weak form of market efficiency of metal commodities ...

Volume 3, Number 3, July – September‟ 2014 ISSN (Print):2279-0896, (Online):2279-090X PEZZOTTAITE JOURNALS

WEAK FORM OF MARKET EFFICIENCY OF METAL COMMODITIES IN INDIA Nikunj Patel17 Dr. Pankajray Patel18 ABSTRACT The aim of this paper is to examine the impact of Information Efficiency on Metal commodities namely Aluminium, Copper and Nickel. This study also examines the volatility in the prices of Metal commodities. Closing prices of metal commodity were taken for the period February 2003 to January 2013. The sample includes total 120 Monthly observations of individual metals for 10 years. Daily log return data were examined for information efficiency using descriptive statistics, Unit root test, Runs test, Autocorrelation Test and GARCH (1, 1). Copper is one of the high-risk return trade-off commodities with mean returns of 1.43 per cent and standard deviation of 7.60 per cent. The Runs test suggests Copper as weak form of inefficiency and Aluminium and Nickel hold weak form of efficiency during the period. Aluminium appears the significant positively autocorrected before lag 5 and significant negative auto correlated beyond lag 4. Copper also shows significant negative autocorrelation between lag 3 to 10 lag. However, the autocorrelations in Nickel disappeared after lag 7. The results also exemplify the existence of ARCH and GARCH effect claiming impact of volatility on Commodity returns. The volatility is persistent in returns and is less news sensitive. This study helps regulator for policy decisions. It also helps speculators to trade to earn abnormal returns, and later helps commodity market to become efficient in weak form. KEYWORDS Commodity Market, Multi-Commodity Exchange (MCX), Augmented Dickey-Fuller, Runs test, ARCH, GARCH etc. INTRODUCTION India has long history of commodity, where places and states are known to be producer of particular commodity. Commodities markets were underdeveloped in India. One of the major reasons is government intervention for the safeguard of farmers. Government still governs specific commodities to produce and distribute. Bombay Cotton Trade Association Ltd was started in 1875 as a first step toward organized trading, but due to introduction of MCX promoted by Financial Technologies Limited and NCDEX ICICI Bank, National Stock Exchange, National Bank for Agriculture and Rural Development and Life Insurance Corporation of India, the market has created platform largely for hedgers and speculators. Metals are used for domestic as well as intermediate purpose because of its strength, ability to conduct heat and electricity and its hardness. Metals are not generally used in its raw form but rather it is used in the mixture with non-metal constituents. Metals are divided based on its property in two different categories, Ferrous and non-ferrous metals. Table-1: World‟s Top-10 Mine Producing Countries (Thousand Metric Tons) Rank Copper Aluminium Nickel 1 Chile 3,357 China 12,900 Philippines 2 Peru 1,108 Russian Federation 3,815 Indonesia 3 China 940 Canada 3,030 Russia 4 Australia 833 Australia 1,943 Australia 5 United States 801 United States 1,727 Canada 6 Russian Federation 750 Brazil 1,536 Brazil 7 Indonesia 633 India 1,400 New Caledonia 8 Canada 607 Norway 1,130 China 9 Poland 429 United Arab Emirates 1,010 Colombia 10 Kazakhstan 420 Bahrain 870 Cuba Sources: Copper and Aluminium Data from www.indexmundi.com, Nickel Data from Statista

440 440 250 240 225 149 145 95 75 66

The table-1 reports the world‘s largest producer of Copper, Aluminium and Nickel. Chile is producing highest Copper, followed by Peru. China is the largest producer of Aluminium followed by Russian Federation. India is 7 th largest producer of Aluminium. India is not producing Nickel; it imports all requirement of Nickel from rest of the world. Philippines is the largest producer of Nickel. 17

Associate Professor, S. V. Institute of Management, Kadi Sarva Vishwavidyalaya, Gujarat, India, [email protected] I/c Director, GIDC Rajju Shroff ROFEL Institute of Management Studies (GRIMS), Gujarat Technological University, Gujarat, India, [email protected] 18

International Journal of Applied Financial Management Perspectives © Pezzottaite Journals.

