lead lag relation

Description
Lead Lag relation

FINAL REPORT- MRP
FINANCIAL RISK MANAGEMENT IN CAPITAL AND COMMODITY MARKET

SUBMITTED TO: Prof. C. Padmavathi SUBMITTED BY: Bhanu Pratap Singh

2/3/2011

IBS HYDERABAD

CONTENTS
INTRODUCTION: INDIAN STOCK MARKET& COMMODITY MARKET ...................................................................5 STOCK MARKET ............................................................................................................................................................................5 a) Primary Market:.......................................................................................................................................................................5 b) Secondary Market ...................................................................................................................................................................6 COMMODITY MARKET....................................................................................................................................................................7 ISSUES AND CHALLENGES CURRENTLY BEING FACED BY THE MARKET .........................................................8 LIQUIDITY: .......................................................................................................................................................................................8 LACK OF INFRASTRUCTURE ..................................................................................................................................................9 SCAMS .................................................................................................................................................................................................9 GOVERNMENT INTERVENTION ...........................................................................................................................................9 DEPENDENCE ON FOREIGN CAPITAL ............................................................................................................................10 LACK OF CO-ORDINATION ...................................................................................................................................................10 METHODOLOGY TO REDUCE THE RISK .............................................................................................................................10 OBJECTIVE ..........................................................................................................................................................................................12 LITERATURE REVIEW..................................................................................................................................................................12 METHODOLOGY...............................................................................................................................................................................14 DATA .....................................................................................................................................................................................................14 RESULTS ..............................................................................................................................................................................................15 CONCLUSION.....................................................................................................................................................................................21 FINANCIAL RISK MANAGEMENT IN COMMODITIES MARKET ..............................................................................22 LITERATURE REVIEW..................................................................................................................................................................22 OBJECTIVE ..........................................................................................................................................................................................25 Page 2 of 55

METHODOLOGY...............................................................................................................................................................................25 HYPOTHESIS ................................................................................................................................................................................25 DATA .....................................................................................................................................................................................................27 TOOLS USED ......................................................................................................................................................................................28 LIMITATION OF THE STUDY ....................................................................................................................................................29 EMPIRICAL RESULTS AND DISCUSSION ............................................................................................................................30 VOLATILITY OF SPOT MARKET .........................................................................................................................................30 EFFECT OF INTRODUCTION OF FUTURES ON SPOT MARKET .........................................................................31 REASONS FOR LEAD-LAG BETWEEN SPOT AND FUTURES PRICES ..............................................................34 CROSS CORRELATION COEFFICIENT FOR GOLD ................................................................................................35 CROSS CORRELATION COEFFICIENT FOR ALUMINIUM .................................................................................36 CROSS CORRELATION COEFFICIENT FOR ZINC ..................................................................................................37 LEAD LAG RELATIONSHIP....................................................................................................................................................38 LEAD LAG RELATIONSHIP FOR GOLD.......................................................................................................................38 LEAD LAG RELATIONSHIP FOR ALUMINIUM .......................................................................................................41 LEAD LAG RELATIONSHIP FOR ZINC ........................................................................................................................44 FINDINGS ............................................................................................................................................................................................48 VOLATILITY OF SPOT MARKET .........................................................................................................................................48 LEAD LAG RELATIONSHIP....................................................................................................................................................49 CROSS CORRELATIONSHIP FUNCTION ....................................................................................................................49 LEAD LAG RELATIONSHIP FOR GOLD (24 MONTHS) .......................................................................................50 LEAD LAG RELATIONSHIP FOR GOLD (30 MONTHS) .......................................................................................50 LEAD LAG RELATIONSHIP FOR ALUMINIUM (75 DAYS) ................................................................................50 LEAD LAG RELATIONSHIP FOR ALUMINIUM (90 DAYS) ................................................................................50 LEAD LAG RELATIONSHIP FOR ZINC (75 DAYS).................................................................................................51 Page 3 of 55

LEAD LAG RELATIONSHIP FOR ZINC (90 DAYS).................................................................................................51 CONCLUSION AND IMPLICATION ..........................................................................................................................................52 SUGGESTIONS ...................................................................................................................................................................................54 REFERENCES.....................................................................................................................................................................................55

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INTRODUCTION: INDIAN STOCK MARKET& COMMODITY MARKET
Indian stock and commodity market are the two most important Asset markets where in the Investors Park in their money in order to earn returns from the markets. As we all know the return is always backed by a term called Risk those who are investing in these respective markets have to face this term and sometime investor lose their hard earn money just because they are not aware of the Risk in the market.

STOCK MARKET
The Indian stock market is divided into two of the following Markets:

a) Primary Market:
The sale of stock by a private company to the public. IPOs are often issued by smaller, younger companies seeking the capital to expand, but can also be done by large privately owned companies looking to become publicly traded. In this market all types of investor divert their funds in order to subscribe to the issue, and the allocation of security is on the basis of pro-rata basis.

IPO market has faced lots of turbulences post liberalization and they are the following:

i.

As the economy opened up for the entry of more players in the market there were many fake companies who have entered the primary market in order to raise capital but the intension was to take all the money moped without even listing the company on the exchanges.

ii.

In the year 2006 IPO market was taken by surprise when shares of companies, which were going for public issues, and listing at substantial premium to their respective offer prices, unscrupulous investor name Roopalben Panchal trying

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to bypass the rules, applying via multiple applications, to get allotment of large number of shares1.

iii.

The small investor are always at the mercy of big investor because in the IPO market the share allocated to the retail investors is taken away by High Net-worth individuals through manipulation in the books.

Thus, the above are the issues and challenges faced by the primary market.

b) Secondary Market
A newly issued IPO will be considered a primary market trade when the shares are first purchased by investors directly from the underwriting investment bank; after that any shares traded will be on the secondary market, between investors themselves. In the primary market prices are often set beforehand, whereas in the secondary market only basic forces like supply and demand determine the price of the security 2. In the Indian secondary market the average daily turnover is around 40000 crore 3 with more than 3000 securities listed on the exchanges. The secondary market has two most active indices and they are SENSEX & NIFTY. It is to be noted that Sensex which is the oldest stock exchange in Asia and Nifty which have came into existence in the year 2001 have faced many issues and challenges like the following:

i.

The secondary market till the year 2000 followed open out-cry ( were in the buy & sell of securities was done by the people shouting on the floor of the exchange ) but later on the open out-cry was transformed into electronic trading , but still the problem with the infrastructure is major problem.

ii.

Even today the problem of Insider Ownership, Corporate Governance and Corporate Performance prevails in the market.

1
http://www.articlearchives.com/trends-events/investigations/1849033-1.html

2
http://www.investopedia.com/terms/s/secondarymarket.asp
Article from the Economic Times

3

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iii. iv. v.

Price volatility both in the spot as well as in the future market which leads to the erosion of investor wealth. As we have seen in the past the scandals which lead to the fall of the market and financial loss to the players in the market. Even the promoters of the company in order to inflate the price of the stock adopt some unethical measures like diverting their own funds in order to buy the stock thus creating artificial rise in the share tempting other investor to invest and after some time see the stock prices touching lows and reducing the portfolio value.

Thus, the above are the issues and challenges faced by the primary market.

COMMODITY MARKET
Commodity markets play a vital role in the growth of an economy, mainly in the economies because of that depends largely on the agricultural sector. Indian economy gains considerably commodity market, as most of the Indians population (around 60%) is dependent

on the agricultural sector4. In India the commodity exchange are still in a nascent stage and there are many hindrances markets are in the expansion of the market. The challenges faced by the Indian commodity very serious in nature and could destroy the Indian futures markets. Trading stipulation and contracts stipulation play a vital role in the growth of any market. The regulator does not allow the trading in some commodities thus the price discoveries are not made properly.

i.

ii.

Apart from large exchanges like National Commodity Derivatives Exchanges (NCDEX) and Multi-commodity Exchange (MCX) there are many exchanges in India where there is lack of infrastructure facilities like clearing house, warehousing, and modern trading. Therefore, the turnover is too low, as the exchanges have to depend on a few commodities.

4

Commodity Market – Arindam Banarjee
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iii.

One of the major obstacles faced in commodity market in India is the warehousing facilities. There are few warehouses which cater to the need of the market.

iv.

Another major challenge is the number of exchanges itself. Among the 25 commodity exchanges functioning in India, the majority of them specialize in trading only in some commodities.

Thou the commodity market has shown growth in recent past but still the Risk specially financial risk prevails in the exchanges and the study will be focused as how to manage and minimize the risk.

ISSUES AND CHALLENGES CURRENTLY BEING FACED BY THE MARKET
The Indian markets both the capital and the commodity market have seen various types of reform taking place in the system in order to safeguard the interest of the various types of investors in the market. The controlling bodies have taken various steps in order to bring the Indian stock market at par with the global standards. Even after the reforms taken by the controlling bodies in the respective markets various types of issues still prevails and thus the Risk quotient of the player in the market increases subsequently.

