Final project on volatility_new 2.doc

8/10/2019 Final project on volatility_new 2.doc

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CHAPTER-1

INDUSTRY PROFILE

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A stock market is a market for the trading of company stock, and

derivatives of same; both of these are securities listed on a stock exchange

as well as those only traded privately.

The term 'the stock market' is a concept for the mechanism that

enables the trading of company stocks (collective shares), other securities,

and derivatives. Bonds are still traditionally traded in an informal, over the

counter market known as the Commodities are traded in commodities

market, and derivatives are traded in a variety of markets (but, like bonds,

mostly 'over-the-counter').

The stocks are listed and traded on stock exchanges which are entities

specialized in the business of bringing buyers and sellers of stocks and

securities together.

The stock market is one of the most important sources for companies

to raise money. This allows businesses to go public, or raise additional

capital for expansion. The liquidity that an exchange provides affords

investors the ability to quickly and easily sell securities. This is an attractive

feature of investing in stocks, compared to other less liquid investments

such as real estate.

History has shown that the price of shares and other assets is an

important part of the dynamics of economic activity, and can influence or be

an indicator of social mood. Rising share prices, for instance, tend to be

associated with increased business investment and vice versa. Share prices

also affect the wealth of households and their consumption. Therefore,

central bank tends to keep an eye on the control and behavior of the stock

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CHAPTER-2

COMPANY PROFILE

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Shah Investors Home LTD

It was establish in 1980

Mission is to provide excellent service in the stock market, in the

capital, Future and options segment as a depository participant.

Accumulated expertise of 25 years under their thinking cap.

As I have undertaken summer project training under the guidance of Mr.

Kishore Parikh being the remissor of shah investment pvt. Ltd. So there is

no requirement of the company profile.

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CHAPTER-3 6


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THEORETICAL

ASPECTS

CONCEPTUAL FRAME WORK

INTRODUCTION:

What is volatility?

The dictionary meaning of the word volatility means the rapid changes

or unpredictable.

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Volatility most frequently refers to the standard deviation of the

change in value of a financial instrument with a specific time horizon. It is

often used to quantify the risk of the instrument over that time period.

Volatility is a measure of uncertainty of the return realized on an asset.

Volatile market carries wide fluctuations on either side. It offers

(fluctuations) false signal for investment. In order to estimate, understand

and forecast these fluctuations, volatility indicator is developed by some

researchers to serve above purpose.

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 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 the period of time.

Volatility is a measurement of change in price over a given period. It is

usually expressed as a percentage and computed as the annualized

standard deviation of the percentage change in daily price. The more volatile

a stock or market, the more money an investor can gain (or lose) in a short

time.

Types of volatility:

1.Standard deviation:

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Standard deviation of a probability distribution, random variable,

or population or multi-set of values is a measure of the spread of its

values. It is usually denoted with the letter (lower casesigma). It

is defined as the square root of the variance. In other words, the

standard deviation is the root mean square (RMS) deviation of

values from their arithmetic mean.

Standard Deviation=N

xx 2)(

The standard deviation is the most common measure of statistical

dispersion, measuring how widely spread the values in a data set is. If thedata points are close to the mean, then the standard deviation is small. As

well, if many data points are far from the mean, then the standard deviation

is large. If all the data values are equal, then the standard deviation is zero.

2.Chaikins volatility:

It is based on the difference between the high and low prices posted

by the scrip. The higher the difference between the high and the low

prices, the higher would be the volatility. In the oscillator, a ten period

average of the difference between the high and the low prices is first

calculated. Then a ten period ROC is calculated of the average values.

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Exampleof chaikins volatility

3.Wilders volatility:

It is based on concept of true range. The true range is defined as the

greater value obtained from the three equations:

Current high current low

Previous periods close- current low

Previous periods close- current high.

The true range so calculated is averaged to get the wilders volatility.

High volatility would indicate that possibility of a top being formed and low

values of volatility would indicate the possibility of a bottom being formed.

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4. Beta volatility:

This method is to calculate each stocks average daily or weekly price

change over that past year or two. A far more sophisticated approach is to

correlate a stocks daily or weekly percent price changes of a broad based

market index. This type of relative volatility is called a beta.

A beta tells not just how volatile a stock volatile it has been relative to

the market. It describes the relationship between the stocks return and

index return.