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Table-2: Yearly Market Statistics of Aluminium, Copper and Nickel Aluminium Copper Volume Value OI Volume Value (In Lacs) (Rs Crores) (in lots) (In Lacs) (Rs Crores) 2009 4008 1844506 291655 139369 38203011 2010 18560 9303593 951235 312099 107065068 2011 15587 8710926 884158 338931 138272613 2012 27686 15005644 1861547 321828 137142784 2013 25272 13631072 1612052 196706 83559850 2014* 9805 5324307 701243 41867 17658067 Note: *Upto May 2014 Sources: SMC Global Securities Ltd Year

Nickel OI Volume Value (in lots) (In Lacs) (Rs Crores) 4995820 58340 12266145 10059029 178553 44825993 8894802 150641 39747638 12354977 152233 35759248 8498175 87212 19353263 2499173 41991 11110432

OI (in lots) 1950707 4744491 3600805 7521886 4361459 1295911

The table-2 reports Volume, trade value and open interest for all metal commodities from the year 2009 through May 2014. The volume in each of the commodities is volatile; either of Volume, Value or Open Interest does not show the clear trend in any of the commodity. However, the larger Volume persists in Copper. Moreover, the total value of contracts is larger in Nickel. Review of Literature on Weak Form of Market Efficiency The market efficiency is central concern to behavioral finance. Largely, the term efficiency is used to refer in which the relevant information is available and reflected in the market prices. The efficiencies are divided in to Operational efficiency and Informational efficiency. The information is the central point that drives the prices; this information is purely based on the expectation or evidences. The good information about the assets will drive prices up and vice versa. All past information reflects in the current prices of assets; the past prices do not drive the prices, which mean that the new information come across the market will drive the market to the upward or downward based on the magnitude of information towards the assets Fama (1965). There are plenty of information available in a market like company specific, industry specific, economic specific, political specific and so on. Nevertheless, not all of this information may possible to reflect in the current prices of assets. If it is so, then market will become efficient in real sense. However, it is hard to believe that every investor may have all of this information at a same point of time. This leads to deviation to the Market efficiency. The pace of adjustment to the prices with arrival of information plays significant role to become efficient market. If the pace of adjustment is slow, every investor buys stock and hold for a shorter period when prices move up. However, when every investor start doing such things, the adjustment will become faster and later market become efficient. Taylor (1980) examined random walk hypothesis for the period 1966-1978 for copper spot prices and for zinc for the period 19701978. He has rejected the random walk hypothesis for all base metals. Since the theoretical framework developed by Fama (1965, 1970), there is common consensus among market participants that the stock returns exhibit random walk behaviour, which means past prices do not add value in prediction of existing price to enable outperforming the market, this means "markets have no memory" (Brealey and Myers, 2003). In strongest form of market efficiency, there should be no cost of information (Fama, 1970). This can be criticized because there are some industry who sales information, that means there are associated cost of accessing information. The study continues with same phenomena to observe efficiency in the commodity prices. It is found from the empirical literature that very few studies have been carried on Metal Commodities. As more studies have been carried on Agricultural Products like (Naik and Jain, 2002; Raipuria, 2002; Kolamkar, 2003; Thomas, 2003; Nair, 2004; Kabra, 2007; Ramaswami and Singh, 2007; Sabnavis and Jain, 2007; Roy, 2008; Dey, 2009; Ghosh 2009a, 2009b, 2010a; Dey and Maitra, 2011). Table-3: Summary of Literature Review Sr. No. 1.

2.

Study Castelino (1992)

Market Under study Wheat and corn

Period of Study 19831985

Methodology Used Regression Analysis

Lucey (2004)

London Metal Exchange

19892002

OLS statistical, Kruskal-Wallis Test, KolmogorovSmirnov test,

Results found Minimum Variance hedging for T-Bills and Eurodollars contracts to minimize risk. However, the author did not find the scope of minimizing contracts for commodities. The extent of daily seasonality in the higher moments. The homogeneity of variance allows the use of Turkey‘s HSD test for examination of exactly which days differ in their means.


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3.

Kenourgios & Samitas (2004)

London Metal Exchange

19892000

Unit Roots Tests, Engle-Granger Unit Roots Tests, Johansen tests for Co integration,

4.