LIQUIDITY:
One of the major problems being faced by the market is lack of liquidity in the market and this issue has been prevalent in the Indian context even before and
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after liberalization. The market have seen stocks and commodity not traded and thus the holder of that asset class faces the Financial Risk as the amount invested by the investor gets locked leading to a fall in the portfolio value .

LACK OF INFRASTRUCTURE
One of the major problem being faced till date by the market is of lack of proper infrastructure which adds to the increase in Risk for the investors in the market. Apart from large exchanges like National Commodity Derivatives Exchanges (NCDEX) and Multi-commodity Exchange (MCX) there are many exchanges in India where there is lack of infrastructure facilities like clearing house, warehousing, and modern trading. Therefore, the turnover is too low, as the exchanges have to depend on a few commodities.

SCAMS
The Indian markets have faced the problem of manipulation and scams even before the liberalization took place in the Indian economy. A classic example is the 1992 historical scam which shook the market as the investors saw their

wealth invested in the stock market taking a nose dive. This, incident was followed by Ketan Parekh who continuously rigged the stock market for his own benefit and the consequences were the same. Even the promoters of the company in order to inflate the price of the stock adopt some unethical measures like diverting their own funds in order to buy the stock thus creating artificial rise in the share tempting other investor to invest and after some time see the stock prices touching lows and reducing the portfolio value.

GOVERNMENT INTERVENTION
The policy set by the government in both the markets does not allow the trading to take place smoothly. Trading stipulation and contracts stipulation play a vital role in the growth of any market. The regulator does not allow the trading in some commodities thus the price discoveries are not made properly. Thus, if the price is
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not determined properly then in that case it will lead to a bubble burst at some point of time.

DEPENDENCE ON FOREIGN CAPITAL
The market looks upon the foreign player as the only drivers who can bring success in driving the indices upwards. The government induces too much faith on the foreign investors who in turn drives the market according to their own wish and this in turn hampers the sentiments of other players in the market. Thus, the Risk in the market increases due to the rigging in stocks by the FII.

LACK OF CO-ORDINATION
Most of the developed economy has sound market because there is proper link between the capital market and the banking system. In case of Indian economy the growth of capital market is restricted because the regulation prevailing in the financial sector. The idle money in the banks cannot be canalized into the capital market before meeting certain norms.

METHODOLOGY TO REDUCE THE RISK
Derivative is a financial instrument whose characteristics and value depend upon the characteristics and value of the underlying assets (commodity, bond, equity or currency). 5 Investors invest in derivatives to manage the risk associated with the underlying security, to protect against fluctuations in value, or to profit from periods of inactivity or decline. In India, the two national exchanges 6 introduced futures on June 9th, 2000 (BSE) and June 12 th, 200 (NSE) fairly at the same time to give the market a new financial instrument that could bring more volatility, efficiency and bring more participants to the market.

The introduction of futures trading improves the depth of the spot equity market and reduces its volatility because the cost to informed traders of responding to mispricing will be smaller. 7 On the contrary, poorly informed speculators in futures markets can lead to a destabilized spot
5 6
http://www.investorwords.com/1421/derivative.html BSE and NSE 7 Danthine (1978), Grossman and Wang (2001)

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equity market.8 A statistically significant increase in the volatility of S&P 500 stocks after the introduction of trading in S&P 500 index futures was noticed in the year 1989. 9 However, trading in financial futures has not led to an increase in the volatility of S&P 500 stocks. 10 A few research studies on the same “Derivatives and its impact on the underlying stock” by different authors in different countries reveal different characters. Stock market volatility increased significantly after the introduction of futures trading in Japan and U.K., but not in Australia, Hong Kong, and other markets. 11 A reduction in the systematic risk of individual stocks was witnessed after the listing of individual stock futures on the Australian markets. 12 The Korean Composite Stock Price Index 200 stocks index futures trading led to an increase in the spot price volatility and market efficiency. 13 The introduction of futures trading has provoked volatility in the underlying spot market in Mexico.14 The NYSE stocks and futures showed evidence that volatility causes volume and also evidenced a significant unidirectional relation running from arbitrage mispricing to cash market volume. 15 Equity volatility is positively associated with unexpected futures trading volume and negatively associated with expected futures trading volume. 16 Overall, there is some evidence from various sources that there exists a relation between volume and volatility. However, no clear evidence is suggested to predict an effect from an external factor like the introduction of related products or unexpected economic shocks, on the volume of the equity market. Consequently, we feel that the relation between the introductions of derivatives (in our case futures) on the underlying cash market should be determined.

8 9

Stein (1987) Harris, 1989, Bae et al., 2004 10 Edwards (1988) and Rahman (2001) 11 Lee and Ohk (1992); Antonios and Holmes (1995); Gulen and Mayhew (2000)
12
13 14 15 16

McKenzie, Brailsford, and Faff, (2001) Bae et al. (2004) Zhong, Darrat, and Otero (2004)
Merrick (1987)

Bessembinder and Seguin (1992, 1993) Page 11 of 55

OBJECTIVE
The objectives are the following: ? To find out does the inclusion of derivatives reduces the risk for the investor. ? To find out the systematic & unsystematic risk before the inclusion of derivatives. ? After the inclusion of derivative the changes to be analyzed.

LITERATURE REVIEW
1 Steven Shuye Wang, Wei Li and Louis T.W Cheng “The Impact of H-share Derivatives on the Underlying Equity Market” tries to conjecture that an introduction of H-share Index17 futures leads to increase in speculating activities 18 in the underlying equities, which leads to an increase in volatility19 and volumes20 of the underlying stocks. The futures trading activities being cheaper and efficient than the underlying stocks21, lead to a significant decline in spot market volatility and volume. However, previous studies conclude that futures trading are associated with an increase in the spot market volatility. Investors trade derivatives for a variety of different purposes viz speculating, hedging, and arbitraging. However, the debate over the role of futures trading on the volatility of the spot equity market has been inconclusive. Here, two popular methodologies have been used to gauge the impact of the introduction of index derivatives on the volatility and liquidity of spot markets. One is to compare differences in spot market volatility using a cross-sectional control sample analysis. 22 And the other is to examine changes in volatility before and after the introduction of derivatives trading using a time series analysis. The paper clearly specifies that the introduction of H-share index futures trading leads to an increase in return volatility. These volatility changes cannot be fully explained by the volatility
17 18

Hong Kong Hang Seng Chinese Enterprise Stock Index Taking large risks, especially with respect to trying to predict the future, gambling, in the hopes of making large and quick profits 19 Relative rate at which the price of a security moves up or down. 20 Number of shares traded during a given time frame. 21 Cheaper: Because the entire money need not be paid when one enters the contract, only a small margin portion to be given. Efficient: True value of the stocks are derived due to futures trading. 22 Harris, 1989; Bae at al., 2004

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changes in the Hong Kong market over the sample period. But the initial finding is consistent with the objective of the authors that the availability of H-share index futures promotes additional speculative activities in the underlying equities, leading to the increase in volatility of the underlying stocks. The rationale behind the same are that the introduction of H-share futures creates additional trading opportunities in the cash market and additional transactions due to hedging, speculating, and arbitraging activities between the futures and the underlying equities. However, the spot market volatility changes after the introduction of the index derivatives trading could not be explained by either the volatility changes in the Hong Kong market or that of the cross-listed Chinese A-shares over the sample period. The paper also studies the difference-in-difference approach to confirm the findings, and that if the result from differences between the HC and control groups, or any bias due to trends. 2 Kiran Kumar Kotha and Chiranjit Mukhopadhay “Impact of Futures introduction on underlying index volatility: An evidence from India” addressed whether, and to what extent, the introduction of futures contract has changed the volatility of the underlying index. They studied whether derivatives market stabilize or destabilize the spot market. The author used linear regression to complex GARCH models with different assumptions and parameters. The paper examines 5 aspects. First, change in the NSE Nifty volatility in the period under consideration through a change point analysis. Secondly, marginal volatilities of before and after series are compared. Thirdly, the paper applies the GARCH model to incorporate the endogenous information in the expression of the conditional volatility. Fourthly, the paper uses the MSCI Index to control the market – wide movements. Lastly, the entire process is repeated on the NIFTY Junior which does not have a corresponding future contract to strengthen the objective of the paper.

3 Sumon Bhaumik and Suchismita Bose Impact of Derivatives Trading on Emerging Capital Markets: A Note on Expiration Day Effects in India address the impact of the expiration day contract on the underlying cash market with respect to volumes, returns and volatility. The authors use AR – GARCH model to analyze the the impact of the same. The results herein indicate that the trading volumes are significantly higher on expiration days and
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during the five days leading to the expiration. The daily return to market returns and the and the volatility of the returns have also been analyzed.

