Testing Of Volatiltility During 2008 Year For Indian Stock Market

A MANAGEMENT THESIS REPORT ON “Testing the volatility of selected A grade securities during the year 2008”

Submitted to:Mrs. Roopam Kothari Faculty :-Finance INC Adam Smith, jaipur Submitted by:Vimal Sharma Roll no.:-8NBJP080

A report submitted in partial fulfillment of the requirements of THE MBA PROGRAMME (2008-2010) 1

Acknowledgement

I would like to express my gratitude to all those who gave me the possibility to complete this project report. I want to thank the Campus Head, Dr. P.C. Jain, for giving me permission to commence this report in the first instance, to do the necessary research work and to use her knowledge. I have furthermore to thank all other faculty members who gave and confirmed this permission and encouraged me to go ahead with this project. I am bound to my co-researchers for their stimulating support. I am deeply indebted to my supervisor, M.T. Guide and our Finance Faculty, Mrs. Roopam Kothari, INC ASIM

JAIPUR, whose help, stimulating suggestions and

encouragement helped me all the time for research and writing of this report My former colleagues and classmates from INC ASIM Jaipur, and all the faculties who have helped me in learning all those concepts needed for this report also owe this gratitude. I would like to thank all the supporting stuff which helped me in making this report. I will start from Microsoft Corporation for their MS Office without which nobody can even think about report making. Then SPSS for Making statistics so easy and handy. In the last but not the least I would thank my family and friends for their continuous support and trust on me.

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Abstract Volatility of stock market is the degree to which financial prices tend to fluctuate. Large volatility means that returns (that is: the relative price changes) fluctuate in a wide range. In the recent past there have been perceptions that volatility in the market has gone up; Inter and Intra-day volatility. News items and some clinical research papers also provided figures to evidence this argument. SEBI undertook a comprehensive and deep analysis of volatility by using several statistical techniques to measure and analyze it. 18 countries covering almost all continents- developed as well as emerging markets and young and old markets- have been analyzed. The results show that the volatility has not gone up much in the recent past as it has been perceived. Indian stock market provides a very high rate of return and comparatively moderate volatility. Efficiency of Indian market appear to have improved in the past few years owing to contraction in settlement cycles, introduction of derivative products, improvement in corporate governance practices etc,. Stock market return exhibit informational efficiency and approximates to normal distribution. In other words, volatility of the stock refers to the amount of uncertainty or risk about the size of changes in a security's value. A higher volatility means that a security's value can potentially be spread out over a larger range of values. This means that the price of the security can change dramatically over a short time period in either direction. Whereas a lower volatility would mean that a security's value does not fluctuate dramatically, but changes in value at a steady pace over a period of time. One measure of the relative volatility of a particular stock to the market is its beta. A beta approximates the overall volatility of security's returns against the returns of a relevant benchmark (usually the S&P is used). For example, a stock with a beta value of 1.1 has historically moved 110% for every 100% move in the benchmark, based on price level. Conversely, a stock with a beta of .9 has historically moved 90% for every 100% move in the underlying index. 3

Table of Contents

1. Introduction 2. A Look at the Current Stock Market Volatility:-

3. Review of literature 4. Reason of volatility and solution to reduce it

5. Objective Hypothesis and Framework 6. Research design 7. Result and analysis 8. Conclusion and recommendations 9. References

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Introduction The ups and downs of the financial markets are always in the news. Wide price fluctuations are a daily occurrence on the world's stock markets as investors react to economic, business, and political events. Of late, the markets have been showing extremely erratic movements, which are in no way tandem with the information that is fed to the markets. Thus chaos prevails in the markets with investor optimism at unexpected levels. Irrational exuberance has substituted financial prudence. Has the stock market volatility increased? Has the Indian market developed into a speculative bubble due to the emergence of "New Economy" stocks? Why is this volatility so pronounced? In this management thesis I try to analyze these questions in the context of Indian stock markets. I try to unearth the rationale for these weird movements. i examine the fundamentalist view put forward by economists who argue that volatility can be explained by Efficient Market Hypothesis. On the other hand, the view that volatility is caused by psychological factors is tested. An empirical study of NSE and a set of representative stocks are carried out to find the changes in their volatility in the last years. The stock market regulation in introduction of rolling settlement and dematerialization as a measure of reducing volatility is put to test. Thus, this management thesis will help the investors as well as market regulators to make the markets more efficient. Volatility estimates are used extensively in empirical research, risk management and derivative pricing by the finance professionals and researchers. Traditionally, volatility of asset returns has been estimated using sample standard deviation of close-to-close daily returns and is scaled to estimate volatility for any period (such as annual, monthly etc.). 5