Karande (2006)

Castor Seed futures market

19851999

Regression Analysis

5.

Cheung and Morin (2007)

Bank of Canada Commodity Price Index (BCPI)

19802006

Co integration Test, Engle-Granger test

6.

Bose (2008)

India

20052007

Correlation analysis, Co integration Analysis,

7.

Korniotis (2009)

19922008

Regression

8.

Goyari and Jena (2011)

Standard and Poor‘s Goldman SachsCommodity Index (SPGSCI) India

20052008

Co-integration test

9.

Jabir and Gupta (2011)

India

20042007

Co-integration test

10.

Kumar and Pandey (2011)

India

20042008

Johansen Co integration Test, Weak Exogeneity Test, Short Run Co integration, Variance Decomposition, Volatility S pillover: A BEKK Model Approach

11.

Inoue and Hamori (2012)

India

20062011

12.

Malhotra (2012)

World Commodity

19702011

Johansen Co-integration Test, Dynamic OLS and fully modified OLS Correlation

The unit root tests results indicate that the spot prices and the fifteen months futures prices are not co integrated. The existence of Co-integration between the copper spot prices and the three months futures prices, using both the Engle-Granger and Johansen tests, confirms the first necessary condition for long-term market efficiency. Basis risk as indicated by RMSE is lower for June contract as all information regarding supply of castor seed is available much before trading begins. Incorporating variable lowers the ADF test statistic from the Engle-Granger test to a level that no longer supports the existence of co integration, suggesting that emerging Asia did not influence long-run metals prices over this period. Correlation analysis implies strong relationship between the price series, and provides preliminary evidence that series respond similarly to changes in market fundamentals. Co integration test found that for the daily multi commodity indices, there was a clear bi-directional lead-lag relationship, showing that both markets assimilate new information and contribute to price discovery. The author looks to see whether physical hoarding is correlated with price growth with the argument that speculation can only influence spot markets. The spot price and the futures price are co integrated for three commodities, suggesting that they have a long-run relationship. As the results from their co-integration and causality tests, they indicate that cointegration exists in these indices for all commodities except wheat and rice and that the direction of causality is mixed, depending on the commodities. The results of long run relationship between Indian futures prices and their world counterparts indicate that the Indian markets are co-integrated with the world markets. The weak exogeneity test indicates that for most of the commodities of Indian futures prices adjust to any discrepancy from long run equilibrium whereas the world prices are exogenous to the system. The Granger Causality test indicates existence of oneway causality from world markets to Indian market in most of the commodities. Efficient co-integrating relationship found between indices and commodity futures market only during the sub-sample period since July 2009. Commodity Futures have been seen to exhibit negative correlation with stock futures and bonds & positive correlation with inflation.


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Sehgal, Rajput

13.

and Dua (2012)

Chana, Guar Seeds, Soya Bean, Kapas, Potato Agra, Turmeric, Black Pepper, Barley, Maize and Castor Seeds

20032012

Unit Root, Co-integration test, Granger Causality Test

Price discovery is confirmed for all commodities except Turmeric. Price discovery results are encouraging given the nascent character of commodity market in India. However the market does not seem to be competitive.

Sources: Authors Compilation OBJECTIVES OF RESEARCH Primary Objective 

The aim of this research is to study the impact of Information Efficiency on selected Metal Commodities.

Secondary Objective   

To study pattern in return among three metal commodities. To investigate whether the three metal commodities follow Random Walk. To find out dependency of information efficiency on metal commodities.

STATEMENT OF PROBLEM To find out the impact of Information Efficiency on selected Metal commodities. A careful survey of the existing literature reveals conflicting evidence on weak-form market efficiency for metal prices, depending on which test a particular study used & which type of data the researcher employed. Degree of market inefficiency determines investor's effort to gather and trade on information. So it has become necessary to carry on this study. METHODOLOGY OF RESEARCH For studying the objectives, sample consists of the Monthly closing prices of the selected metal commodities of Copper, Aluminium and Nickel. Closing prices of metal commodity under study were taken for the period February 2003 to January 2013. The sample includes total 120 Monthly observations of individual metals for 10 years. The data was collected from www.indexmundi.com. DATA ANALYSIS AND INTERPRETATION Analysis of Descriptive Statistics Table-4: Results Descriptive Statistics for the selected Metals Returns Aluminium Copper 0.43% 1.43% Mean 1.04% 1.64% Median 13.70% 24.00% Maximum -21.80% -28.88% Minimum 5.34% 7.60% Std. Dev. 12.42 5.31 CV -0.61979 -0.70937 Skewness 5.050825 6.55719 Kurtosis 28.71233 73.33205 Jarque-Bera 0.000* 0.000* Probability 51.52342 171.0199 Sum 3390.035 6875.491 Sum Sq. Dev. 120 120 Observations Sources: Authors Compilation