METHODOLOGY
Taking cue from Bessembinder and Seguin, 1992; Gulen and Mayhew, 2000; and McKenzie et al., 2001, we investigate whether the introduction of derivatives trading is associated with greater systematic risk and unsystematic risk. For the purpose of the volatility we used the F – test analysis as has been deployed by Steven Shuye Wang, Wei Li and Louis T.W Cheng in “The Impact of H-share Derivatives on the Underlying Equity Market”.

Since, the systematic and the unsystematic risk will only analyze the impact on the price differentials and the return analysis making the approach specific. Therefore, the use both methods (Systematic and Unsystematic Risk and F-test) in this study to gauge the impact of not only the price and return differentials but also the volatility impact too.

DATA
For the purpose of our study the NSE as the benchmark. This is because the futures market of NSE is much bigger and volatile than the BSE and it is one of the best exchanges in the world in terms of turnover and volumes. NSE also have more listed companies than BSE and hence would give us more efficiency.

For the purpose 15 companies of diversified industries like Oil and Natural Gas (Reliance Industries), Cement (ACC), Information Technology (Wipro), etc. (the list is attached in Annexure). These companies have been chosen due to their market capitalization and impact on the market.

We have selected the NIFTY as the base to show us the market return. The data has been collected from 1st January 1997 to 31st August 2009. The data for the companies have also been collected for the same duration. However, since some companies have been listed afterwards so their data has been taken from the day they have been listed on the exchange.
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For the purpose of the future that data has been collected from the date the company?s shares has been put on the futures list of the exchange. All the data have been collected from www.nseindia.com. The collected data have been divided into two periods: Period – I and Period – II. Period – I is the per iod before the company?s future stock came and Period – II id the period after the company?s future stock came. The whole period has been defined as a time frame from 1 st of January 1997 to 31st of August 2009. ( PRE AND POST CRISIS)

RESULTS
The below table shows the mean price, mean return and the standard deviation for the stocks for the Whole Period. This shall provide us the base upon which we need to work. Table - 1 COMPANY INFOSYSTCH RELIANCE L&T ITC ICICI BANK SBIN GRASIM TATA POWER HERO HONDA ACC WIPRO MEAN PRICE 3,309.86 727.09 888.07 624.36 347.86 632.06 1,032.16 356.33 656.60 583.08 1,317.86 MEAN RETURN 0.0001362846 0.0003038898 0.0002607288 (0.0000549046) 0.0003965111 0.0002679226 0.0002540200 0.0003145659 0.0002422929 (0.0000685689) 0.0000754244
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STANDARD DEVIATION 0.0375430874 0.0277311069 0.0359514638 0.0292059405 0.0340691839 0.0266785660 0.0276068175 0.0290484575 0.0310431277 0.0330419317 0.0423936853

BHEL ABB SAIL PNB

818.54 910.61 51.42 367.36

0.0003250029 0.0000407309 0.0002763424 0.0006873020

0.0314230794 0.0288840693 0.0411418662 0.0315251519

Table – II gives the detail of the mean price, mean standard deviation and F – test over the Whole Period, Period – I and Period – II Table – II Cash Segment F – test Mean Price Whole Company Period 3459.8651 INFY RELIANC E 274.4875 187.84464 L&T 29 676.98928 ITC ICICI BANK SBIN 57 145.11842 11 198.05357 79 Period I 2982.9 83 263.49 286 169.74 714 673.01 071 142.85 263 192.17 Mean Standard Deviaton Whole Period II Period 3936.747 321 285.4821 429 207.2635 714 680.9678 571 147.3842 105 203.9321 0.0420612 24 0.0256713 66 0.0353199 16 0.0306488 71 0.0161326 58 0.0182292 Period I 0.043903 317 0.016304 872 0.037184 879 0.041194 052 0.018884 875 0.019075 Period II 0.040317 404 0.032752 585 0.022253 451 0.017626 755 0.013043 446 0.018681 0.533368 77 0.012161 206 0.087916 485 0.006262 57 0.136962 523 0.943554 (I)

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14 281.96785 GRASIM TATA POWER HERO HONDA 71 119.95892 86 119.95892 86 146.74464 ACC 29

5 279.82 857 112.52 5 112.52 5 138.25 714 1522.8

429 284.1071 429 127.3928 571 127.3928 571 155.2321 429 1427.884 211

58 0.0204872 52 0.0259883 81 0.0210479 88 0.0305883 85 0.0212845 11 0.0240146

751 0.020284 876 0.027852 517 0.015141 606 0.035579 635

772 0.021422 179 0.024758 758 0.026111 862 0.027585 462 0.016558

283 0.853202 125 0.689873 153 0.030507 229 0.390389 441 0.087753 384 0.030818 385 0.058605 918 0.061296 467 0.271667 56

WIPRO

1475.35 144.38571

158 137.92 143 1245.0 357

0.025361 0.029378 921 0.030863 247 0.017506 116 0.031548 654

018 0.015220 2 0.012000 53 0.008620 694 0.041088 794

BHEL

43 1237.8607

150.85 1230.685 714

04 0.0214809 76 0.0143402

ABB

14

SAIL

73.8525

73.71 162.28

73.995

19 0.0358248

PNB

164.375

5

166.465

2

The mean standard deviation and the mean price of the Period – II have taken into account the impact of the futures introduction and as a result their values have been far from the Whole Period. However, the Period – I data are in sync with the Whole Period and thus it can be inferred that the futures have an impact on the spot market. Table – III gives in the detail about all the aspects in the futures market. Table – III

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Future Segment Company INFOSYSTCH RELIANCE L&T ITC ICICI BANK SBIN GRASIM TATA POWER HERO HONDA ACC WIPRO BHEL ABB SAIL PNB Mean Price 3,949.93 285.67 205.92 682.31 682.31 204.27 285.64 127.61 229.06 155.56 1,429.05 151.17 1,227.43 74.10 167.90 Mean Standard Deviation 0.04154691 0.03239731 0.023070534 0.017400724 0.01225288 0.019134991 0.022288505 0.026522276 0.024183623 0.027278315 0.016960099 0.017846671 0.007264464 0.009881854 0.043206292 F – test 0.826095983 0.970482779 0.902639979 0.965074417 0.89228752 0.935185926 0.016924091 0.815488704 0.755394113 0.969697457 0.922346979 0.58988975 0.294679337 0.708578081 0.833501401

The mean standard deviation of the future segment and the Period – II segment are in sync clearly indicating that the futures have an impact on the spot market. Table – IV gives in the detail of the systematic risk and the unsystematic risk of all the companies. Table – IV COMPANY Period I SYSTEMATIC RISK UNSYSTEMATIC RISK
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Period II SYSTEMATIC RISK UNSYSTEMATIC RISK

Reliance

0.042231%

0.957769%

0.039536%

0.960464%

Infosys

0.048747%

0.951253%

0.021116%

0.978884%

L&T

0.046613%

0.953387%

0.000887%

0.999113%

ICICI BANK ITC Grasim Tata Power Hero Honda SAIL BHEL WIPRO PNB ABB ACC SBIN

0.027381%

0.972619%

0.052933%

0.947067%

0.046613% 0.023703% 0.024951% 0.008514%

0.953387% 0.976297% 0.975049% 0.991486%

0.013757% 0.021265% 0.034693% 0.012603%

0.986243% 0.978735% 0.965307% 0.987397%

0.038286% 0.041985% 0.000003% 0.015083% 0.010671% 0.044666% 0.041266%

0.961714% 0.958015% 0.999997% 0.984917% 0.989329% 0.955334% 0.958734%

0.000237% 0.034257% 0.036690% 0.040069% 0.030409% 0.022299% 0.035289%

0.999763% 0.965743% 0.963310% 0.959931% 0.969591% 0.977701% 0.964711%

The systematic risk has reduced over the years for 60% of the companies and increased for 40% of the companies. This signifies that the companies whose systematic risk has increased signify that the company has been affected by the futures trading and that the future trading has led to more investor security. Table – V gives in the details of the turnover over the various periods for both the cash and the future segment. However, since, the F – test details could not be attached herewith due to paucity of space, the analysis in written but the data has been attached in the excel sheet.
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Table - V Average Turnover Whole Period INFOSYSTC H 24,192.44 19,872.44 28,512.43 4,258.51 Period - I Period - II Future

RELIANCE

7,487.48

7,879.93

7,095.04

1,080.33

L&T

6,116.85

4,170.62

8,063.09

1,316.90

ITC

6,819.29

9,302.57

4,336.00

345.54

ICICI BANK

1,151.98

1,259.88

1,044.08

591.73

SBIN

636.88

689.90

583.86

38.56

GRASIM TATA POWER HERO HONDA

507.12

332.88

681.36

18.68

272.37

240.14

304.60

53.81

1,425.70

827.11

2,024.30

1,232.34

ACC

3,567.45

2,703.48

4,431.42

797.73

WIPRO

6,655.00

9,020.56

4,289.44

2,279.56

BHEL

611.15

588.08

634.22

21.72

ABB

415.20

405.93
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424.46

193.73

SAIL

5,005.74

5,280.28

4,731.20

4,974.96

PNB

3,419.92

2,864.07

3,975.77

2,836.27

The data on analysis reveal the fact that the market has changed. The hypothesis being that the stock market has changed both in terms of price and volume is revealed by the F – test conducted on the price differentials and the volume differentials (all the F – Test details are attached in the excel sheets). The z – test data for all the stocks over the 2 analysis i.e. period – I to Period – II and that Period – II to Futures lies within the acceptable limit at the 95% confidence level. This signify that our hypothesis have been accepted and that the derivative trading has its impact on the cash segment i.e. derivatives do have an impact on the underlying stock.

