final project on volatility_new

8/3/2019 Final Project on Volatility_new

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CHAPTER-1

INDUSTRY

PROFILE

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HISTORICAL TREND

Mirada Group was incorporated in 1992 with the vision of

providing superior standards of Financial Services focusing on

professionalism, speed and ethics to a wider Corporate in the

subcontinent & overseas. The foundation is on "Value" Systems - "Value"

addition to Corporate, Retails and HNI Individuals through superior Wealth

Creation Practices. All actions are based on stringent "Values" - integrity,

confidentiality & commitment. "True Value" for money through a holistic

business practice. Finally, "Value" for client satisfaction, predominates our

relationship criteria.

"The company is 5th Leading retail broking house.*(D&B

Indias Leading equity Broking Houses 2008 Report). Ranked amongst

top 10 performers in BSE in the equity segments during the year 2009-10.

In 17 years, the company has emerged as one of Indias fastest growing

retail broking houses with retail market share at 2.73%.

The company is rated at P2+ and BBB+/stable by Crisil ratings

for the bank facilities for 200 crores. The company has 1000+ employees

strength is very talented, young and dynamic to take on any challenges in

future."

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

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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. Shareprices 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 market and, in general, on the smooth operation of financial

system functions. Financial stability is the raison d'tre of central banks.

Exchanges also act as the clearinghouse for each transaction,

meaning that they collect and deliver the shares, and guarantee payment

to the seller of a security. This eliminates the risk to an individual buyer or

seller that the counterparty could default on the transaction.

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ADVANTAGES

We have an extended web of experts from various domains

like law, marketing, economics which we draw upon from time-to-time, in

order to effectively meet the specific requirements of clients'

assignments.

Our broad and varied clientele spans several industries. Some

of them are in the Fortune 500 list. Our rich experience, in diversifiedindustries, helps us offer our clients practical solutions for their specific

business needs.

Realistic Assessment We take a hard look at our capacity and

resources prior to accepting an assignment. When we take on the job, we

know we can deliver.

Total Transparency We are entirely transparent: our associates as

well as our outsourced specialists deal directly with our clients. Full

Responsibility Once we accept the assignment we shoulder the

responsibility with complete dedication.

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Company profile

Name : Marwadi Shares and Finance Ltd.

Establishment : 1992

Head office Ltd. : Marwadi Shares and finance,

Nr. Kathiawad Gymkhana,

Dr, radhakrishan road.

Rajkot, 360001

Ph No : (0281) 2481313

E-Mail :[email protected]

Web Site : www.marwadionline.com

: www.msfl.com

Managing Director : Mr. Ketan Marwadi

Directors : Mr. Deven Marwadi

: Mr. Sandeep Marwadi

Deputy General Manager : Mr. Haresh Maniyar

CEO : Mr. Jay kumar A.S

Company Secretary :Mr. Tushit Mangaliya

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CHAPTER-3

THEORETICAL

ASPECTS

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CONCEPTUAL FRAME WORK

INTRODUCTION:

What is volatility?

The dictionary meaning of the word volatility means the rapid

changes or unpredictable.

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 volatilitymeans 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.

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Types of volatility:

1.Standard deviation:

Standard deviation of a probability distribution, random variable, orpopulation or multi-set of values is a measure of the spread of its

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

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 the

data 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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http://en.wikipedia.org/wiki/Sigma_(letter)http://en.wikipedia.org/wiki/Sigma_(letter)


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Example of 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 relativeto 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 of finance, the

implied volatility of contract i.e. option is the volatility implied by the

market price of the option based on an option pricing model.

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http://en.wikipedia.org/wiki/Financial_mathematicshttp://en.wikipedia.org/wiki/Volatilityhttp://en.wikipedia.org/wiki/Market_pricehttp://en.wikipedia.org/wiki/Valuation_of_optionshttp://en.wikipedia.org/wiki/Financial_mathematicshttp://en.wikipedia.org/wiki/Volatilityhttp://en.wikipedia.org/wiki/Market_pricehttp://en.wikipedia.org/wiki/Valuation_of_options


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7. Volatility smile:

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 distribution is taking place.

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

signaling uncertainty.