Nickel 0.75% 0.65% 24.93% -31.59% 9.38% 12.51 -0.28014 3.643773 3.6418 0.16188 90.33407 10466.48 120

During the period from February 2003 to January 2013, metal commodities shows positive average monthly returns, the highest monthly return comes from the Copper (1.42) and Nickel (0.75). The lowest monthly return observes in Aluminium (0.42). At the same time, Copper is showing 1.64 median, which is moving in positively, and showing good sign for return whereas Aluminium do not indicate and shows 5.33 volatility which is less than the other metals. Nickel shows 9.37 volatility and Copper at 7.60.


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Statistical properties reveal the fact that the monthly time series returns is not normally distributed except Nickel. The lowest returns witness in Aluminium of 0.43%, followed by Copper of 1.43%. The highest SD observes in Nickel followed by Copper. Nickel is most volatile commodity with SD of 9.38%. The highest range observes in Nickel. The higher value of Co-efficient of Variation in Nickel and Aluminium indicates more SD with lower monthly returns. The values of skewness shows that all the return distributions are skewed, indicating departure from normality along with value of Kurtosis, which indicates fat tails against normal distribution. Thus, the values of Skewness and Kurtosis suggest that the daily returns series are not normally distributed. All commodities witness negatively skewed, the result is consistent with Patel et al. (2011) and Patel et al. (2012) who studied Asian equity Markets. Return in all commodities observes leptokurtic distribution as they have kurtosis value of higher than 3. Jarque-Bera (JB) test1 applies on the monthly returns to check the normality in distribution. Jarque-Bera (JB) test fails to accept the null hypothesis of normal distribution at the 1% level of significance all commodities except Nickel. Therefore, none of these monthly returns series is then well approximated by normal distribution except Nickel. It means the non-normal frequency distributions of the commodities returns deviate from the prior condition of random walk model. Unit root test Unit root test applies to check the stationarity in time series. The absolute commodity prices create problem of unit root, which is the first observation to the random walk (Gujarati, 2004). Therefore, all data series transforms to the logarithm returns well in advance. The benefit of converting in to logarithm returns is that these time series will produce returns time series that are differenced. The presence of a ―random walk‖ implies no possibility of modeling the prices series‘ trajectory; this indicates the current prices cannot be predicted based on the historical prices. However, if the prices are predicted, there will be chance to earn abnormal profits. Thus, these returns on prices are informationally inefficient in the weak form. Dickey and Fuller (1981) test used to examine the stationarity of the time series. A series with unit root is said to be non-stationary indicating non-random walk. The standard Dickey-Fuller (DF) test is appropriate for a series generated by an autoregressive process of order one, AR (1). Augmented Dickey-Fuller (ADF) test (Dickey and Fuller, 1981) applied to assay the existence of unit root in the time series of price return. Majorly it helps to check the stationarity in the time series. Augmented Dickey-Fuller (ADF) test j

Rt b0  b1   0 Rt 1   i Rit-1   t t=1

where Rt is the variable being tested for unit roots, b denotes the regression coefficients, and

t

represents the random error term,

which is normally distributed with a mean of 0 and variance  . The t-test statistic for the null hypothesis is H0: b = 1 is (b-1) / s(b), where s(b) is the standard error of regression coefficient b. Using the null hypothesis that b = 0 versus the alternative of b < 0 for any Rt. 2