CONCLUSION
In this study, we find that the introduction of the derivative trading is associated with a significant increase in the volatility of the cash segment. We also argue the fact that the future also induces more speculation and arbitrage opportunities against the underlying security and stimulates an increase in volatility and volumes of the underlying stocks. This can also be explained by the fact that the turnover on the exchange has been increasing manifold and the day trading activity has also increased. The day trading activity refers to those trades which do not go in for delivery and are squared – off on the same day. This can be analyzed by the Open Interest Position, and with our analysis we also have found the same factor.

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FINANCIAL RISK MANAGEMENT IN COMMODITIES MARKET

LITERATURE REVIEW
The effect of introduction of futures on the volatility of the spot markets and in turn, its role in stabilizing or destabilizing the spot markets has remained an active topic of research. Though there has been extensive study done on futures and spot markets of stocks and stock indices, there is not much research done on commodities on similar topic. Most studies have dealt with the study of impact of futures trading on spot market volatility in two ways: by comparing spot market volatilities during the pre-and post-futures trading eras and second, by evaluating the impact of futures trading (generally proxied by trading volume) on the behavior of spot markets. The literature is, however, inconclusive on whether introduction of futures leads to an increase or decrease in the spot market volatility. Most studies have used the models consisting of GARCH technique and Granger Causality Tests to find the impact of futures on spot market volatility and the lead-lag relationship between spot and futures prices respectively. According to Snehal Bandivadekar and Saurabh Ghosh, in their paper “Derivatives and Volatility in Indian stock markets” (2003), there is a decline in spot market volatility after the introduction of index futures due to increased impact of recent news and reduced effect of uncertainty originating from the old news. Using surrogate indices like BSE200 and Nifty Junior it was seen that the effect of introduction of futures plays a definite role in the reduction of volatility in the case of S&P CNX Nifty, in the case of BSE Sensex, where derivative turnover is considerably low, its role seems to be ambiguous. According to Panayiotis Alexakis, in his study “the effect of index futures trading on stock market volatility” (2007) conducted in Athens Mar ket in Greece, he found that index of futures trading is fully consistent with efficient market operation as it exerts a stabilizing effect in the spot market, reducing volatility asymmetries and improves the quality and speed of flow of information leading to better price discovery. In the study “Impact of futures and options on the underlying market volatility: an empirical study on S & P CNX Nifty Index” by Mr. Sibani Prasad Sarangi & Dr. K. Uma Shankar Patnaik have found that there are no significant changes in the volatility of the spot market of the S & P CNX Nifty Index, but the structure of the volatility has been changed to some extent. The study
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also found that the new information is assimilated into prices more rapidly than before, and there is a decline in the volatility since the introduction of futures trading. According to the study undertaken by Kiran Kanade “A study of Castor seed Futures Market in India” (2006), the study period for impact of volatility of futures on spot prices was divid ed in three parts: pre futures period from September 1982 to April 1985, futures early sub period from September 1985 to April 1988 and futures later sub period from September 1994 to April 1997. It was found that the introduction of castor seed futures market at Mumbai and Ahmedabad had a beneficial effect on the castor seed spot market in the futures early period. This effect has remained stable in the futures later period, a possible reason being the rise in futures trading volume post 1994 at Ahmedabad. Suchismita Bose (2007) found that using futures prices for the S&P CNX Nifty Index traded on the National Stock Exchange of India, there is significant information flow from the futures to the spot market and futures prices/returns have predictive power for the spot prices. In the long run it was found that between futures and spot prices there is a clear bidirectional information flows or feedback between the markets. The contributions of the two markets to the price discovery process are also almost equal with the futures showing a marginal edge over the spot market, as the information flow into the stock prices from the futures is slightly higher than the price information flows to the futures market from the spot market. While investigating the effect of introduction of the stock index futures on the volatility of the spot equity market, Puja Padhi found evidence that there is not much change in the volatility pattern after the introduction of futures in the Indian stock market. According to the study “Commodity Derivative Market and its Impact on Spot Market” undertaken by Golaka C Nath and Thulasamma Lingareddy in some important agricultural commodities which were banned by the government from trading in futures and their impact on spot prices, it was found that in India, futures trading in the selected commodities had apparently led to increase in prices of commodities like Urad but the same may not be statistically true for other commodities. However the study finds that introduction of futures in selected commodities has not helped in reducing seasonal/cyclical fluctuations in prices. It also finds that futures have increased the volatilities in the spot market for some of the commodities. According to the study “Spot and Futures Markets of Selected Co mmercial Banks in India: What Causes What?” conducted by P. Srinivasan and K. Sham Bhat (2009), the lead -lag relationship between NSE spot and futures market for selected twenty-one commercial banking stocks of India was examined and the analysis revealed mixed findings. Most of the selected commercial bank stocks in India reveal future leads to spot and equal number of selected banking stocks reveals bi-directional and spot lead to future prices. The variation of price discovery mechanism from one bank stock to another is due to the fact that the selected commercial banking stocks are
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widely dispersed in terms of its bank-specific activities and also they are subject towards prevailing differential market frictions such as transaction costs, initial margin requirements, leverage positions and flexibility of short positions and liquidity differences between spot and futures markets. According to the study “Price Leadership between Spot and Futures Markets” by K Kiran Kumar and Chakrapani Chaturvedula (2007), it was found that the spot prices lead the prices discovery as compared to the general expectation that because futures prices give additional information and improve information flow, the futures price lead to price discovery.

According to the paper “Lead – Lag Relationship in Indian Stock Market: Empirical Evidence” by Bhaskkar Sinha and Sumati Sharma, the Nifty Futures market leads the nifty index cash market, a lead – lag relation can be traced for all the years under study individually (1st April 2002 to 31st March 2005), the relationship among the Nifty index futures and cash market has differed considerably during the mentioned time period. The reason for this can be that the two markets are now becoming more efficient and a much faster flow of information between the two markets can be seen.

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OBJECTIVE
The purpose of the study is to investigate the empirical relationship between the futures and the spot prices in relation to commodities in India. The primary objective is to determine if there is any change in the volatility of the underlying spot prices due to the introduction of futures and whether movements in the futures price provide predictive information regarding subsequent movements in the spot market. Hence, the objectives of the study are: a) To examine the volatility of spot market before and after the introduction of futures. b) To examine the lead-lag relationship between spot and futures prices in commodities.

METHODOLOGY
HYPOTHESIS
The study tests the following hypotheses: The volatility of the underlying spot market has not changed after the introduction of futures. In statistical terms the null and alternative hypotheses are specified as under: H0: s (before) = s (after) H1: s (before) ? s (after) For testing the null hypotheses of equal variances researchers, in general, have used four tests, viz. (a) The F-test, (b) The Bartlett test, (c) The Levene test, and (d) The modified Levene test. However, in this study, only the F-test is used for testing the null hypotheses. The impact of introduction of futures contracts on the underlying spot market has been examined by comparing the daily volatility (measured by standard deviation)
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before and after futures introduction in terms of daily closing prices based on ln (Ct/Ct-1) and tested for statistical significance by using F-test.

The lead-lag relationship has been examined by using the following steps: First, univariate time series properties of the spot and futures returns series are observed to account for the infrequent trading and bid-ask price effects. The effect of infrequent trading and bid-ask bounce is examined by running the serial correlation tests on the daily price returns series of the spot and futures markets to determine if past price has an effect on the futures price. In the next step, cross-correlation tests are run between the spot and futures return series. This test helps in determining the extent to which the two markets are correlated to each other and the length of the lead/lag is also determined from the cross-correlation coefficients of the spot index and futures markets. Finally, the lead/lag coefficients are determined by regressing the spot market returns with the current and lagged futures returns and vice versa using simultaneous equation modeling. The following Simultaneous Equation Modeling is estimated to examine the nature of lead-lad relationship between returns in the cash and the futures markets.

k=n RS, t = a + ? ? k RF, t-k + e t k=-n

k=n RF, t = a + ? ?k RS, t-k + e t k=-n RS, t and RF, t are the daily spot and futures returns at time t, n denotes the number of leads/lags used and et denotes the error term.