5.Square root volatility:

Square root rule states that given a certain market advances all stocks

change in price by adding a constant amount to the square root of their

beginning prices.

Example:if the average priced stock advances from 25 to 36, the square

root of the average price has moved from 5 to 6 or, up by 1 point.

6. Implied volatility:

Volatility implied from an option price. In terms offinance, the implied

volatility of contract i.e. option is thevolatilityimplied by themarket priceof

the option based on anoption pricingmodel.

7.Volatility smile:

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Variation of implied volatility with strike price.

8.Volume volatility:

It refers to the number of shares or contracts traded in a security or

an entire market during a given period. Volume is normally considered on a

daily basis, with a daily average being computed for longer periods.

Example

Large increases in volume can be seen on days [1],[3] and [5] - when closing

price falls sharply, signaling that distributionis taking place.

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There is unusually low volume on days [2] and [4], both areinside days

signaling uncertainty.

9. Historical volatility(or ex-post volatility):

It is the volatility of a financial instrument based on historical

returns.

CHAPTER 4

RESEARCH

METHODOLOGY 13
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2.1 Research statement:

To study volatility of BSE SENSEX.

2.2 Objective:

Primary objective:

Study of volatility of BSE SENSEX

Secondary objectives:

-To measure risk through volatility.

-To know the average relationship between standard deviation and

closing price through regression analysis.

-To check the volatility of intra day.

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-To study the impact of bse sensex volatility change on different sectors

stocks/scripts through correlation.

2.3 Research methodology:

Research methodology is the systematic design, collection and analysis

and reporting of data and findings, relevant to appraisal specific personnel

situation facing the company. Research methodology describes the research

procedure covers the following:

(A)Research design

(B)Data collection method

(A)Research Design:

It is an overall framework of project that indicates what information to be

collected from which sources and by which procedures. It is the blueprint

for the collection, measurement and analysis of data.

Research study is the plan structure and strategy of investigation

conceived so as to obtain answers to research queries and to control

variance.

Descriptive research design is used for this study.

(B)Data collection method:

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There are two sources of data:

1.Primary data sources

2.Secondary data sources

Here, secondary data collection method is used, which are collected from

website of www.bseindia.com.

Statistical test used:

Coefficient of correlation and regression analysis are used for statistical test.

How to conduct statistical test:

The statistical test are conducted in Microsoft excel.

Sample period:

The study has included data from 1989 to 2007 JAN. in almost all the

cases except for SBI which went public in 1994 and Infosys which was

started in 1993.

About the study:

These study focuses on the volatility of BSE SENSEX i.e. through

monthly and weekly data of index, but the findings of it cant be generalize

on each and every stocks or scripts. Therefore four scripts from four

different sectors are taken into consideration they are:

Cipla (pharmaceutical co.)

Infosys (Information technological co.)

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Reliance Industries ltd

State bank of India (Bank).

The above companies are selected as they are the leading companies in

there respective sector so there findings can be generalized on different

companies of each sector.

Benefits from the study:

Through this project I got the practical training of research

It helps the investors to identify the risk associated with each

company under study. It facilitates their investment decisions.

Organization can use this data for further study also.

It can also use to measure risk.

Limitations of study:

In each and every study there are limitation, which lead that project

is not the perfect study though there can be the hard work and sincere

efforts.

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Efforts are made to make the study successful, but it is impossible to

do away with all human intervention and unavoidable circumstances there

are some limitations on which project is lacking far behind for some extent.

Following are the limitations of my study.

Time act as a constraint.

Scope of study is limited.

Project also focuses on the volume volatility but BSE SENSEX does

not give volume data.

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CHAPTER - 5

DATA ANALYSIS &

INTERPRETATION

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DATA ANALYSIS:

Methods employed for the study are as follow:

1.Standard deviationis the one of the measure of volatility.

A.Standard deviation is calculated on closing price

1.1 BSE weekly

In 1.1 maximum volatility is 813.4155 on 9/6/2006 and minimum is

11.29047.

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1.2 BSE monthly.

Inference:

In1.2 maximum volatility is 1019.18 on 30/11/2006 and minimum is

15.3709 on 27/1/1989.