Table-5: Results of Augmented Dickey-Fuller for Metals under Study Aluminium Copper Nickel -8.249679 -6.89686 -7.920111 ADF 0.0000* 0.0000* 0.0000* p-value Note: The t- statistics critical value at 1%, 5% and 10% are -3.43288, -2.86254, and -2.56735 respectively * indicate 1% level of significance Sources: Authors Compilation The unit root tests reveals that time series understudy are non-stationary in its absolute indices; however, transforming into logarithm returns, it becomes stationary. This fulfils the condition of AR (1) Condition. The null hypothesis of a unit root for ADF rejects at the 1% level of significance. This indicates that all index levels are stationary and integrated of order one. The result therefore indicates that there exists some evidence of random walks in time series. However, the existence of random walk components in commodity price does not necessarily imply that returns are unpredictable. If a white noise process characterizes returns, the corresponding indices deviates a random walk. In that case, the returns are unpredictable. Based solely on unit root tests may not imply that markets understudy is weak form efficient, since the ADF and PP unit root test only examined the existence of stochastic trend components, but does not detect the predictability in returns. The results of ADF and PP were consistent with earlier evidences unanimously. ______________________________ 2 Jarque-Bera statistics (JB) tests whether a series is normally distributed. The statistics is given by: JB = n S2  (K  3)  , Where S

1

6

4



is Skewness and K is Kurtosis. International Journal of Applied Financial Management Perspectives © Pezzottaite Journals.

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Runs Test The Jarque-Bera (JB) test rejects the null hypothesis that series for returns are normally distributed. Therefore, a parametric serial correlation test is inappropriate and replaces with a nonparametric runs test (Abraham et al., 2002). Runs test (also called WaldWolfowitz test) is a non-parametric test, designs to examine whether successive price changes are independent (Patel et al., 2012). The non-parametric runs test is applicable as a test of randomness for the sequence of returns. For instance, when returns increase (decrease) sequentially three times in a row, the runs test treats them as +++ (- - -). Once this sequence is broken with a decrease or no change in returns, and then a new runs count begins again. A sample with too many or too few runs suggests that the sample is not random. In fact, too few runs would suggest a time trend or a systematic arrangement due to temporal dependence while too many runs would suggest cyclical or seasonal fluctuations or clustering. The randomness of a particular series assesses after an analysis of the distribution of the duration of specific runs. Furthermore, this test is very useful, as it does not require series to be normally distributed. Accordingly, it tests whether returns in market indices are predictable. The null hypothesis for this test is for temporal independence in the series (or weak-form efficiency); in this perspective, this hypothesis is tested by observation the number of runs or the sequence of successive price changes with the same sign i.e. positive, zero or negative. Each change in return classifies according to its position with respect to the mean return. Hereby, it is a positive change when return is greater than the mean, a negative change when the return is less than the mean and zero when the return equals to the mean (Guidi, Gupta, & Maheshwari, 2010). The runs carried out by comparing the actual runs to the expected number of runs. Table-6: Results of the Runs Test Copper Aluminium Nickel K=Mean 0.014275 0.004308 0.007492 Cases < K 57 56 60 Cases >= K 63 64 60 Total Cases 120 120 120 Number of Runs 44 62 52 Z- Statistics -3.097 0.233 -1.650 p-value 0.002* 0.816 0.099*** Notes: If the Z-statistic is greater than or equal to ± 1.96, then we cannot be accepted null hypothesis at 5% level of significance. * Indicates rejection of the null hypothesis that successive price changes are independent. **indicate 1 % level of significance ***indicate 10 % level of significance Sources: Authors Compilation As pointed out by Guidi et al. (2010), positive (negative) Z values obtain when actual number of runs exceed (fall below) the expected runs. A negative Z value indicates a positive serial correlation, whereas a positive Z value indicates a negative serial correlation. The positive serial correlation implies that there is a positive dependence of prices, which indicates a violation of random walk. Since the distribution Z is N (0, 1), the critical value of Z at the five percent significance level is ±1.96. The results of Runs test for the returns on metals are indicating in the table - 6. The runs test clearly shows that the successive returns for all metals except the Aluminium and Nickel, are not independent at 1% and 5% level of significance (significance value of ±1.96) and the null hypothesis of return independence cannot be accepted in Copper which indicated that Copper are inefficient in weak form. Moreover, Aluminium and Nickel, we cannot reject null hypothesis, concluded that both metals are efficient and follow random walk so investor cannot predicted the market returns. Auto-Correlation Test The relationship between the time series and its own values at different lags examine the value of autocorrelation. The term autocorrelation defines as ―correlation between members of series of observations ordered in time‖ (Gujarati, 2004). Negative autocorrelation in the time series indicate mean reversal. To determine whether the returns in the market exhibit serial dependence, the autocorrelation test developed by Ljung and Box (1978) was used. Campbell, Lo & MacKinlay (1997) point out that the autocorrelation coefficient is a natural time series extension of the coefficient of correlation between random variables x and y. Ljung and Box (1978) introduce the finite-sample correction that yields a better fit to the χ2 distribution for small sample sizes: k