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DATA
For the purpose of the study MCX is taken as the benchmark. This is because the futures market of MCX is much bigger and volatile than any other exchange in India. MCX is also one of the best exchanges in the world in terms of turnover and volumes. It also provides trading in a comparatively a lot of more commodities and hence would give us more efficiency. For the purpose of the study three commodities (Aluminium, Gold, and Zinc) are selected. These commodities have been chosen due to their market capitalization and impact on the market. Gold is the most important precious metal. It is the base around which all the commodities evolve. All the other commodities have been taken as per the availability of the spot price data. The data has been collected in two tranches for Spot Prices. One tranche includes before the futures were launched and the other after the futures were launched. These data have been collected from NMCE (http://nmcecoin.w01.winhost.com/MarketData/hdata.aspx). The futures data points have been collected from MCX (http://www.mcxindia.com/). The spot rates from January 2005 have been collected from MCX (http://www.mcxindia.com/). The data for the commodities have been collected for different duration depending on their futures trading and availability of data.

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TOOLS USED
SPSS 16.0:
SPSS for WINDOWS provides a powerful statistical analysis and data management system in a graphical environment, using descriptive menus and simple dialogue boxes, to do most of the work. In addition to the simple point/click interface for statistical analysis, SPSS has eight different types of windows: 1 Data Editor 2. Viewer 3. Draft Viewer 4. Pivot table editor 5. Chart Editor 6. Text output editor 7. Syntax Editor

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LIMITATION OF THE STUDY
It must, be noted that since the introduction of futures the Indian market has witnessed several changes in its market micro-structure such as the abolition of the traditional `badla system?, reduction in the trading cycle etc. Therefore, these results should be interpreted in the light of these changes. However, there is no conclusive evidence, which suggests that, the futures volatility is higher (lower) in comparison to the underlying spot market in terms of measures of volatility. The study, being first in the Indian context, has several policy implications for regulators, policy makers, and investors. In lead lag study the white noise effect has not been considered. Also, availability of high frequency data (intraday data, hourly data) could have been made Lead-Lag relationship more concrete.

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EMPIRICAL RESULTS AND DISCUSSION

VOLATILITY OF SPOT MARKET
The study has used following measure of volatility. The measure is based upon close-to-close prices. Therefore, in the first place, the daily returns based on closing prices were computed using equation Rt = ln (Ct/Ct-1) Where Ct and Ct-1 are the closing prices on day t and t-1 respectively; Rt represents the return in relation to day t. Next, variance (standard deviation) is computed of this return series to understand the interday volatility by using equation:

Where Rt is the daily return based on closing prices; R bar is mean of daily returns: T is the time period.

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EFFECT OF INTRODUCTION OF FUTURES ON SPOT MARKET
1. GOLD: Table 1 shows the effect of introduction of futures on the volatility of spot market. Returns are calculated on the basis of daily closing prices. Several window periods are taken for the purpose of study. Standard deviation is calculated for the two price return series; before and after the introduction of the futures. From the table it can be seen that after the introduction of the futures, volatility of the spot market have been declined. The results are statistically significant at 5% level of significance for most of window periods thereby supporting the view that post introduction volatility of the spot market has declined for Gold.

TABLE1: Effect of introduction of futures on spot market volatility for Gold.

DURATION (MONTH-30 DAYS) 1 2 3 6 9 12 18 24 30

STANDARD DEVIATION BEFORE 0.0210733 0.0166442 0.0143089 0.0118522 0.0102783 0.0105685 0.0099906 0.0096941 0.0098459

STANDARD DEVIATION AFTER

F- RATIO

0.0077684 0.0091315 0.0095476 0.0110717 0.0102940 0.0095216 0.0097294 0.0116721 0.0113670

0.057 0.110 0.533 0.866 0.650 0.688 0.615 0.579 0.598

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*Table 1 reports standard deviation of interday measure ln (Ct/Ct-1) for spot index. It tests whether the spot market volatility is significantly lower (higher) for different periods after the introduction of futures contracts.

2. ALUMINIUM: Table 2 shows the effect of introduction of futures on the volatility of spot market. Returns are calculated on the basis of daily closing prices. Several window periods are taken for the purpose of study. Standard deviation is calculated for the two price return series; before and after the introduction of the futures. From the table it can be seen that after the introduction of the futures, volatility of the spot market have been increased for Aluminium. The results are statistically significant at 5% level of significance for most of window periods thereby supporting the view that post introduction volatility of the spot market has increased for Aluminium.

TABLE2: Effect of introduction of futures on spot market volatility for Aluminium.

DURATION (DAYS)

STANDARD DEVIATION BEFORE

STANDARD DEVIATION AFTER

F- RATIO

15 30 45 60 75 90

0.0079322 0.0087034 0.0082297 0.0077627 0.0080246 0.0076379

0.3986038 0.2861919 0.2353977 0.2043956 0.1830828 0.1677613

4.437 3.513 1.496 1.671 1.070 1.110

*Table 2 reports standard deviation of interday measure ln (Ct/Ct-1) for spot index. It tests whether the spot market volatility is significantly lower (higher) for different periods after the introduction of futures contracts.

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3. ZINC: Table 3 shows the effect of introduction of futures on the volatility of spot market. Returns are calculated on the basis of daily closing prices. Several window periods are taken for the purpose of study. Standard deviation is calculated for the two price return series; before and after the introduction of the futures. From the table it can be seen that after the introduction of the futures, volatility of the spot market have been increased for Zinc. The results are statistically significant at 5% level of significance for most of window periods thereby supporting the view that post introduction volatility of the spot market has increased for Zinc.

TABLE3: Effect of introduction of futures on spot market volatility for Zinc.

DURATION (DAYS)

STANDARD DEVIATION BEFORE

STANDARD DEVIATION AFTER

F- RATIO

15 30 45 60 75 90

0.0094466 0.0067791 0.0058446 0.0054192 0.0055225 0.0051082

0.0258687 0.0264582 0.0563782 0.0563782 0.0502739 0.0467896

0.686 0.514 0.378 0.438 0.462 0.715

*Table 3 reports standard deviation of interday measure ln (C t/Ct-1) for spot index. It tests whether the spot market volatility is significantly lower (higher) for different periods after the introduction of futures contracts.

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REASONS FOR LEAD-LAG BETWEEN SPOT AND FUTURES PRICES
From a practical perspective, it is generally agreed that the two phenomena of market sentiment and arbitrage trading are the major determinants linking index futures and the spot market. Conventional wisdom amongst professional traders suggests that movements in the futures price should reflect expected future movements in the underlying cash price. The futures price should quickly reflect all available information regarding events that may affect the underlying and respond quickly to new information. The index should respond in a similar fashion, but for the index to react to the new information completely the underlying stocks must all be revalued, i.e. every constituent stock must re-evaluate the new information and adjust accordingly. Consider a trader with news just arrived to the market that is bullish - the trader has two options. 1. Buy underlying commodity. 2. Purchase the commodity futures. In this scenario, the futures trade can be executed immediately with little initial cash outlay, as futures are a levered instrument, compared to trading actual underlying commodity, which would require a greater up-front investment and a probable longer implementation time because of selection and numerous underlying transactions. This transaction preference for futures may explain why the lead-lag relationship in many markets. Trading futures also has the advantage of a highly liquid market, easily available short positions, low margins, leverage positions and rapid execution. Such trading would move the futures price first then „lead? the spot index when arbitrageurs respond to the deviations from the cost of carry relationship. Futures pricing thus may provide sentiment indicator for changes in commodity prices and hence the spot index. It is also possible that cash index price changes lead changes in the futures prices. If the index were to decline or rise for whatever reason, the price change might induce a change in sentiment that would be reflected in subsequent declines or increases in the futures price. As long as the basis (absolute difference between the futures and spot price) lies within the no arbitrage range, changes in the market sentiment would affect both the futures price and the index in the same direction. The arbitrage bound is essentially the futures to cash price differential, which normally falls within boundaries determined by financing costs. In situations where the bound is breached, arbitrageurs would be able to make riskless profits until the prices traded back within the noarbitrage band. Hence, this study aims to examine the possible lead/lag relation between the spot and futures market and also explore the reasons that cause the lead/lag between the cash and futures market. In order to estimate the lead/lag relation between the spot and futures return series, it is necessary to determine the length of the lead/lag coefficients.
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CROSS CORRELATION COEFFICIENT FOR GOLD

The choice of lead-lag is based on the cross correlation coefficients between index and futures returns for 4 lead/lags as indicated in the Table 4. Table4. Cross correlation coefficient between GOLD SPOT and FUTURES returns.