Wider fluctuations can be seen in weekly volatility compare to monthly this

shows that consistent trend can be obtain in monthly volatility compare to

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weekly. Whether it is monthly or weekly data, period of year 2006 can be

considered to be the most volatile year.

1.3 Cipla weekly

Inference:

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1.3 is the graph of cipla weekly data, maximum volatility is 541.18413 on

28/5/2004 and minimum is 0.07059 on 6/7/1990 whereas mean value is

23.1029. The values which are very far away from mean shows the price

fluctuations are higher.

1.4 Infosys weekly.

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1.4 is the graph of Infosys weekly data, maximum volatility is 148.171 on


24.5633.

1.5 Infosys monthly

1.5 is the graph of Infosys monthly data, maximum volatility is 232.2 on


51.7056.

1.6 Reliance weekly

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1.6 is the graph of reliance weekly data, maximum volatility is 108.7 on

14/11/1997 and minimum is 0.894427 on 7/6/1990 and 15/6/1990 i.e.

remains constant for one week whereas mean value is 15.2273.

1.7 Reliance monthly

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1.7is the graph of reliance monthly data, maximum volatility is 166.16 on

29/6/2007 and minimum is 2.5767 on 29/9/2000 whereas means value is

33.5106.

Reliance monthly does not shows wide fluctuations as weekly, after the

period of 2005 in monthly data, volatility does not reaches below maximum

bottom. There is increase in volatility at a diminishing rate.

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1.8 SBI weekly

1.8 is the graph of SBI weekly data, where maximum volatility is 85.9865 on


14.7913.

1.9SBI monthly

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1.9 is the graph of the of SBI monthly, where maximum value is 225.75 on


34.9086.

Inference:

From the entire above, maximum volatility can be seen in month wise data

analysis whereas minimum value can be seen in week wise data analysis.

The cipla is considered to be attaining the highest volatility i.e.541.1841 in

the year 2004 which is highly deviated from the mean value i.e.23.1029 as

compared to any other stocks.

BSE SENSEX index faced highest volatility in the year 2006.

Low value of standard deviation indicates that possibility of a bottom being

reached and high value of it indicate that a top being formed.

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Wider fluctuations can be seen in weekly volatility compare to monthly this

shows that consistent trend can be obtain in monthly volatility compare to

weekly.

B. Standard deviation calculated on the basis of absolute value.

As we need to check the validity of the available data. So we use two

methods viz. closing price magnitude and ratio of the closing price. This is

done to smoothen out any inconsistency in the data that is available to us.

The test that is time dimension is considered where t2-t1 and t2/t1 is

applied. This is taken to have a test of validity and to know the whether this

test is best fit or not in a modified data (t2-t1 and t2/t1).

(1)The below is the standard deviation taken from the absolute value i.e.

(current close price previous close price)and its standard deviation.

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BSE monthly:The below is the graph of standard deviation of t2-t1.

Maximum value 936.665 on 31/5/2006

Minimum value - 22.4038 on 24/2/1989

Bse weekly

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Maximum value 2458.6896 on 11/3/1994. Minimum value 8.9934 on

20/7/1990

Cipla weekly- S.D. @ t2-t1 close price

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Maximum value 443.0707 on 21/5/2004, Minimum value 0.0947 on

10/5/1990

Infosys monthly:S.D.@ t2-t1 close price


Minimum value 1.9690 on 31/10/1995

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Infosys weekly:The below is the graph of standard deviation of t2-t1



Reliance monthly:

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Minimum value 3.3075 on 29/9/2000.

Reliance weekly

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SBI monthly


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SBI weekly



(2)The below is the standard deviation taken from the absolute value i.e.

(current close price / previous close price)which indicates volatility of

absolute value.

BSE Monthly:

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Maximum value 0.260655 as on 5/29/1992

Minimum value 0.013479 as on 4/28/1995

Average value 0.07064, maximum values lies between 0.05 and 0.1.

Values which are above and far away from average values are considered to

outliners, so values above 0.1 are considered to be the outliners.

Cipla weekly:

The below is the graph of standard deviation at t2/t1 of cipla weekly data

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Minimum value - 0.006985 as on 10/5/1990

Average value 0.05312, therefore values which are far from mean value are

considered to be outliners.

Infosys weekly:

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The below is the graph of standard deviation at t2/t1 of Infosys weekly.



