 0.011242 0.102084 0.106070 0.081803 0.168955 0.063495 Std. Dev. 1.048100 1.040023 1.141528 1.168860 1.134154 1.154031 0.431462 Skewness -0.300378 -0.584469 -0.640804 0.250309 -0.087367 -0.545324 2.062100 Kurtosis 9.879979 6.553224 7.449919 13.39583 5.326086 9.350240 16.23641


Observations 1804 295 305 307 306 299 292

Table IV Descriptive Statistics for Non-Trading Period


Monday Non Trading Period

Tuesday Non Trading Period

Wednesday Non Trading

Period

Thursday Non Trading

Period

Friday Non Trading Period

Saturday Non Trading

Period

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Return Return Return Return Return Return Mean 0.016795 0.035780 0.006049 0.021128 0.012717 -0.008071 0.034081 Trimmed Mean 0.017546 0.031310 0.007735 0.015931 0.007147 0.003008 0.038739 Median 0.031182 0.053478 0.022449 0.032614 0.021927 0.008975 0.038665 Maximum 2.185756 1.724572 1.640995 2.185756 1.088430 2.109134 1.906272 Minimum -3.645309 -2.964293 -1.523890 -2.157625 -1.036991 -1.893307 -3.645309 Std. Dev. 0.331065 0.386029 0.287064 0.387774 0.252017 0.289693 0.361159 Skewness -1.080972 -1.485405 -0.124122 -0.137791 0.408767 -0.327630 -3.226559 Kurtosis 26.60410 23.33778 12.30846 18.36202 7.165566 19.32522 46.17567


Observations 1803 295 305 307 306 299 291 The main observations from the results depicted in above tables are-

Value of trimmed mean has improved than mean in almost all the cases except for

Thursday daily return, Saturday daily return, Saturday trading period return,

Monday non trading period, Wednesday non trading period and Thursday non

trading period. Thus all these exceptional periods have more negative extreme

values than other periods have.

As far as skewness is related some periods have positive skewness and some have

negative skewness. Main observation about skewness is that skewness for

Saturday daily return has a positive value of 0.233846 whereas its trading period

return has a positive value of 2.062100 and non trading period return has a

negative value of 3.226559, it is an exceptional observation for which no reasons

could be found by the researchers of the present study.

All the distributions are non-normal as the null hypothesis of normal distribution

is rejected by Jarque-Bera test ( value for all the cases is 0)

Stationarity of Data: The results of OLS regressions will be spurious if the dependent

variable is non-stationary (Pandey, I.M. 2002: 4). In the present study, dependent variable

is daily gold return, gold return for trading period and gold return for non trading period.

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So we checked the stationarity of all the three return series graphically and the results

have been presented with the help of Graph I, Graph II and Graph III.

Graph I Daily Return

Graph II Trading Period Return

Graph III Non Trading Period Return

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It is clear from the above graphs that trading period return is more clustered than non

trading period return and non trading period return has more spikes than trading period

return. But as far as stationarity is concerned, all the return series seem to be stationary.

Stationarity is further been checked by the Augmented Dickey-Fuller (ADF) test.

Table V

Result of the Augmented Dickey-Fuller Test Null Hypothesis: Return Series have a unit root Exogenous: Constant Lag Length: 0 (Automatic based on SIC, MAXLAG=24)

Augmented Dickey-Fuller test statistic t-Statistic Prob.* Daily Return -43.80035 0.0001 Trading Period Return -42.70118 0.0000 Non Trading Period Return -36.71938 0.0000 *MacKinnon (1996) one-sided -values. Results of graphical presentation are confirmed by the results of the Augmented Dickey-

Fuller test. Null hypothesis of having unit root by the series have been rejected in case of

all the three series and it can be said that all the three return series are stationarity and the

required econometric models/techniques can be applied on the series.

Day of the Week Effect on Return Series:

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Results of the ordinary least square equation with dummies for 6 days of the week, as

discussed in the methodology, have been applied on daily return, trading period return

and non trading period return and the results are presented in Table VI, Table VII and

Table VIII.