“A statistical measure of the dispersion of returns for a given security or market index. Volatility can either be measured by using the standard deviation or variance between returns from that same security or market index. Commonly, the higher the volatility, the riskier the security.” In other words, volatility refers to the amount of uncertainty or risk about the size of changes in a security's value. A higher volatility means that a security's value can potentially be spread out over a larger range of values. This means that the price of the security can change dramatically over a short time period in either direction. A lower volatility means that a security's value does not fluctuate dramatically,

What is volatility? “In words volatility is the degree to which financial prices tend to fluctuate. Large volatility means that returns (that is: the relative price changes) fluctuate in a wide range.” Volatility is the variability of the asset price changes over a particular period of time and it is very hard to predict it correctly and consistently. In financial markets volatility presents a strange paradox to the market participants, academicians and policy makers – without volatility superior returns are can not be earned, since a risk free security offers meager returns, on the other hand if it is ‘high’ it will lead to losses for the market participants and represent costs to the over all economy. Therefore there is no gain saying with the statement that volatility estimation is an essential part in most finance decisions be it asset allocation, derivative pricing or risk management. During year 2008 market is more fluctuate so importance of testing volatility is increased by the investor point of view, so I choose this topic for the management thesis.

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Stock prices are changed everyday by the market. Buyers and sellers cause prices to change as they decide how valuable each stock is. Basically, share prices change because of supply and demand. If more people want to buy a stock than sell it - the price moves up. Conversely, if more people want to sell a stock, there would be more supply (sellers) than demand (buyers) - the price would start to fall.

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A Look at the Current Stock Market Volatility:Current volatility in the stock market is causing chaos in many investors portfolios. Almost every investor is suffering, and many have lost significant portions of what they previously had. Over the past 12 months, the news has been pretty bleak, with some peaks and valleys along the way, but no real end in sight. Market volatility has caused investors who were invested in things they shouldn't have been to get out of the market, and other investors have taken advantage in the crash in prices to gain an adequate stake in the market. As much as we may complain about market volatility, it is an important part of our economy and has always existed, at times more than others. Market volatility is defined as the rate in which a security changes, which is measured by watching the daily change in market price. Although volatility describes both the up and downturns in the market, the significant decreases in price is what is generally most focused on. If a stock is rapidly moving up and down in share price, in significant amounts, it is known to have a high level of volatility. If a share price stays relatively the same over a long period of time, it has low volatility. There is no way to predict market volatility, although many websites try to convince you that they can predict what will happen in the market. If there were an easy answer, so many people wouldn't have lost money in this economic downturn. If you are already invested in a fund, it is probably a good idea not to jump out now, if you have lost a significant amount of money. Although the market is extremely volatile right now, over years the rate of volatility usually levels out, so making a rash decision based on recent activity is a bad idea. If you are not investing currently but are interested in getting into the market, volatility is important to keep in mind if you are investing in a mutual fund or hedge fund. It may be a good idea to contact a licensed investment professional to represent you, as they have experience in dealing with market volatility. 8