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Infosys Monthly:

The below is the graph of the standard deviation on t2/t1 of Infosys monthly



Average value 0.1154.

The values are near to the mean value which indicates very less fluctuation.

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Reliance weekly:

The below is the graph of the standard deviation on t2/t1 of Reliance

weekly.



Values fluctuate above and below 0.05.

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Reliance monthly:The below is the graph of standard deviation of t2/t1 of

Reliance monthly.

Maximum value 0.45331 as on 6/21/1992, Minimum value 0.02287 as

on 8/15/2000.

SBI weekly:

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The above is the graph of SBI weekly of Std. deviation at t2/t1.

SBI Monthly:

The below is the graph of SBI monthly of standard deviation at t2/t1

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Inference:

Through original data we concluded that the maximum value are

obtained in month wise data analysis but here maximum volatility is seen in

week wise data analysis (in absolute data analysis). Here more fluctuation

can be seen in month wise data analysis than week wise data analysis.

Further trend can not be determined through absolute value.

If absolute value of t2/t1 and its standard deviation are taken than

they show very low value that volatility can not be measured.

In BSE weekly very few fluctuations are seen and the values which are

very far from the mean are considered to be out liners.

Limitations of the standard deviation method

It gives more weight age to extreme items and less to those which are near to

the mean.

2. Chaikins volatility:It is the measurement of volatility of intra day

trading.

2.1 BSE monthly:

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High value of chaikins volatility indicates that there are wide fluctuations

during intra day.

2.2 BSE weekly:

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2.3 Cipla weekly:

2.4 Infosys weekly:

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2.5 Infosys monthly:

Maximum value 523.024 on 28/11/1997,Minimum value 38.9503 on

31/8/1995

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2.6 Reliance weekly:


6/2/1998

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2.7 Reliance monthly

Maximum value 313.4752

Minimum value (-71.4251)

2.8 SBI weekly:

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15/7/1994

2.9 SBI monthly:

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Maximum value 233.4691 on 30/6/1999,

Minimum value 53.0883 on 29/10/2004.

Inference:

The higher the difference between the high and the low prices, the

higher would be the volatility and vice versa.

Chaikins volatility indicator measures the volatility of security. High

values indicate that prices are changing a large amount during the day. Low

values indicate that prices are staying relatively constant. Both trending and

level can have high or low volatility.ROC= current difference of average high

n low /ten days ago difference of average high n low*100.

3.Volume volatility:

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BSE SENSEX volume data are not available.

Standard deviation as a measure of volatility on volume.

3.1 cipla S.D. @ volume

(a) Original values are taken to calculate standard deviation.

Inference:

Maximum value 2188356 on 26/5/2006

Minimum value 58 on 29/1/93 & 5/2/93

In above chart in the initial period fluctuations are very minute and upto

certain extent values remain constant for particular period of time i.e. 190

is constant for 3 weeks.

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(b) Below is the graph of standard deviation of t2-t1 i.e. absolute value to

measure volatility of volume.

Maximum value is seen on 26/5/2006 after which stock enters into bear

trend. Minimum value as on 13/12/1993, after this, stock has the bullish

trend and that to longer period of time.

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(c) Below is the graph of standard deviation of t2/t1 i.e. absolute value to


Maximum volatility as on 21/11/1997 and minimum volatility on

22/4/1994.

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3.2 Infosys weekly:

(a) Below is the graph, of Original values which are taken to calculate

standard deviation.



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Maximum value as on 1/10/1997, minimum value is 0.

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3.3 Infosys monthly:


standard deviation.

Inference:

Maximum value- 4122546.4 on 31/5/2001, Minimum value- 10612.44 on

31/1/96.

More fluctuations can be seen in the weekly volume compare to monthly

volume volatility; in long run one can avoid risk due to less fluctuation.

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Maximum value was on 5/31/1996, minimum value was on 4/29/1994.

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Maximum volatility was on 5/31/1996, minimum as on 4/29/1994.

Inference:

High value of standard deviation indicates high volatility. From above

charts we can conclude that, an increase in volatility may well mark a turn

in the direction of a bearish trend and over longer term, a decrease in

volatility would indicate the possibility of a significant bull market top being

approached.

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standard deviation.

Maximum value 3160102.65 on 13/4/2006




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Maximum value was as on 4/7/2006 and minimum value was on

10/21/1998.