Lag

CrossCorrelation -0.001 -0.014 0.045 0.126 0.559 0.319 -0.023 0.038 0.016

-4 -3 -2 -1 0 1 2 3 4

Positive value of the coefficients at lags at k = 1,2,3 would indicate that returns in the futures market tend to lead those in the stock market, and positive values for the coefficients at leads k = -1, -2, -3 would indicate that the stock market tends to lead the futures market. Hence, the coefficients with negative subscripts are the lead coefficients and the positive subscripts are the lag coefficients. If the lead coefficients are significant then the cash leads the futures and, if the lag coefficients are significant, the cash index lags the futures. Apart from giving a preliminary look at the lead-lag relation between the two markets, it suggests the number of leads, and lags to be used in the later regression analysis. The contemporaneous correlation is 0.559 suggesting that the two time series are moderately correlated thought not perfectly correlated. The lagged futures returns seem to have forecast power in explaining current spot index returns as the lag 1 coefficient is 0.319. The
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serial correlation between the current spot and futures returns is quite low. The subsequent lead/lag coefficients are diminishing and the results suggest that cross-correlation coefficients at longer leads/lags are not significant. The serial correlation coefficients indicate that the current spot returns are correlated to the current future returns and one lead/lag futures returns. Thus, the coefficient of the lead/lag is estimated by regressing the spot market returns to the current and one lead/lag of futures returns. Similarly, the futures market returns are regressed against the current and one lead/lag of cash market returns.

CROSS CORRELATION COEFFICIENT FOR ALUMINIUM
The choice of lead-lag is based on the cross correlation coefficients between index and futures returns for 4 lead/lags as indicated in the Table 5. Table5. Cross correlation coefficient between ALUMINIUM SPOT and FUTURES returns.

Lag

CrossCorrelation 0.097 -0.066 -0.052 0.104 0.463 0.296 -0.016 -0.049 0.188

-4 -3 -2 -1 0 1 2 3 4

The contemporaneous correlation is 0.463 suggesting that the two time series are moderately correlated thought not perfectly correlated. The lagged futures returns seem to have forecast power in explaining current spot index returns as the lag 1 coefficient is 0.296. The serial correlation between the current spot and futures returns is quite low. The subsequent
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lead/lag coefficients are diminishing and the results suggest that cross-correlation coefficients at longer leads/lags are not significant. The serial correlation coefficients indicate that the current spot returns are correlated to the current future returns and one lead/lag futures returns. Thus, the coefficient of the lead/lag is estimated by regressing the spot market returns to the current and one lead/lag of futures returns. Similarly, the futures market returns are regressed against the current and one lead/lag of cash market returns.

CROSS CORRELATION COEFFICIENT FOR ZINC
The choice of lead-lag is based on the cross correlation coefficients between index and futures returns for 4 lead/lags as indicated in the Table 6. Table6. Cross correlation coefficient between ZINC SPOT and FUTURES returns .

Lag

CrossCorrelation -0.015 0.017 -0.032 0.142 0.401 0.272 0.049 -0.065 0.036

-4 -3 -2 -1 0 1 2 3 4

The contemporaneous correlation is 0.401 suggesting that the two time series are moderately correlated thought not perfectly correlated. The lagged futures returns seem to have
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forecast power in explaining current spot index returns as the lag 1 coefficient is 0.272. The serial correlation between the current spot and futures returns is quite low. The subsequent lead/lag coefficients are diminishing and the results suggest that cross-correlation coefficients at longer leads/lags are not significant. The serial correlation coefficients indicate that the current spot returns are correlated to the current future returns and one lead/lag futures returns. Thus, the coefficient of the lead/lag is estimated by regressing the spot market returns to the current and one lead/lag of futures returns. Similarly, the futures market returns are regressed against the current and one lead/lag of cash market returns.

LEAD LAG RELATIONSHIP
The lead lag relationship between spot and index futures are estimated using Simultaneous equation model (ordinary least squares and two stage least squares regression). The spot as a function of futures and the futures market as a function of spot market are assessed.

LEAD LAG RELATIONSHIP FOR GOLD
The lead lag results for different time periods are discussed below. Lead lag relationship for 24 months The results of the regression are given in Table 7 and 8. The adjusted R2 of 0.795 and a high value of F Statistic (408.017-0.01 level of significance) indicate the goodness of fit of the regression model. The contemporaneous coefficient ?0 is 0.825, and is the largest among all coefficients, suggesting that two markets react simultaneously to much of the information. High positive T-values of coefficients ?0, ?1, ?2 at 0.01 level of significance show that the regression coefficients are statistically significant. The coefficient of lag 1(0.110) futures is higher than the lead 1 futures (0.101), which indicate that futures market, lead the cash index. It is interesting to note that the coefficients of one-lead/lag futures do not differ largely. It may be due the returns taken on a daily basis. The difference would have been prominent had the lag/lead been in minutes or on hourly basis. Nevertheless, the futures lag coefficient is still high enough as against the lead one futures coefficient, which is a clear indication of futures market leading the cash market. On examining the predictive effect of lag spot index returns
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on current futures returns, it is found that the model is deemed fit, as the F statistic (353.132) is statistically significant at 0.01 level of significance.

Table7 Results of the Lead-Lag estimates of GOLD Futures return. Description ?-1 ?0 ?1 LEADS (FUTRET,1) Spot futures returns LAGS (FUTRET,1) Coefficients(?) 0.101 0.825 0.110 T- Ratio 4.230 34.416 4.544 Sig. 0.000 0.000 0.000

a. Predictors: (Constant), spot future returns, LAGS (FUTRET, 1), LEADS (FUTRET, 1) b. Dependent Variable: post futures spot index returns.

Model Summary R R Square Adjusted R Square Standard error of estimate 0.795 7.154E-03

0.893

0.797

It is seen from the regression results (presented in Table 8) that none of the lead/lag coefficients of index returns are significant. Only the spot index returns coefficient (0.881) is statistically significant and much of the adjusted R2 (0.77) is explained single-handedly by the current index returns. The evidence suggests that movements in the index do not provide any information about the upcoming futures prices. However, the possibility of any information transmission from the index to the futures could be verified by pruning the length of the lead/lag to intra-day returns ranging from minute-by-minute to hourly returns as studied by Abhyankar (1995), Chan (1992), Stoll and Whaley(1990).

Table8 Results of the Lead-Lag estimates of GOLD SPOT returns.
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Description ?-1 ?0 ?1 LEADS(INDXRET, 1) Spot index returns LAGS(INDXRET, 1)

Coefficients(?) -0.003 0.881 -0.010

T- Ratio -0.110 31.924 -0.382

Sig. 0.913 0.000 0.702

a. Predictors: (Constant), LEADS (INDXRET, 1), LAGS (INDXRET, 1), spot index returns. b. Dependent Variable: spot futures returns.

Model Summary R R Square Adjusted R Square Standard error of estimate 0.770 8.061E-03

0.879

0.772

Lead lag relationship for 30 months The regression analysis is carried out for 30 months period. The model is considered a good fit as the F-statistic (Table 9) (171.846), is significant at 0.01significance level. The results are a clear indication that the futures market tends to lead the cash market as the lag one futures coefficient is 0.124 (0.01 level of significance) is significant while the lead one futures coefficient is a mere 0.072 (0.05 level of significance). The clear distinction between the lead and lag coefficients vindicates the fact that futures markets are the ones that transmit the information to cash market. From the above analysis it is reinforced that the futures market tend to the lead spot market due to lower transaction costs and the absence of infrequent trading and poor liquidity problems. Table9 Results of the Lead-Lag estimates of spot returns.

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Description ?-1 ?0 ?1 LEADS((FUTRET,1) Spot futures returns LAGS((FUTRET,1)

Coefficients(?) 0.072 0.800 0.124

T- Ratio 1.908 21.108 3.296

Sig. 0.058 0.000 0.000

a. Predictors: (Constant), spot futures returns, LAGS (FUTRET, 1), LEADS (FUTRET, 1). b. Dependent Variable: post futures spot index returns.

Model Summary R R Square Adjusted R Square Standard error of estimate 0.702 8.4326E-03

0.840

0.706

LEAD LAG RELATIONSHIP FOR ALUMINIUM
The lead lag results for different time periods are discussed below. Lead Lag relationship for 75 days The lead lag relation may be affected by the intensity of trading activity in the two markets. Lower trading activity implies that the securities are less frequently traded and thus observed prices lag „true? value more. Moreover, information dissemination may relate to the intensity of trading activity. Admati and Pfleinderer (1988) showed that, in general, traders of both discretionary liquidity traders and informed traders cluster, with each group preferring to trade when the market is thick. The clustering of trade causes more information to be released when trading activity is higher. Therefore, the lead-lag relation is expected to vary with the relative intensity of trading activity in the two markets. Stephan and Whaley (1990) study the intra-day relation between the stock market and the stock option market. They find that not only do price changes of stocks lead price changes of options , but that trading activity (proxied by the number of transactions and trading volume) in the two markets also bears the same kind of lead lag relation. This provides evidence that price discovery and trading activity are related. The results as given in Table 10 and 11 show that the current returns of index is not significantly
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correlated to the current futures returns and current futures returns is not significantly influencing the current index returns during this period of analysis. However the most significant variable influencing Futures is lead 1 of Nifty and the most significant variable influencing spot index is lag 1 of futures. Thus, the results show that futures lead the spot market returns. The model is considered a good fit as the F-statistic is significant at 0.01significance level.