Literature Review

Indian Stock Markets are one of the oldest in Asia. Its history dates back to nearly 200 years ago. The earliest records of security dealings in India are meager and obscure. The East India Company was the dominant institution in those days and business in its loan securities used to be transacted towards the close of the eighteenth century. Thus, at present, there are totally twenty-one recognized stock exchanges in India excluding the Over The Counter Exchange of India Limited (OTCEI) and the National Stock Exchange of India Limited (NSEIL). A study conducted by Rajni Mala and Mahendra Reddy on Measuring Stock Market Volatility in an Emerging Economy. According to them Volatility may impair the smooth functioning of the financial system and adversely affect economic performance. Similarly, stock market volatility also has a number of negative implications. One of the ways in which it affects the economy is through its effect on consumer spending. changes in market volatility would merely reflect changes in the local or global economic environment. Others claim that volatility is caused mainly by changes in trading volume, practices or patterns, which in turn are driven by factors such as modifications in macroeconomic policies, shifts in investor tolerance of risk and increased uncertainty. This article benefits from developments in the measurement of volatility through econometric techniques. Here, the regime-switching- ARCH model introduced by Engle (1982) and its extension, the GARCH model, (Bollerslev, 1986) is used to estimate the conditional variance of Fiji’s daily stock return from January 2001 to December 2005. Gregory et.al. (1996) examined how volatility of S&P 500 Index futures affects the S&P 500 Index volatility. Their study also examined the effect of good and bad news on the spot market volatility. Volatility was estimated using EGARCH model. They find that bad news increased volatility more than good news and the degree of asymmetry was higher for futures market. 9

A study conducted by Dante M. Pirouz on National Culture and Global Stock Market Volatility. according to them Volatility is technically defined as the degree to which a market rises or falls in a short-period of time (Mullins, 2000). Since the 1970’s volatility in the bond and stock markets has increased globally and stock market volatility is not only detrimental to investors but also can be harmful to the stability of national and global economic systems (Gerlach, Ramaswamy, & Scatigna, 2006). The Asian financial crisis of 1997 is only one example of the negative effect stock market volatility can have on the global financial network and as a result there is strong interest in both the private and public sectors to understand the antecedents of global stock market volatility. While volatility worldwide is on the rise, some countries’ stock markets are more volatile than others. It remains unclear the underlying factors that cause some global stock markets to have differential volatility. A great deal of literature is devoted to the study of volatility especially in understanding what gives rise to volatility, how it can be predicted and measured but there is still no clear understanding of why global stock markets suffer from volatility, why global stock markets vary in their volatility or how to predict which markets will be more volatile than others. The volatility on the Indian stock exchanges may be thought of as having two components: The volatility arising due to information based price changes and Volatility arising due to noise trading/ speculative trading, i.e., destabilizing volatility. As a concept, volatility is simple and intuitive. In a large scale, the success of derivatives trading will depend on the choice of products to be traded in the markets. The popularly traded and usual types of derivatives are futures and options. The products to be traded in the stock markets need to have the following characteristics which are mentioned by Tsetsekos Varangis (2000): ......a sufficiently higher as well as lower level of price volatility to attract hedgers or speculators, a significant amount of money for speculative motive at a 10

certain level of risk; a significant number of domestic market participants—and possibly buyers and sellers from abroad; a large number of producers, processors, and banks interested in using derivatives contracts (that is, enough speculators to provide additional liquidity); and a weak correlation between the price of the underlying asset and the price of the already-traded derivatives contract(s) in other exchanges (basis risk). Board, Sandamann and Sutcliffe (2001), have tested the hypothesis that increases in the futures market trading activity increases spot market price volatility. They used the GARCH model and Schewert Model and found that the result does not support the hypothesis. The data samples are taken from the U K market. Jeanneau and Micu (2003) have explained that information based or speculative transaction also creates a link between volatility and activity in asset and derivatives market. This link depends in part on whether the new information is private or public and on the type of asset traded. In theory, the arrival of new private information should be reflected in a rise in the volatility of return and trading volumes in single equity and equity related futures and options. A study conducted by George Panayotov on VOLATILITY ISSUES IN FINANCIAL MARKETS. In contrast, time-series studies find that more than one stochastic factor drives asset returns volatility. Engle and Lee (1998) find support for a model with two volatility factors - permanent (trend) and transitory (mean-reverting towards the trend). Gallant, Hsu and Tauchen (1999), Alizadeh, Brandt and Diebold (2002) and Chernov, Gallant, Ghysels and Tauchen (2003) estimate models with one highly persistent and one quickly mean-reverting volatility factor and show that they dominate over one-factor specifications for volatility. Engle and Lee (1998) find that the permanent (or persis-tent) factor in volatility is significantly positively correlated with the market risk premium, while the transitory factor is not. Mac Kinlay and Park (2004) confirm the positive correlation of the permanent volatility factor with the risk premium and also find a time-varying and typically negative correlation of the transitory volatility factor with the risk premium. 11

Reason of volatility and solution to reduce:Reason:“Fundamental” factors:• • • • • •

Macroeconomic stability - vol of GDP growth Stabilizing or destabilizing monetary policy, fiscal policy. Competition on markets - more competition means more uncertain earnings Leverage of firms Indian firms that graduate into MNCs Crises: currency crisis, political crises

Factors internal to the securities markets:• • •

Liquidity of the market: be able to absorb shocks to the order flow. Securities trading issues: “adequate” supply of rational traders - individuals, hedge funds, arbitrageurs. Crises: payments crisis, scandal on the market, regulatory crackdown giving adverse shocks to liquidity.