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3.5 Reliance monthly:


standard deviation.

Inference

Maximum value: 60248999.52 on 29/11/1996

Minimum value: 869276.64 on 31/1/90

Low volatility indicates that fluctuations has dried up and high volatility

value which are very far away from mean value that values are considered to

be the outliners.

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Maximum value was in 10/31/1996 and minimum was on 2/28/1989.

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3.6 SBI weekly:


standard deviation.

Inference:

Maximum value: 1360552.698 on 28/5/1999, Minimum value: 58885.69

on 7/10/1994.



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3.7 SBI monthly:


standard deviation.

Inference:

Maximum value: 47046084.42 on 31/7/1996, Minimum value: 231870.048

on 23/12/94.



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Inference:

Low volatility indicates that fluctuations has dried up and high

volatility value which are very far away from mean value that values are

considered to be the outliners.

An increase in volatility may well mark a turn in the direction of a

bearish trend and over longer term, a decrease in volatility would indicate

the possibility of a significant bull market top being approached.

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4. Correlation analysis:

It deals with the association between two or more variation.It

attempts to determine the degree of relationship between variables.

CIPLA INFOSYS SBI RELIANCE BSE

BSE -0.08437 0.901543 0.81966 0.445132075 1

There is linear high degree positive relationship between Bse Sensex and

Infosys. Similarly there is high degree positive relationship between Bse

Sensex and SBI.

There is non linear high degree negative relationship between BSE

Sensex and Cipla. There is low degree positive relationship between BSE

Sensex and Reliance.

From the above correlation we can conclude that price movement in Bse

Sensex index has direct effect on INFOSYS and SBI price movement in same

direction and vice versa.

Inference:

There is positive relationship between BSE SENSEXand INFOSYS

where as there is negative relationship between BSE SENSEX and CIPLA.

Positive relation indicates that there is high degree direct relationship

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between BSE SENSEX and INFOSYS. And negative relation indicates there

is low degree indirect relationship between BSE SENSEX and CIPLA.

5. Regression Analysis:

It gives average relationship between two or more variables. Therefore

it is useful in estimating and predicting the average value of one variable for

a given value of another variable.

It provides estimates of values of the dependent variables from the

values of independent variables.

The second goal of regression analysis is to obtain a measure of

the error involved in using the regression line as a basis forestimations.

R2= bxy*byx.

5.1 BSE Weekly:

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The above is the graph of regression (R2) between the closing price of the

BSE SENSEX weekly and standard deviation on close price.

On date 8/10/90 R2is 0.93368, on 11/26/93 R2is 0.965475, on

1/9/04 R2is 0.963724. This shows strong relationship between closing

price and its standard deviation.

On the other hand on 5/25/90 R2is 0.002199, on 12/1/00 R2is

0.000151 this shows far away relationship between closing price and its

standard deviation.

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The above is the graph of R2between absolute value i. e. current

closing price previous day closing price and its standard deviation. Most of

the values are between 0.001 and 0.15 this shows very low relationship

between difference of close price and its standard deviation.

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The above is the graph of the regression between absolute value i. e.

current close price / previous day close price and its standard deviation.

Onlyon the date 3/4/94 R2 is 1 this shows strong relationship between

ratio and its standard deviation. But on the contrary, most of the value falls

between 0 to 0.2 as on date 3/16/90, 3/8/91, 1/12/96, 7/4/03, 4/29/05,etc. this shows no close relationship between ratio of closing price and its

standard deviation. So we cant compare with the index. It does not indicate

true market position. Therefore we should rely on original value rather than

on any derived value.

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5.2 BSE Monthly:

The above is the graph of the R2between closing price and its standard

deviation.

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The above is the graph of R2between absolute value i. e. t2-t1 and its

standard deviation.

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(b) the graph of R2between t2/t1 and its standard deviation.

The maximum values are between 0.1 and 0.2.

(c) The graph of the R2between t2-t1 and its standard deviation.

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Maximum values falls between 0.1 and 0.2.

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5.4 Infosys weekly:

(a) The graph of R2between close price and its standard deviation.

Maximum value falls between 0.2 and 0.6.

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(b) the graph of the R2between t2-t1 and its standard deviation.

Here maximum values are less than 0.2.

(c) the graph of R2between t2/t1 and its standard deviation.

