Nikunjkumar Patel
Prof. Nikunj Patel holds MBA in Finance from the HNGU, Patan. He has almost thirteen years of standing in his academic career. Currently, he is working with Institute of Management, Nirma University, Ahmedabad, His areas of teaching and research include Accounting, Financial Management, Security Analysis and Portfolio Management, Behavioural Finance and International Finance. He has also acted as a resource person in several faculty development and Management development programmes.
Phone: +91-9825674507
Address: A/6, Akshardham Township,
Near Sahajanand School,
Visnagar - 384315,
Dist. Mehsana,
Gujarat, India
Phone: +91-9825674507
Address: A/6, Akshardham Township,
Near Sahajanand School,
Visnagar - 384315,
Dist. Mehsana,
Gujarat, India
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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.
August 2001 to July 2004. But thereafter, market became random walk because no significant autocorrelation found after 2004. Runs Test conclude that the whole period null hypothesis of random walk was not accepted. In all months, null hypothesis is accepted of random walk except January month. But in all days random walk is prevailing. The period of 2004 to 2010 support weak form Market Efficiency."
DATA/PERIOD AND METHODOLOGY– The data were collected from finance.yahoo.com. The data includes daily adjusted closing index prices of Indian Stock market (BSE Sensex) and 10 other major developed and emerging stock markets. We have taken sample period of daily data from July 1997 to Dec 2009. We have also divided the data in two sub-periods, Period-I is ranging From July 1997 to September 2003 and Period –II is ranging from October 2003 to December 2009. We have used logarithm transformed stock price indices to neutralize their returns.
STATISTICAL TOOLS USED – Daily log return data are examined for co-movement and interdependence using descriptive statistics, correlation among the major indices, Unit Root Test/ Stationary test, and Granger causality test.
FINDINGS–The SENSEX has given highest Risk adjusted return for the whole period followed by BVSP, whereas Nikkei has given negative Risk adjusted return for the same period. It has been observed that SENSEX has highest correlation with BVSP (98%) among all the pairs. With the help of bi-variate granger causality test, it is revealed that SENSEX is affected by HANGSENG, STI, DJIA, FTSE and DAX. So we can interpret that SENSEX is interdependent on Developed countries stock markets except NIKKEI. We can also see that SENSEX causes SCI, BVSP NIKKEI, KOSPI and AORD. It means that these markets are interdependent on stock price movement in SENSEX.
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.
August 2001 to July 2004. But thereafter, market became random walk because no significant autocorrelation found after 2004. Runs Test conclude that the whole period null hypothesis of random walk was not accepted. In all months, null hypothesis is accepted of random walk except January month. But in all days random walk is prevailing. The period of 2004 to 2010 support weak form Market Efficiency."
DATA/PERIOD AND METHODOLOGY– The data were collected from finance.yahoo.com. The data includes daily adjusted closing index prices of Indian Stock market (BSE Sensex) and 10 other major developed and emerging stock markets. We have taken sample period of daily data from July 1997 to Dec 2009. We have also divided the data in two sub-periods, Period-I is ranging From July 1997 to September 2003 and Period –II is ranging from October 2003 to December 2009. We have used logarithm transformed stock price indices to neutralize their returns.
STATISTICAL TOOLS USED – Daily log return data are examined for co-movement and interdependence using descriptive statistics, correlation among the major indices, Unit Root Test/ Stationary test, and Granger causality test.
FINDINGS–The SENSEX has given highest Risk adjusted return for the whole period followed by BVSP, whereas Nikkei has given negative Risk adjusted return for the same period. It has been observed that SENSEX has highest correlation with BVSP (98%) among all the pairs. With the help of bi-variate granger causality test, it is revealed that SENSEX is affected by HANGSENG, STI, DJIA, FTSE and DAX. So we can interpret that SENSEX is interdependent on Developed countries stock markets except NIKKEI. We can also see that SENSEX causes SCI, BVSP NIKKEI, KOSPI and AORD. It means that these markets are interdependent on stock price movement in SENSEX.