For reduce volatility:“Fundamental” factors:• Reduction in GDP growth volatility • Firms with more equity financing • Indian firms that are MNCs • Avoid currency crisis, avoid political crises. Factors internal to the securities markets:• • • •

More liquidity More rational traders Avoid crises : payments crisis, scandal on the market, regulatory crackdown giving adverse shocks to liquidity.

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Objective Hypothesis and Framework:-

The research can be used to measure what is the impact of returns for a given security or market index. Volatility can either be measured by using the standard deviation or variance between returns from that same security or market index. This is the exploratory research which tries to shows the factors which are making stock market volatile.

Null hypothesis:- market is not volatile Alternate hypothesis:- market is volatile

 Objective of the study To study volatility in Indian stock market while taking NIFTY of National stock exchange as a source of secondary data during year 2008.

 Universe of the study:As the study is concern about the volatility testing of A grade securities during year 2008, the population of the study would be all the securities in Indian market.

 Sampling of the field of study:As a student it was not possible to do the study with the population (universe of the study) along with our regular studies. 13

So we selected a sample smaller and convenient for the study. so I took a sample of 50 A grade securities from the Indian market.

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 Data collection:Data used in this study is of secondary in nature. Sensex and Nifty is taken as a source of information which widely describes Indian stock market. Here monthly prices of both indexes are taken for the study purpose.

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Research design To testing the volatility of stock market , I took a sample of 5o A grade company of nifty. Then I used these company daily prices to test volatility. I proceed in two ways:• I took whole year data and calculate return, variance, beta, systematic risk, unsystematic risk, total risk. • Then I took duration of 15 days and follow the same procedure

Steps which I used testing the volatility:Step 1:

Return is calculated using logarithmic method as follows. rt = (log pt–log pt-1)*100 where rt= Market return at the period t Pt= Price index at day t Pt-1= Price index at day t–1 and log = Natural log

step 2: then I calculate the variance of securities and for market value step3 :- I used SPSS software for regression and find out beta for each securities step4:- then I calculate the systematic risk of the market systematic risk= (beta)2 * variance of the market step5: then I calculate the unsystematic risk of the market unsystematic risk= total risk – systematic risk where total risk= variance of the security

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step6: then I find correlation between systematic risk and unsystematic risk. The terms which are used in testing volatility. I described them here:1. returns 2. variance 3. regression 4. regression model 5. beta 6. systematic risk 7. unsystematic risk 8. total risk 9. correlation.

Returns:The return on the market as a whole, called the market portfolio. The difference between the return on a stock (or entire portfolio) and the performance of an index.

Variance:A measure of the average distance between each of a set of data points and their mean value; equal to the sum of the squares of the deviation from the mean value. In probability theory and statistics, the variance of a random variable or distribution is the expected square deviation of that variable from its expected value or

mean.

Variance is the measure of the amount of variation of all the scores for a variable. If a random variable x has expected value(mean) μ = E(X), then the variable var(x) of x is given by:-

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Regression:In statistics, regression analysis includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables

Regression model:Regression analysis refers to techniques for modeling and analyzing a number of variables, when the focus is on identifying the relationship between a dependent variable and one or more independent. More specifically, regression analysis helps us understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables — that is, the average value of the dependent variable when the independent variables are held fixed. Less commonly, the focus is on a quintile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function, which can be described by a probability distribution. Regression analysis is widely used for prediction (including forecasting of timeseries data). Use of regression analysis for prediction has substantial overlap with the field of machine learning. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances, regression analysis can be used to infer causal relationships between the independent and dependent variables. Dependent variable:- particular security Independent variable:- market return