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Maximum values falls below 0.3.

5.5 Infosys Monthly:

(a) The graph of theR2between close price and its standard deviation.

(c) the graph of R2between t2/t1 and its standard deviation.

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(a) the graph of the R2between close price and its standard deviation.

(b)the graph of the R2between t2-t1 and its standard deviation.

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(c)The graph of the R2between t2/t1 and its standard deviation.

5.7 Reliance monthly:

(a) The graph of the R2between close price and its standard deviation.

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(b)the graph of R2between t2-t1 and its standard deviation.

(c)the graph of the R2between t2/t1 and its standard deviation.

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5.8 SBI Weekly:

(a) the graph of the R2between close price and its standard deviation.

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(b) is the graph of the R2between t2-t1 and its standard deviation

(c)The graph of the R2between t2/t1 and its standard deviation.

5.9 SBI monthly:

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(a) The graph of the R2between close price and its standard deviation.

(b) the graph of R2between t2-t1 and its standard deviation.

(c)the graph of the R2between t2/t1 and its standard deviation.

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Inference:

Maximum value ranges between 0.2-0.6

This shows that investor is having good idea about the trend prevailing in

the market by seeing the standard deviation of closing price.

Good regression or close regression reflect that less fluctuation is there &

more reliable picture came into existence and vice-versa.

From above graph it seems that original values and t2-t1 regression shows

concrete relationship, but in case of t2/t1 regression shows value below 0.7.

So from this we can say that original values and t2-t1 regression represent

stock well with the closing price and standard deviation.

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CHAPTER 6

FINDINGS &

CONCLUSION

CONCLUSIONS:

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BSE SENSEX index faced highest volatility in the year 2006.

Reliance monthly does not shows wide fluctuations as weekly, after

the period of 2005 in monthly data, volatility does not reaches below

maximum bottom. There is increase in volatility at a diminishing rate.

The cipla is considered to be attaining the highest volatility

i.e.541.1841 in the year 2004 which is highly deviated from the mean

value i.e.23.1029 as compared to any other stocks.

Cipla is the most volatile stock compare to other three stocks and SBI

is less volatile stock.

Chaikins Volatility peak occurs as the market retreats from a newhigh and enters a trading range.

The market ranges in a narrow band - note the low volatility.

The breakout from the range is not accompanied by a significant rise

in volatility.

Volatility starts to rise as price rises above the recent high.

A sharp rise in volatility occurs prior to a new market peak.

The sharp decline in volatility signals that the market has lost

impetus and a reversal is likely.

Market tops would formed by an increase in volatility and market

bottoms would be formed by a decrease in volatility.

High value of standard deviation shows high volatility but trend can

not stay at a high value for a longer period of time and so it has to come

down, low volatility value shows that the fluctuation has dried up.

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Low values of standard deviation would indicate the possibility of a

bottom being reached i.e. low volatility and high value of standard deviation

would indicate the possibility of a top being formed i.e. high volatility.

High value indicates high volatility and vice versa. The values which

are above and far away from the mean value are considered to be outliners.

High volatility means high risk and low volatility means low risk.

Modified data shows very minor fluctuations so it cant be used in

measuring volatility or risk.

An increase in volatility may well mark a turn in the direction of a

bearish trend and over longer term, a decrease in volatility would indicate

the possibility of a significant bull market top being approached.

Example: After reaching at new high of 909.47 as on 4/30/1992, BSE

Sensex closing price volatility mark a turn of bear trend and that to a longer

period of time. A decrease in volatility upto 82.24 as on 4/30/2004, Bse

Sensex closing price volatility showed the indication of bull market for top

being approached.

Investors have to look upon R2of closing price and standard deviation

of monthly and weekly data for better insight.

VOLATILITY FOR MARKET PLAYERS:

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Volatility is often viewed as a negative in that it represents uncertainty

and risk. However volatility can be good in that if one shorts on the peaks,

and buys on the lows one can make money with greater money coming with

greater volatility. The possibility for money to be made via volatile market is

how short term market players like day traders hope to make money and is

in contrast to the long term investment view of buy and hold.

VOLATILITY DOES NOT IMPLY DIRECTION:

An instrument that is more volatile is likely to increase or decrease in

value more than one that is less volatile.