Table VI Results of Ordinary Least Square Equation with Dummy Variables on Daily Return

Dependent Variable: DAILY RETURN Method: Least Squares Sample (adjusted): 2 1804 Included observations: 1803 after adjustments Variable Coefficient Std. Error t-Statistic Prob. MONDAY 0.015691 0.064626 0.242796 0.8082TUESDAY 0.075405 0.063557 1.186412 0.2356WEDNESDAY 0.073069 0.063350 1.153424 0.2489THURSDAY -0.001750 0.063453 -0.027573 0.9780FRIDAY 0.117426 0.064192 1.829304 0.0675SATURDAY 0.126942 0.065068 1.950901 0.0512Durbin-Watson stat 2.059987 Obs*R-squared

46.52858 0.0000

Table VII Results of Ordinary Least Square Equation with Dummy Variables on Trading Period Return

Dependent Variable: TRADING PERIOD RETURN Method: Least Squares Sample: 1 1804 Included observations: 1804 Variable Coefficient Std. Error t-Statistic Prob. MONDAY -0.020089 0.061030 -0.329167 0.7421TUESDAY 0.069356 0.060021 1.155527 0.2480WEDNESDAY 0.051941 0.059825 0.868218 0.3854THURSDAY -0.014466 0.059923 -0.241416 0.8093FRIDAY 0.125498 0.060620 2.070230 0.0386SATURDAY 0.091359 0.061342 1.489331 0.1366Durbin-Watson stat 2.010375 Obs*R-squared 33.54269 0.0000

Table VIII

Results of Ordinary Least Square Equation with Dummy Variables on Non Trading Period Return Dependent Variable: Non Trading Period Return Method: Least Squares Date: 12/20/12 Time: 20:51 Sample (adjusted): 3 1804 Included observations: 1802 after adjustments Variable Coefficient Std. Error t-Statistic Prob.

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MONDAY 0.031003 0.019128 1.620819 0.1052TUESDAY 0.000653 0.018790 0.034756 0.9723WEDNESDAY 0.019022 0.018712 1.016584 0.3095THURSDAY 0.010374 0.018743 0.553493 0.5800FRIDAY -0.009807 0.018960 -0.517268 0.6050SATURDAY 0.034996 0.019217 1.821112 0.0688

Non Trading Period Return (-1) 0.144435 0.023345 6.187105 0.0000Durbin-Watson stat 2.009072 Obs*R-squared 9.706437 0.0018

It is clear from the Tables VI, VII and VII that for daily return, Friday and Saturday are

significant days, respectively at 6.75% and 5.12% level of significance; for trading

period return significant day is only Friday; and for non-trading period return, Saturday is

significant at 6.88% level of significance.

Durbin Watson stat is the test for the presence of serial correlation in residuals. This stat

should be near to two, so that there is no serial correlation in residuals. We found this stat

near to two in case of daily return and for the return for trading period; but it was

1.710703 in case of non trading period return, so we tried to include volume and open

interest as exogenous variable, but the value of Durbin Watson stat was not improved.

Then we tried the one period lagged value of non trading period return and get the Durbin

Watson stat near to two. Thus the problem of serial correlation was solved by adding one

period lagged value of non trading period return as exogenous variable. In all the three

cases value of Obs*R-squared, which is the test for heteroskesdasticity in residuals, is

significant, thus indicating the presence of heteroskesdasticity and also indicating the

need for applying the model of ARCH family. For this purpose we applied vastly applied

GARCH (1,1) model.

Table IX Results of GARCH (1,1) Model with Dummy Variables in Daily Return Equation

Dependent Variable: DAILY RETURN Method: ML - ARCH (Marquardt) - Normal distribution

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Sample (adjusted): 2 1804 Included observations: 1803 after adjustments Convergence achieved after 87 iterations Presample variance: backcast (parameter = 0.7) GARCH = C(7) + C(8)*RESID(-1)^2 + C(9)*GARCH(-1) Variable Coefficient Std. Error z-Statistic Prob. MONDAY 0.010191 0.057318 0.177802 0.8589TUESDAY 0.101268 0.044831 2.258879 0.0239WEDNESDAY 0.055899 0.035984 1.553437 0.1203THURSDAY -0.005852 0.045093 -0.129780 0.8967FRIDAY 0.079437 0.046444 1.710368 0.0872SATURDAY 0.077948 0.119096 0.654495 0.5128 Variance Equation C 0.011293 0.001965 5.745980 0.0000RESID(-1)^2 0.053784 0.006091 8.829623 0.0000GARCH(-1) 0.936935 0.006047 154.9423 0.0000