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Beta:In finance, the beta (β) of a stock or portfolio is a number describing the relation of its returns with that of the financial market as a whole. An asset with a beta of 0 means that its price is not at all correlated with the market; that asset is independent. A positive beta means that the asset generally follows the market. A negative beta shows that the asset inversely follows the market; the asset generally decreases in value if the market goes up and vice versa. The beta coefficient is a key parameter in the capital asset pricing model (CAPM). It measures the part of the asset's statistical variance that cannot be mitigated by the diversification provided by the portfolio of many risky assets, because it is correlated with the return of the other assets that are in the portfolio. Beta can be estimated for individual companies using regression analysis against a stock market index. The formula for the beta of an asset within a portfolio is

where ra measures the rate of return of the asset, rp measures the rate of return of the portfolio, and Cov(ra,rp) is the covariance between the rates of return. In the CAPM formulation, the portfolio is the market portfolio that contains all risky assets, and so the rp terms in the formula are replaced by rm, the rate of return of the market. Beta is also referred to as financial elasticity or correlated relative volatility, and can be referred to as a measure of the sensitivity of the asset's returns to market returns, its non-diversifiable risk, its systematic risk, or market risk. On an individual asset level, measuring beta can give clues to volatility and liquidity in the marketplace. In fund management, measuring beta is thought to separate a manager's skill from his or her willingness to take risk. 19

Systematic risk:In finance, systematic risk, also sometimes called market risk, aggregate risk, or undiversifiable risk, is the risk associated with aggregate market returns. Systematic risk is a risk of security that cannot be reduced through diversification. It should not be confused with systemic risk, which is the risk that the entire financial system will collapse as a result of some catastrophic event, not to any individual's entity.

Unsystematic risk:The risk of price change due to the unique circumstances of a specific security, as opposed to the overall market. This risk can be virtually eliminated from a portfolio through diversification

Correlation:In statistics, correlation (often measured as a correlation coefficient, ρ) indicates the strength and direction of a relationship between two random variables. The correlation coefficient ρX, Y between two random variables X and Y with expected values μX and μY and standard deviations σX and σY is defined as:

where E is the expected value operator and cov means covariance.

Total risk:Total risk is equal to the variance of the particular security. And also sum of the systematic risk and unsystematic risk. 20

Result and Analysis I test the volatility step wise step and found some result . analysis of the result shown below:1. When I used whole year data:I calculated beta , systematic risk ,unsystematic risk for each securities and arrange them in a table shown below:-

company abb acc ambujace ment bhartiartl bhel bpcl cairn cipla dlf gail grasim hcltch Hdfc hdfc bank Herohon da Hindalco Hindunilv r Icicibank Idea Infosystc h Itc l&t m&m Maruti Nationalu m Ntpc Ongc Pnb Powergri

beta

beta2

systematic risk of market

unsystematic risk

total risk

proportion of systematic risk

proportion of unsystematic risk

0.769 0.616

0.591361 0.379456

0.000468358 0.000300529

0.000638197 0.000707146

0.001106555 0.001007675

0.42325764 0.298240074

0.57674236 0.701759926

0.635 0.746 0.765 0.488 0.635 0.609 0.801 0.628 0.696 0.647 0.768 0.757

0.403225 0.556516 0.585225 0.238144 0.403225 0.370881 0.641601 0.394384 0.484416 0.418609 0.589824 0.573049

0.000319354 0.000440761 0.000463498 0.00018861 0.000319354 0.000293738 0.000508148 0.000312352 0.000383657 0.000331538 0.000467141 0.000453855

0.000908927 0.000645342 0.000974247 0.001227775 0.001779347 0.000370643 0.002358687 0.001687045 0.000581113 0.001547212 0.001294475 0.000861297

0.001228281 0.001086103 0.001437745 0.001416385 0.002098701 0.000664381 0.002866835 0.001999397 0.000964771 0.00187875 0.001761615 0.001315152

0.260000832 0.40581849 0.322378552 0.133162976 0.15216753 0.4421227 0.17725049 0.156223162 0.397667078 0.176467486 0.265177396 0.345096919

0.739999168 0.59418151 0.677621448 0.866837024 0.84783247 0.5578773 0.82274951 0.843776838 0.602332922 0.823532514 0.734822604 0.654903081