VOLATILITY OVER TIME:

During some periods prices go up and down quickly while during

other times they might not seem to move at all. Periods when prices fall

quickly (a crash) are often followed by prices going down even more or going

up by an unusual amount. Also a time when price rise quickly (a bubble)

may often be followed by prices going up even more or going down by an

unusual amount.

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

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value can potentially be spread out over a larger range of values. Meaning

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.

CHAPTER 7

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RECOMMENDATIONS

RECOMMENDATION :

High volatility levels can sometimes be used to time trend reversals such

as market tops and bottoms. Low volatility levels can sometimes be used

to time the beginning of new upward price trends following period of

consolidated.

The values which are far away from the mean are considered to be the

outliners, at this period it not advisable for investors to invest. Example

volatility of cipla was 514.18 during the year 2004 which is far deviated

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from the mean value of 23.1029. During this period it is risky on the

part of the investor to invest in such stock.

Volatility can be good in that if one shorts on the peaks, and buys on the

lows one can make money with greater money coming with greater

volatility. The possibility for money to be made via volatile market is how

short term market players like day traders hope to make money and is in

contrast to the long term investment view of buy and hold.

Cipla stock volatility was highest in 2004 and lowest in 1990. So selling

at peaks and buys on lows would help investor to earn more.

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BIBLIOGRAPHY

BIBLIOGRAPHY:

Reference Books:

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Norman G. Fosback (1998), Stock Market Logic,Tata Mc. Graw Hill

John C. Hull (fifth edition), Option, futures and Derivatives, Delhi: Pearson

education (Singapore) Pte. Ltd.

S. P. Gupta (2004) , Statistical Methods, New Delhi : Sultan Chand & Sons

Education Publications

Business Research Methods , Donald R Cooper & Pamela S. Schindler ,

Eight Edition , Tata Mc. GRAW-HILL EDITION

Websites:

http://en.wikipedia.org/wiki/volatility

http://en.wikipedia.org/wiki/standarddeviation

http://www.esignalcentra.com/support/futuresource/workstation/help/ch

arts/studies/wilders_volatility.htm

http://en.wikipedia.org/wiki/beta_coefficient

http://en.wikipedia.org/wiki/implied_volatility

http://www.trade10.com/volatility.htm

http://stockcharts.com/school/doku.php?id=chart_school:glossary_v

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http://en.wikipedia.org/wiki/volatilityhttp://en.wikipedia.org/wiki/standardhttp://www.esignalcentra.com/support/futuresource/workstation/help/charts/studies/wilders_volatility.htmhttp://www.esignalcentra.com/support/futuresource/workstation/help/charts/studies/wilders_volatility.htmhttp://en.wikipedia.org/wiki/beta_coefficienthttp://en.wikipedia.org/wiki/implied_volatilityhttp://www.trade10.com/volatility.htmhttp://stockcharts.com/school/doku.php?id=chart_school:glossary_vhttp://en.wikipedia.org/wiki/volatilityhttp://en.wikipedia.org/wiki/standardhttp://www.esignalcentra.com/support/futuresource/workstation/help/charts/studies/wilders_volatility.htmhttp://www.esignalcentra.com/support/futuresource/workstation/help/charts/studies/wilders_volatility.htmhttp://en.wikipedia.org/wiki/beta_coefficienthttp://en.wikipedia.org/wiki/implied_volatilityhttp://www.trade10.com/volatility.htmhttp://stockcharts.com/school/doku.php?id=chart_school:glossary_v


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http://www.incrediblecharts.com/technical/volume.htmhttp://www.incredi

blecharts.com/technical/chaikin_volatility.htm

www.matstat.com

www.statisticxl.com
http://www.incrediblecharts.com/technical/volume.htmhttp://www.incrediblecharts.com/technical/chaikin_volatility.htmhttp://www.incrediblecharts.com/technical/volume.htmhttp://www.incrediblecharts.com/technical/chaikin_volatility.htmhttp://www.matstat.com/http://www.statisticxl.com/http://www.incrediblecharts.com/technical/volume.htmhttp://www.incrediblecharts.com/technical/chaikin_volatility.htmhttp://www.incrediblecharts.com/technical/volume.htmhttp://www.incrediblecharts.com/technical/chaikin_volatility.htmhttp://www.matstat.com/http://www.statisticxl.com/

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