Table X

Results of GARCH (1,1) Model with Dummy Variables in Trading Period Return Equation Dependent Variable: TRADING PERIOD RETURN Method: ML - ARCH (Marquardt) - Normal distribution Sample: 1 1804 Included observations: 1804 Convergence achieved after 76 iterations Presample variance: backcast (parameter = 0.7) GARCH = C(7) + C(8)*RESID(-1)^2 + C(9)*GARCH(-1) Variable Coefficient Std. Error z-Statistic Prob. MONDAY -0.022111 0.051695 -0.427720 0.6689TUESDAY 0.108090 0.039164 2.759934 0.0058WEDNESDAY 0.039982 0.034118 1.171859 0.2413THURSDAY -0.011069 0.040357 -0.274282 0.7839FRIDAY 0.099143 0.041501 2.388910 0.0169SATURDAY 0.062691 0.104806 0.598164 0.5497 Variance Equation C 0.009615 0.001579 6.089796 0.0000RESID(-1)^2 0.062532 0.006109 10.23690 0.0000GARCH(-1) 0.930495 0.005651 164.6511 0.0000

Table XI Results of GARCH (1,1) Model with Dummy Variables in Non Trading Period Return Equation

Dependent Variable: Non Trading Period Return Method: ML - ARCH (Marquardt) - Normal distribution Sample (adjusted): 3 1804 Included observations: 1802 after adjustments Convergence achieved after 35 iterations Presample variance: backcast (parameter = 0.7) GARCH = C(8) + C(9)*RESID(-1)^2 + C(10)*GARCH(-1) Variable Coefficient Std. Error z-Statistic Prob.

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MONDAY 0.013703 0.007657 1.789679 0.0735TUESDAY -0.000837 0.008241 -0.101580 0.9191WEDNESDAY -0.001659 0.007046 -0.235438 0.8139THURSDAY 0.011841 0.009431 1.255457 0.2093FRIDAY 0.012321 0.005683 2.168150 0.0301SATURDAY 0.029269 0.005301 5.521213 0.0000Non Trading Period Return(-1) -0.091117 0.018611 -4.895889 0.0000 Variance Equation C 0.010691 0.000770 13.87952 0.0000RESID(-1)^2 0.942720 0.032904 28.65054 0.0000GARCH(-1) 0.386123 0.015935 24.23079 0.0000

After accounting for heteroskesdasticity, observations related to Tables IX, X and XI are:

For all the three return series, GARCH as well as ARCH term are significant indicating that volatility in case of gold return is time varying and persistent.

Daily return is negative on Thursday but insignificant and for other days it is positive; significant return is found only for Tuesday and Friday at 2.39% and

8.72% respectively.

Trading period return is negative for Monday and Thursday, but not significant for both the days. Significant positive return occurs on Tuesday and Friday.

Non trading period return is negative on Tuesday and Wednesday, though not significant. Significant positive return occurs on Monday, Friday and Saturday at

7.35%, 3.01% and 0% level of significance. One period lagged return for non

trading period has also significant impact on the return of non trading period.

Extreme Value Analysis: For analysing the impact of day of the week on extreme

values, day of the week for 180 extreme positive daily returns as well as 180 extreme

negative returns were observed and non-parametric chi- square test was applied. Results

for this exercise are presented in table XII.

Table XII Days for Extreme Values

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Day (Frequency) Positive Negative Monday 28 29 Tuesday 42 37 Wednesday 29 35 Thursday 37 45 Friday 36 30 Saturday 8 4 2 23.933 32.533 0.0000 0.0000

For stock market, negative return is the indication of declaration of bad news and positive

return is the indication of declaration of good news. Though same may not hold in

commodity return, but to some extant impact of the day of the week, on extreme positive

and extreme negative can be observed. In the present study it has been done with the help

of applying non-parametric chi square test. On observing the results given in Table XII, it

is clear on the first instance that extreme positive and extreme negative returns are

observed on Saturdays in minimum cases. Result of chi-square also confirm the day of

the week effect on extreme returns.