Table10 Results of the Lead-Lag estimates of Aluminium futures returns.

Multiple R

R Square

Adjusted R Square 0.78260

Standard Error 0.54051

F

Sig F

0.88645

0.78580

245.78999

0.0000

Variable SPOT LAG,S1 LEAD,S1 (constant)

B -0.005044 -0.005876 0.851018 1.622245

SE B 0.031440 0.031337 0.031418 0.560194

Beta -0.005237 -0.006134 0.885987 -

T -0.160 -0.188 27.087 2.896

Sig T 0.8727 0.8514 0.0000 0.0042

Dependent variable, FUTURE

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Table11 Results of the Lead-Lag estimates of spot index returns.

Multiple R

R Square

Adjusted R Square 0.80762

Standard Error 0.52918

F

Sig F

0.90026

0.81046

285.06288

0.000

Variable FUTURE

B 0.006259

SE B 0.031978

Beta 0.006027

T 0.196

Sig T 0.8450

LAG,FU1 LEAD,FU1 (constant)

0.934488 0.044292 0.141716

0.032013 0.032284 0.585206

0.903815 0.042470 -

29.191 1.372 0.242

0.0000 0.1716 0.8089

Dependent variable, SPOT

Lead Lag relationship for 90 days The analysis has been done and shown in Table 12 and 13. The results show that during this period futures lead the spot and there is weak evidence of spot leading the futures. However, the influence of spot futures returns seems to be higher than the lag futures as the beta coefficient is very high for spot returns.

Table12 Lead-Lag estimates of spot returns. Multiple R R Square Adjusted R Square 0.90270 Standard Error 0.37324 F Sig F

0.95099

0.90438

535.98484

0.0000

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Variable FUTURE LAG,FU1 LEAD,FU1 (constant)

B 1.004607 0.068281 -0.021905 -0.523016

SE B 0.025107 0.025247 0.025142 0.457088

Beta 0.950111 0.064325 -0.020720 -

T 40.013 2.705 -0.871 -1.144

Sig T 0.0000 0.0075 0.3848 0.2541

Dependent variable, SPOT

LEAD LAG RELATIONSHIP FOR ZINC
The lead lag results for different time periods are discussed below.

Lead Lag relationship for 75 days The lead lag relationship considering the entire period from has been examined and the results are given in Table 13 and Table 14. The results show that the current index returns is significantly influencing the current future returns (.47) apart from lead 1 of spot (.39). Thus, the results show that future leads the spot market returns. The model is considered a good fit as the F statistic is significant at 0.01significance level.

Table13. Lead-Lag estimates of spot Index returns. Multiple R R Square Adjusted R Square 0.41967 Standard Error 1.13319 F Sig F

0.65035

0.42296

128.51644

0.0000

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Variable SPOT LAG,S1 LEAD,S1 (constant)

B .463469 .042801 .390406 1.029827

SE B .032745 .032661 .032631 .539367

Beta .473434 .043730 .399210

T 14.154 1.310 11.964 1.909

Sig T 0.000 .1906 0.000 .0568

Dependent variable, FUT

The results as given in Table 14 shows that the current spot returns is influenced by current futures returns and past futures returns i.e. lag 1 futures returns. It may be inferred that the futures lead the spot and the previous day futures returns also influences the current spot index returns.

Table14. Lead-Lag estimates of futures returns. Multiple R R Square Adjusted R Square 0.42703 Standard Error 1.15126 F Sig F

0.65596

0.43028

132.17036

0.000

Variable FUTURE LAG,FU1 LEAD,FU1 (constant)

B 0.493103 0.415352 0.036211 0.548326

SE B 0.033843 0.033785 0.033814 0.559402

Beta 0.482714 0.406273 0.035391

T 14.570 12.294 1.071 0.980

Sig T 0.000 0.000 0.2847 0.3274

Dependent variable is zinc spot.

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Lead Lag relationship for 90 days The lead lag relationship considering the entire period from has been examined and the results are given in Table 15 and Table 16. The results show that the current index returns is significantly influencing the current future returns. Thus, the results show that future leads the spot market returns. The model is considered a good fit as the F statistic is significant at 0.01significance level.

Table15. Lead-Lag estimates of SPOT index Multiple R R Square Adjusted R Square 0.42595 Standard Error 1.15234 F Sig F

0.65598

0.43030

98.94705

0.000

Variable FUTURE LAG,FU1 LEAD,FU1 (constant)

B 0.492857 0.415032 0.035904 0.551568

SE B 0.033923 0.033899 0.033921 0.560434

Beta 0.482473 0.405961 0.035091

T 14.529 12.243 1.058 0.984

Sig T 0.000 0.000 0.2903 0.3255

Dependent variable, SPOT

The results as given in Table 16 shows that the current spot returns is influenced by current futures returns and past futures returns i.e. lag 1 futures returns. It may be inferred that the futures lead the spot and the previous day futures returns also influences the current spot index returns.

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Table16. Lead-Lag estimates of futures returns. Multiple R R Square Adjusted R Square 0.41860 Standard Error 0.13423 F Sig F

0.65038

0.42299

96.21678

0.000

Variable SPOT LAG,S1 LEAD,S1 (constant)

B 0.042414 0.390090 0.463166 1.033085

SE B 0.032769 0.032713 0.032823 0.540201

Beta 0.043335 0.398887 0.473124

T 1.294 11.925 14.111 1.912

Sig T 0.1961 0.0000 0.0000 0.0564

Dependent variable, future

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FINDINGS
VOLATILITY OF SPOT MARKET
This study in particular addresses the impact of introduction of futures trading on the volatility of spot market. The study utilized daily price data (high, low, open and close) of SPOT Index for all three commodities- GOLD, ALUMINIUM AND ZINC. Similar data of Index Futures is used for all three commodities. The study has used following measure of volatility: The measure is based upon close-to-close prices. Therefore, in the first place, the daily returns based on closing prices were computed using equation Rt = ln (Ct/Ct-1) Where Ct and Ct-1 are the closing prices on day t and t-1 respectively; Rt represents the return in relation to day t. Next, variance (standard deviation) is computed of this return series to understand the interday volatility by using equation:

Where Rt is the daily return based on closing prices; R bar is mean of daily returns: T is the time period. The empirical results obtained after using above measure indicate that the over-all volatility of the underlying spot market has declined after the introduction of index futures for GOLD while volatility of the underlying spot market has increased after the introduction of index futures for ALUMINIUM and ZINC. Table17. Volatility of spot market COMMODITIY Gold Aluminium Zinc VOLATILITY IMPACT Decreased Increased Increased

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The study reveals that there is a fall in volatility of spot market for GOLD since the inception of futures trading which may be attributed to increased trading in cash markets, due to faster dissemination of information, making cash markets more liquid and, therefore, less volatile. It could also be due to shift of speculators from cash to futures market due to low transaction costs and high leveraging in the futures market. This shift can be attributed to low margins, low transaction costs and the standardized contracts and trading conditions prevalent in the futures market. The finding that the volatility of the spot market has decreased with the introduction of futures trading and the explanatory power of index futures on spot market volatility support the introduction of derivatives trading and validates the financial sector reforms in the country. While the volatility of GOLD spot market is decreased after the introduction of futures, same is not true for ALUMINIUM and ZINC, although it was expected other way round. This may be attributed to various reasons: ? ? ? Data for ALUMINIUM and ZINC was not available for longer duration. High frequency (hourly, intraday data) data could have made the difference. Infrequent trading is more frequent for ALUMINIUM and ZINC as compared to GOLD.

These all reasons with combine effect lead to increase in volatility of spot market for ALUMINIUM and ZINC after the introduction of futures.

LEAD LAG RELATIONSHIP
Uncovering lead and lag relations in price changes raises an interesting possibility that the futures and cash markets are not equal in their capacity to discover new information about asset prices. In this backdrop, the study examines the lead-lag relation between the daily futures and cash index prices over the different sample period.

CROSS CORRELATIONSHIP FUNCTION Correlation between the current SPOT returns and one lead and one -lag futures returns are significant with subsequent lead/lag coefficients diminishing. The lagged one futures return seem to have forecast power in explaining current spot index returns with a higher cross correlation coefficient. The results suggest that cross-correlation coefficients at longer leads/lags are not significant. The choice of lead -lag is based on the cross correlation coefficients between index and futures returns and thus one lead / lag is considered for subsequent analysis.

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LEAD LAG RELATIONSHIP FOR GOLD (24 MONTHS) The contemporaneous coefficient ?0 is 0.825, and is the largest among all coefficients, suggesting that two markets react simultaneously to much of the information. The coefficient of lag 1(0.110) futures is higher than that lead 1 futures (0.101), which indicate that futures market, lead the cash index. It is interesting to note that the coefficients of one -lead/lag futures do not differ largely. It may be due the returns taken on a daily basis. The difference would have been prominent had the lag/lead been in minutes or on hourly basis. Nevertheless, the futures lag coefficient is still greater than the lead one futures coefficient, which is a clear indication of futures market leading the cash market. The results indicate that much of the information is transferred from the futures to the spot market than from spot to futures market.