0.444 0.756

0.197136 0.571536

0.000156132 0.000452657

0.000531198 0.001696258

0.000687329 0.002148915

0.227157099 0.21064426

0.772842901 0.78935574

0.586 0.835 0.667

0.343396 0.697225 0.444889

0.00027197 0.000552202 0.000352352

0.000433098 0.001924238 0.001382376

0.000705067 0.00247644 0.001734728

0.385735665 0.222982224 0.203116661

0.614264335 0.777017776 0.796883339

0.637 0.636 0.496 0.651 0.602

0.405769 0.404496 0.246016 0.423801 0.362404

0.000321369 0.000320361 0.000194845 0.00033565 0.000287024

0.000622697 0.000397192 0.00312704 0.001229779 0.000695838

0.000944066 0.000717553 0.003321884 0.001565429 0.000982862

0.340409556 0.446463211 0.058654866 0.214414284 0.29202889

0.659590444 0.553536789 0.941345134 0.785585716 0.70797111

0.539 0.814 0.77 0.722 0.767

0.290521 0.662596 0.5929 0.521284 0.588289

0.000230093 0.000524776 0.000469577 0.000412857 0.000465925

0.002178008 0.000613662 0.000622114 0.000847382 0.000892671

0.002408101 0.001138438 0.001091691 0.001260239 0.001358595

0.095549421 0.460961273 0.430137109 0.327602166 0.342946019

0.904450579 0.539038727 0.569862891 0.672397834 0.657053981

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d Ranbaxy Rcom Reliance Rpl Sail satyamco mp Sbin Seimens Ster Sunphar ma Suzlon Tatamoto rs Tatapow er Tatasteel Tcs Unitch Wipro Zeel

0.459 0.77 0.868 0.801 0.756

0.210681 0.5929 0.753424 0.641601 0.571536

0.000166859 0.000469577 0.000596712 0.000508148 0.000452657

0.001421981 0.002020738 0.000851861 0.001175456 0.001698581

0.00158884 0.002490315 0.001448573 0.001683604 0.002151237

0.105019582 0.188561202 0.411930751 0.301821592 0.210416815

0.894980418 0.811438798 0.588069249 0.698178408 0.789583185

0.509 0.776 0.425 0.732

0.259081 0.602176 0.180625 0.535824

0.000205192 0.000476923 0.000143055 0.000424373

0.001772156 0.000868135 0.003583402 0.00187094

0.001977348 0.001345058 0.003726457 0.002295312

0.103771379 0.354574533 0.038389009 0.184886642

0.896228621 0.645425467 0.961610991 0.815113358

0.299 0.517

0.089401 0.267289

7.08056E-05 0.000211693

0.000718039 0.014522521

0.000788844 0.014734214

0.08975864 0.014367437

0.91024136 0.985632563

0.704

0.495616

0.000392528

0.001232651

0.001625178

0.241529099

0.758470901

0.729 0.774 0.669 0.685 0.703 0.575

0.531441 0.599076 0.447561 0.469225 0.494209 0.330625

0.000420901 0.000474468 0.000354468 0.000371626 0.000391414 0.000261855

0.001353555 0.001707384 0.000959462 0.00524636 0.000980184 0.000911479

0.001774456 0.002181852 0.00131393 0.005617986 0.001371597 0.001173334 average=

0.237200115 0.217461186 0.269777112 0.06614936 0.285370634 0.223171825 0.25017002

0.762799885 0.782538814 0.730222888 0.93385064 0.714629366 0.776828175 0.74982998

After of calculation beta I calculated Correlation between systematic risk and unsystematic risk. The result shown below:-

Correlations

unsystematic

Pearson Correlation Sig. (2-tailed) N

systematic

Pearson Correlation Sig. (2-tailed)

unsystematic 1

systematic -.208 .161

47

47

-.208

1

.161

N

47

** Correlation is significant at the 0.01 level (2-tailed).

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47

Analysis:Since the total risk is categorized into systematic and unsystematic risk , there should exist a negative correlation between the two components of risk. To test the magnitude of correlation and its significance the Karl Pearson correlation is calculated at 5% confidence level. The results show that there exist a negative correlation of -0.208, which is highly significant at 5% and 1% confidence level. Further when the proportion of unsystematic risk in the total risk is calculated for the sample companies it comes out to be .25017. This shows that the proportion of diversifiable risk is . 74983. means there is 75% chance to diversify the risk and 25% chance to face the risk.