QLjung-Box n  n     t 

  t  n t


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Where 

 t  an estimated autocorrelation coefficients and t is is a given lag. Table-7: Results of Autocorrelation and Q-Statistics for Returns Lag

Aluminium

Copper

0.263 0.422 (8.4827) (21.889) 0.004* 0.000* 0.093 0.146 2 (9.5556) (24.52) 0.008* 0.000* 0.13 -0.004 3 (11.672) (24.522) 0.009* 0.000* 0.194 -0.033 4 (16.398) (24.658) 0.003* 0.000* -0.055 -0.07 5 (16.781) (25.277) 0.005* 0.000* -0.257 -0.168 6 (25.251) (28.888) 0.000* 0.000* Note: *Significant at 1% level; **Significant at 5% level; ***Significant at 10% level Sources: Authors Compilation 1

Nickel

Lag

Aluminium

Copper

Nickel

0.303 (11.33) 0.001* 0.074 (12.001) 0.002* -0.007 (12.006) 0.007* 0.07 (12.624) 0.013** 0.017 (12.659) 0.027** 0 (12.659) 0.049**

7

-0.229 (32.03) 0.000* -0.174 (35.978) 0.000* -0.162 (39.452) 0.000* -0.267 (48.947) 0.000* -0.143 (51.712) 0.000* -0.044 (51.978) 0.000*

-0.137 (31.315) 0.000* -0.276 (41.295) 0.000* -0.211 (47.179) 0.000* -0.05 (47.513) 0.000* 0.182 (51.977) 0.000* 0.119 (53.891) 0.000*

-0.005 (12.662) 0.081*** -0.037 (12.845) 0.117 -0.091 (13.937) 0.125 0.011 (13.954) 0.175 0.055 (14.357) 0.214 -0.061 (14.855) 0.249

8

9

10

11

12

Table-7 provides the results of the sample autocorrelation coefficients, the Ljung-box statistics (in Parentheses) and p-value for the monthly returns of Aluminium, Copper and Nickel metals. To test the Random walk hypothesis for the selected metals under study, autocorrelation tests up to 12 lags was performed for monthly commodity returns. Positive autocorrelation indicates predictability of returns in short period, which is general evidence against market efficiency, whereas negative autocorrelation indicate mean reversion in returns. Aluminium appears the significant positively autocorrected before lag 5 and significant negative auto correlated beyond lag 4. Copper also shows significant negative autocorrelation between lag 3 to 10 lag. However, the autocorrelations in Nickel disappeared after lag 7. Thus, it gave general evidences of predicted prices of commodities, which is against the weak form of efficiency. GARCH (1, 1) ARCH (Autoregressive Conditional Heteroscedasticity) and GARCH (Generalized Autoregressive Conditional Heteroscedasticity) are the most popular models used by most of the researchers to analyze volatility in financial time series. The ARCH model was introduced by Engle (1982) and GARCH was introduced by Bollerslev (1986). GARCH is known to be the best predictor of the variance in the next period, it is a weighted average of the long run average variance, the variance predicted for this period, and the new information in this period that is captured by the most recent squared residual (Engle, 2001). The GARCH (1, 1) model is run on Gaussian processes, which is one of the popular and frequently used methods for nonparametric regression. Gaussian processes are defined by its mean and covariance functions. The GARCH (1, 1) indicates number of autoregressive lags (or ARCH term) and Moving average of lags (or GARCH term). It is assumed that the observations are normally distributed. It is further be noted that ( sense if the weights are positive, requiring 

  )  1 , and it only really makes

 1 ,   1 and   1 .