LEAD LAG RELATIONSHIP FOR GOLD (30 MONTHS) The clear distinction between the lead and lag coefficients vindicates the fact that futures markets are the ones that transmit the information to cash market. However, during this period the lag one futures coefficient 0.124 (0.01 level of significance) is much higher than the lead one futures coefficient which is a mere 0.072 (0.05 level of significance).The results are a clear indication that the futures market tends to lead the cash market.

LEAD LAG RELATIONSHIP FOR ALUMINIUM (75 DAYS) The findings are similar to the other results that futures lead the spot market returns but interestingly, during this period there seems to be very insignificant relationship between the current returns of index and current futures returns. However, the most significant variable influencing Futures is lead 1 of spot and the most significant variable influencing spot index is lag 1 of futures. The coefficient of the lagged returns is highest and the other coefficients are insignificant.

LEAD LAG RELATIONSHIP FOR ALUMINIUM (90 DAYS) The findings are similar and the results show that futures lead the spot and there is weak evidence of spot leading the futures. However, the influence of spot futures returns seems to be higher than the lag futures as the beta coefficient is very high for spot futures returns.

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LEAD LAG RELATIONSHIP FOR ZINC (75 DAYS) The results show that that the futures lead the spot and the previous day futures returns also influences the current spot index returns. Interestingly, the influence of current price returns and lag returns is more or less equal compared to the other time periods. Thus, the lead lag analysis shows that the futures returns leads the spot market returns and beta coefficient of the lagged futures returns has been increasing from while the lead futures returns has been decreasing. The influence of current futures price on spot index returns has been declining. However, considering the post futures period, the influence of current futures returns and lag one futures returns are more significant and equal while the lead futures returns is insignificant in determining the spot index price.

LEAD LAG RELATIONSHIP FOR ZINC (90 DAYS) From the regression analysis it was observed that the coefficient of the lagged futures returns is the highest and the other coefficients (current futures returns and lead one futures returns) are insignificant in influencing the spot index returns. From the above analysis, it is reinforced that the futures market tend to the lead spot market and the index futures market serves as a primary market of price discovery. This is attributed to the ease at which the information is absorbed by the index futures contracts due to lower transaction costs and high leveraging in the futures market. These results are plausible given that transaction and entry costs in the stock index futures are lower than the spot markets and probably due to the absence of infrequent trading and poor liquidity problems. It is also shown that the cash index does not lead the futures returns. Though the futures lead the spot market returns by one day, the exact time by which the futures lead the spot market returns is not identified as the study is conducted using daily returns due to lack of data in terms of minute-by-minute or hourly returns.

Table18. Lead-Lag Relationship and Volatility Impact. COMMODITY LEAD-LAG RELATIONSHIP Yes (futures leads spot) Yes (futures leads spot) Yes (futures leads spot)
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VOLATILITY IMPACT

Gold Aluminium Zinc

Decreased Increased Increased

CONCLUSION AND IMPLICATION
The decision to hedge in futures markets is largely influenced by the subjective as well as the objective considerations. The objective considerations are based on historical hedging performance of the contracts. The poor hedging performance of futures can discourage participants from taking positions. As various commodities futures have started only in the recent past it is immature to evaluate their historical hedging performance. An objective evaluation of the performance of any markets requires fairly long period price data. The lack of awareness about the benefits of futures may be attributed to the lackluster response especially to various argri commodities futures markets. The farmers? responses indicate that certain subjective factors have deeply influenced their decisions to use futures. These subjective factors including attitudes and perceptions are important especially in the formative periods of a market. The formation of negative attitudes among them and the inadequate initiatives of the market authorities and regulators to change many of the unhealthy prejudices and negative perceptions about futures trading have prevented the penetration of the markets. Moreover, the awareness about futures and its potential benefits has not been sufficient enough to neutralize all subjective influences. The prevailing market environment therefore poses serious threat to the services offered by the exchange while opportunities are abound due to enormous size of the market that remain unexplored. In addition to making farmers, storage houses, dealers, exporters and processors aware of the utility and operational aspects of futures markets certain specific and focused action is required in order to develop vibrant markets in commodities futures. The exchange should adopt a marketing approach which not only generates more volume and liquidity but also improves penetration of the markets by bringing all ready market players to its fold. This requires a pro-active approach in alleviating all misconceptions and improving attitudes of the existing and potential customers.

As shown by the results of this study that information flows from one market to another market, these results are very useful to regulators as well as to market participants. By using the results of the study, following important suggestions and contributions can be made: ? ? Market participant such as investors, hedgers and speculators can use these results to predict impact of shocks to the futures market on cash market. Any regulatory initiative on futures market will have its desired impact on cash market. Therefore, regulators can take actions in the futures market such as reduction in
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contract size, changes to margins and others which will have their impact on the cash market. ? The Volatility has been found reduced (for GOLD) after the introduction of Index futures. The following suggestions may be implemented to further improve efficiency, liquidity and reduce volatility: a. Futures contracts on more number of indices can be introduced. b. Mini size (smaller value contracts) may be permitted. c. Efforts may be made to look at margin imposition system and reduce margins without compromising on the integrity of the market. ? Right now institutional participation appear to be negligible in the total turnover, therefore, efforts should be made to enhance their role in derivatives participation. ? Results from daily price volatility indicate that information gets reflected first in the futures market. This can be very useful to find out the effect of some information on the spot market.

Futures markets benefit various participants in two ways; directly and indirectly. Participants benefit from direct participation in the market by initiating positions while they benefit indirectly by making use of the price information that futures markets transmit to spot markets on a regular basis. The continuous price discovery in futures markets help farmers and other participants predicts their future cash flows and evens out supply and demand imbalances through out the year. The analysis of the spot market price trends of commodities in this study after MCX launched futures shows that there was substantial improvement in these prices which has undoubtedly helped various participants realize relatively higher price. Currently, there is a renewed interest in futures markets. The Government has been eying for a market driven pricing and risk management system in agriculture for ensuring stable income to farmers which would help ease pressure of subsidy on fiscal situation. Moreover, the government„s commitments under WTO Agreement on Agriculture to phase out direct price and income support to farmers required, inter alia, introduction of a market driven price stabilization mechanism through futures trade in organized exchanges. This re-orientation of government policy towards agricultural product markets has been given thrust in the National Agricultural Policy 2000. In pursuance of this policy, government has removed ban on futures trading for all commodities in 2003. The government, the regulator and exchanges have a role in transforming futures market to more participative and broad based. The most important step in this direction would be the establishment of aggregators who can operate as pool operators in futures markets. Farmers associations, cooperatives and national and state level marketing federations can assume the role of aggregators. Moreover, in view of the prevailing misconceptions and lack of awareness about futures markets, there is need for a comprehensive awareness raising and training programs.
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Hence, results in this study show that there are significant impacts on the informational efficiency in the stock and futures market following the policy changes. These results provide invaluable insight for regulators and policy makers regarding effects of implementing any major policy changes in these and similar markets into the markets.

SUGGESTIONS
? ? ? ? The volatility can also be studied using other models like GARCH. In the lead lag analysis other models like ENGEL GRAGNER, JOHNSON?S MODEL coupled with high frequency data can be used to enhance the results. White noise effect can be considered if better statistical software?s are used. One of the constraints of the data is that daily close values are used whereas the information might get transmitted much faster. This particular aspect can be stated more authoritatively only if high frequency data is used for this purpose. High frequency data is currently not available for spot market Index in India; therefore they could not be employed in the equation. From the results it is very difficult to say how much time it takes to transmit information to cash market.

?

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REFERENCES
1. Snehal Bandivadekar and Saurabh Ghosh, “Derivatives and Volatility in Indian stock markets” (2003) 2. Panayiotis Alexakis, “The effect of index futures trading on stock market volatility” (2007) 3. Mr. Sibani Prasad Sarangi & Dr. K. Uma Shankar Patnaik, “Impact of fut ures and options on the underlying market volatility: an empirical study on S & P CNX Nifty Index” 4. Kiran Kanade “A study of Castorseed Futures Market in India” (2006) 5. Suchismita Bose (2007), “Contribution of Indian Index Futures to Price Formation in the Stock Market” 6. Puja Padhi, “Derivatives and asymmetric responseof Volatility to news in Indian stock market” 7. Golaka C Nath and Thulasamma Lingareddy, “Commodity Derivative Market and its Impact on Spot Market” 8. P. Srinivasan and K. Sham Bhat (2009), “Spot and Futures Markets of Selected Commercial Banks in India: What Causes What?” 9. K Kiran Kumar and Chakrapani Chaturvedula (2007), “Price Leadership betIen Spot and Futures Markets” 10. Bhaskkar Sinha and Sumati Sharma, “Lead – Lag Relationship in Indian Stock Market: Empirical Evidence”

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