Conclusions and Discussion:

Present study is an attempt to investigate the day of the week effect on gold return of near

month futures contract, traded on biggest Indian commodity exchange, Multi Commodity

Exchange of India Limited (MCX). For the purpose of study, opening and closing prices

have been analysed with the help of dummy variables. Heteroskesdasticity was found to

be there in residuals of all the series after applying ordinary least square equation, and the

same was accounted for by applying GARCH (1,1) model. Results of the study indicate

that to some extent there is an impact of day of the week on daily return. When the daily

return was decomposed to trading period and non trading period return, more impact of

day of the week was found during non trading period than during trading period. Same

observation was made by Dyl, et al. 1986 for S&P 500. Here it is important to mention

19

that gold is traded on MCX from 10.00 a.m. to 11.30 p.m. from Mondays through

Fridays; and from 10.00 a.m. to 2.00 p.m. on Saturdays; and as discussed in methodology

part that for any day, impact of previous close to present open is considered in the study

as non trading period return. During the non trading period of MCX, markets of US are

open, so one of the probable reasons for the impact during non trading period may be the

spill over from US commodity market; and other reasons may be impact of volume and

open interest rather than the impact of a particular day of the week. The empirical

findings of some future research may through some light on the impact of these factors.

For designing a strategy of buying and selling, transaction cost is also to be considered by

the market players, but they can use the results of the present study for timing their

tradings.

Reference:

Agrawal, A. 1994. Anomalies or illusions? Evidence from Stock markets in eighteen countries. Journal of

International Money and Finance, 13: 083-106.

Aggarwal, R. and Lucey, B.M. 2007. Psychological barriers in gold prices? Review of Financial

Economics, 16: 217-230.

Aly, H., Mehdian, S. and Perry, M.J. 2004. An Analysis of Day-of-the-Week Effects in the Egyptian Stock

Market. International Journal of Business, 9(3): 301-308.

Ball, C.A., Torous, W.N. and Tschoegl, A.E. 1982. Gold and the Weekend Effect. The Journal of

Futures Markets,2(2): 175-182.

Berument, H. and Kiymaz, H. 2001. The Day of the Week Effect on Stock Market Volatility. Journal of

Economics and Finance, 25(2): 181-193.

Dyl, E.A. and Maberly, E.D. 1986. The Daily Distribution of Changes in the Price of Stock Index Futures.

The Journal of Futures Markets, 6(4): 513-521.

French, K.R. 1980. Stock Returns and the Weekend Effect. Journal of Financial Economics, 8:55-69.

20

Gay, G.D. and Kim, T.H. 1987. An Investigation into Seasonality in the Futures Market. The Journal of

Futures Markets,7(2): 169-181.

Gujarati, D.N. and Sangeetha. 2007. Basic Econometrics. New Delhi: Tata McGraw-Hill Publishing

Company Limited.

Kamath, R.R., Chakornpipat, R. and Chatrath, A. 1998. Return Distributions and the Day-of-the-Week

Effects in the Stock Exchange of Thailand. Journal of Economics and Finance, 22(2-3): 97-106.

Kaur, H. 2004. Time Varying Volatility in the Indian Stock Market. Vikalpa, 29(4): 25-42.

Lucey, B.M. and Tully, E. 2006. Seasonality, risk and return in daily COMEX gold and silver data 1982-

2002. Applied Financial Economics, 16: 319-333.

Ma, C.K. 1986. A Further Investigation of the Day-of-the-Week Effect in the Gold Market. The Journal of

Futures Markets, 6(3): 409-419.

Pandey, I.M. 2002. Retrieved from http://www.iimahd.ernet.in/assets/snippets/workingpaperpdf/2002-09-

08IMPandey.pdf on 05-12-2012.

Puja, P. 2010. Days-of-the-Week-Effect and Stock Return Volatility: Theory and Empirical Evidence.

Advances in Management, 3(4):14-21.

Rahman, M.L. 2009. Stock Market Anomaly: Day of the Week Effect in Dhaka Stock Exchange.

International Journal of Business and Management, 4(5):193-206.

Sarma, S.N. 2004. Stock Market Seasonality in an Emerging Market. Vikalpa, 29(3): 35-41.

Singhal, A. and Bahure, V. 2009.Weekend Effect of Stock Returns in the Indian Market. Great Lakes

Herald, 3(1): 12-22.

Suman, and Chahal, S.S. 2011. Day of the Week Effect on Stock Market Return and Volatility of India.

HSB Research Review, 2(2): 54-59.

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Documents

week effect

gold futures return

nontrading period return

different trading periods

daily return

different periods

arch effect

different days