2. When I used data with 15 days duration:In this case I calculated beta, systematic risk and unsystematic risk for 15 days duration. I arranged the result of correlation with significance value in a table shown below:-

Company Abb Acc Ambuja Bharti Bhel Bpcl Cairn Cipla Dlf Gail Grasim Hcltch Hdfc hdfc bank herohonda Hindalco Hindunilvr

R 0.692 0.769 0.695 0.326 0.554 0.264 0.333 0.692 0.623 0.579 0.637 0.631 0.496 0.734 0.753 0.758 0.721

sig. value 0 0 0 0.12 0.005 0.212 0.112 0 0.001 0.003 0.001 0.001 0.014 0 0 0 0

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significant or insignificant Significant Significant Significant Insignificant Significant Insignificant Insignificant Significant Significant Significant Significant Significant Insignificant Significant Significant Significant Significant

Icicibank Idea infosystch ITC L&T M&M MARUTI NATIONALUM NTPC ONGC PNB POWERGRID RANBAXY RCOM RELIANCE RPL SAIL SATYAMCOMP SBIN SEIMENS STER SUNPHARMA SUZLON TATAMOTORS TATAPOWER TATASTEEL TCS UNITECH WIPRO ZEEL

0.582 0.324 0.707 0.613 0.786 0.675 0.297 -0.007 0.54 0.848 0.734 0.248 0.558 0.764 0.748 0.533 0.455 0.745 0.508 0.729 0.452 0.394 0.566 0.816 0.525 0.464 0.42 0.507 0.29 0.379

0.003 0.122 0 0.001 0 0 0.159 0.974 0.006 0 0 0.242 0.005 0 0 0.007 0.026 0 0.011 0 0.026 0.057 0.004 0 0.008 0.022 0.041 0.012 0.169 0.068

Significant Insignificant Significant Significant Significant Significant Insignificant Insignificant Insignificant Significant Significant Insignificant Significant Significant Significant Insignificant Insignificant Significant Insignificant Significant Insignificant Insignificant Significant Significant Insignificant Insignificant Insignificant Insignificant Insignificant Insignificant

Analysis:by this result I analysis that there is 27 securities out of 47 that are have significance correlation between systematic risk and unsystematic risk and remaining 20 securities are insignificant in correlation.

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Conclusion Through the analysis here I conclude that when I study the market for whole year there is negative correlation between systematic risk and unsystematic risk which is significance means they are inversely correlated. The proportion of unsystematic risk on total risk shows that there 75 % chance to diversify risk in the market. And 25% risk can be covered through certain techniques like hedging. And during this time market is highly fluctuate. Market is highly volatile. And when I study the market for duration of 15 days then the proportion of securities is high on total securities which I study in respect of significance value of correlation. There is 27 securities which is significant correlated out of 47 securities.

The current volatility of the market has both encouraged and discouraged some investors - it is important to understand volatility to be an investor that makes it through the long term ups and downs of the market.

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References Zuliu, H, “Stock Market Volatility and Corporate Investment”, IMF

1.

Working Paper, 1995,pp.95–102. 2.

Levine, R. and S. Zervos, “Stock Market Economic Development and

Long– Run Growth”, World Bank Economic Review, 1996, 10, pp.323–339. 3.

Securities Market In India–A Review, 2003–04, p.20.

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Arestis, P., P.O. Demetriades and K.B. Luintel (2001)”Financial Development

and Economic Growth: The Role of Stock Markets”, Journal of Money, Credit and Banking, 33(2):16-41. 5.

Bilson, C.M., Brailsford, T.J. and Hooper,V.J. (1999)”Selecting

Macroeconomic Variables as Explanatory Factors of Emerging Stock Market Returns”. Magazines 1. Business week 2. Frontline 3. Business world News papers 1. Business standard 2. Financial express 3. Economic times 4.

Times of India 5.

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Websites 1. www.capitalmarket.com . 2. www.wikipedia.org 3. www.google.com 4. www.yahoofinance.com 5. www.moneycontrol.com 6. www.yahoofinance.com

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