The basic GARCH (1, 1) model can be expressed as:

Mean Equation

Rt     t

Variance Equation

 2     t21   t21


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Where  t21 is the news about volatility from the previous period and  t21 is the last period forecast variance. (   ) being close to one shows high persistence in volatility clustering and implies inefficiency of the market. Table-8: Results GARCH (1, 1) Aluminium 240

Aluminium Mean Equation C Variance Equation



( C)

 (ARCH (1))  (GARCH (1)) ( +  ) R-squared DW stat

Statistics 200

0.631 (1.732) 1.676 (1.059) 0.272 (2.141)** 0.698 (5.921)* 0.970 -0.001 1.468

160

120

80

40

0 2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2010

2011

2012

2010

2011

2012

Conditional variance

Copper 350

Copper Mean Equation C Variance Equation



( C)

 (ARCH (1))  (GARCH (1)) ( +  )

Statistics

300

1.475 (1.87)***

250 200

6.835 (1.808)*** 0.206 (2.445)**

150 100

0.686 (5.633)* 50

0.892 0.000 1.156

R-squared DW stat

0 2003

2004

2005

2006

2007

2008

2009


Nickel 240

Nickel Mean Equation C Variance Equation



( C)

 (ARCH (1))  (GARCH (1)) ( +  ) R-squared DW stat

Statistics 200

0.773 (0.976) 160

3.535 (0.892) 0.129 (1.702)*** 0.838 (11.258)* 0.967 0.000 1.390

120

80

40

0 2003

2004

2005

2006

2007

2008

2009


Note: *Significant at 1% level; **Significant at 5% level; ***Significant at 10% level Sources: Authors Compilation From figure, it has been observed that Aluminium and Copper became more volatile in the period 2008-09; this may be because of international crash in equity markets around the globe. However, the general observation indicates that all metal prices provide evidences of volatility, which is against the market efficiency.


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Table-8 reports the parameter estimates of all conditional volatility (GARCH) model. A typical pattern is observed for commodity returns where the coefficients on all three terms in the conditional variance equation are statistical significant, with a small value for the variance intercept term C, a somewhat moderate ARCH term, and larger GARCH term in most of the cases. This indicates persistency in returns and less news sensitivity. The ARCH term represents the lagged squared error, while the GARCH term represents the lagged conditional variance. For all commodities returns, the sum of ARCH and GARCH coefficients is very close to one, indicating that volatility shocks are quite persistent. The coefficient of the lagged squared returns is positive and statistically significant for most specifications. This is an indication for strong GARCH effects in all commodities returns. In addition, the coefficient of lagged conditional variance is significantly positive and less than one, indicating that the impact of ‗old‘ news on volatility is significant. It is also noted that the GARCH coefficient is greater than ARCH coefficient, it represents that conditional variance is more dependent on last period's forecast variance. Moreover, it was also observed that the value of Dubin Watson statistics less than 2 that portrays very low level of autocorrelation among the residuals. It is observed that the Z-statistics (in Parentheses) is positive which suggests that the returns of commodities are more than the mean returns. The results exemplify the existence of ARCH and GARCH effect claiming impact of volatility on Commodity returns. CONCLUSION The study provides the evidence of weak form of efficiency of the selected metals. The overall results from the empirical analysis suggest that the metals under study are weak-form efficiency. To verify the normality of the data Jarque-Bera test was performed and visualized the skewness and kurtosis. Copper is one of the high-risk return tradeoff commodities with mean returns of 1.43 per cent and standard deviation of 7.60 per cent. The Runs test suggests Copper as weak form of inefficiency and Aluminium and Nickel hold weak form of efficiency. To verify the weak form of efficiency of selected metals like Copper, Aluminium, Nickel, Unit Root test and Auto-correlation tests were applied. No autocorrelation was found in Aluminium and Copper. Nickel was auto correlated at lag 7 to 23. From the study, it is also found the volatility is persistent in returns and less news sensitive. The results exemplify the existence of ARCH and GARCH effect claiming impact of volatility on Commodity returns. REFERENCES 1.

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