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http://www.incrediblecharts.com/technical/volume_patterns.htm#Distributionhttp://www.incrediblecharts.com/technical/bar_charts.htm#Uncertainhttp://www.incrediblecharts.com/technical/volume_patterns.htm#Distributionhttp://www.incrediblecharts.com/technical/bar_charts.htm#Uncertain


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

It is the volatility of a financial instrument based on historical

returns.

CHAPTER 4

RESEARCH

METHODOLOGY

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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.

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

sectors stocks/scripts through correlation.

2.3 Research methodology:

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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:

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 ofwww.bseindia.com.

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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.

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.)

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

This shows how study is useful

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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.

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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1.2 BSE monthly.

BSE monthly

0

200

400

600

800

1000

1200

1/29/1988

1/29/1990

1/29/1992

1/29/1994

1/29/1996

1/29/1998

1/29/2000

1/29/2002

1/29/2004

1/29/2006

Date

standarddeviationvalu

S. D. @

close

price

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

2006 can be considered to be the most volatile year.

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1.3 Cipla weekly

CIPLA weekly

0

100

200

300

400

500

600

5/1/1990

26/10/1990

16/8/1991

5/6/1992

7/5/1993

4/3/1994

23/12/1994

20/10/1995

16/8/1996

6/6/1997

27/3/1998

15/1/1999

7/11/1999

25/8/2000

15/6/2001

5/4/2002

24/1/2003

15/11/2003

15/10/2004

12/8/2005

2/6/2006

Date

standarddeviationvalue

S. D.@

close price

Inference:

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.

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1.4 Infosys weekly.

INFOSYS weekly

0

50

100

150

200

6/18/1993

6/18/1994

6/18/1995

6/18/1996

6/18/1997

6/18/1998

6/18/1999

6/18/2000

6/18/2001

6/18/2002

6/18/2003

6/18/2004

6/18/2005

6/18/2006

6/18/2007

Date

standarddeviatio

value

S.D.@

close

price

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

18/2/2000 and minimum is 0.46735 on 22/7/1993 whereas mean value is

24.5633.

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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.

Wider fluctuations can be seen in weekly volatility compare to monthly

this shows that consistent trend can be obtain in monthly volatilitycompare 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

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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).

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

(current close price previous close price) and its standard

deviation.

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

BSE monthly

0

200

400

600

800

1000

1/29/1988

1/29/1990

1/29/1992

1/29/1994

1/29/1996

1/29/1998

1/29/2000

1/29/2002

1/29/2004

1/29/2006

Date

value

S.D.

@t2-

t1

Maximum value 936.665 on 31/5/2006

Minimum value - 22.4038 on 24/2/1989

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Bse weekly

Bse weekly

0

500

1000

1500

2000

2500

3000

12/1/1989

12/1/1990

12/1/1991

12/1/1992

12/1/1993

12/1/1994

12/1/1995

12/1/1996

12/1/1997

12/1/1998

12/1/1999

12/1/2000

12/1/2001

12/1/2002

12/1/2003

12/1/2004

12/1/2005

12/1/2006

Date

value

S.D.@

t2-t1 cl

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

20/7/1990

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

Cipla weekly

0

100

200

300

400

500

Date

12/10

/1990

26/7

/1991

7/5

/1992

2/4

/1993

20/1

/1994

3/11

/1994

25/8

/1995

14/6

/1996

28/3

/1997

9/1

/1998

23/10

/1998

6/8

/1999

19/5

/2000

2/3

/2001

14/12

/2001

27/9

/2002

11/7

/2003

4/6

/2004

24/3

/2005

6/1

/2006

21/10

/2006

value

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

10/5/1990

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

Infosys monthly

0

50

100

150

200

6/30/1

993

6/30/1

994

6/30/1

995

6/30/1

996

6/30/1

997

6/30/1

998

6/30/1

999

6/30/2

000

6/30/2

001

6/30/2

002

6/30/2

003

6/30/2

004

6/30/2

005

6/30/2

006

6/30/2

007

Date

value

S.D.@t2-t1

cl.


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

Infosys weekly

0

50

100

150

200

250

300

6/

18/1993

6/

18/1994

6/

18/1995

6/

18/1996

6/

18/1997

6/

18/1998

6/

18/1999

6/

18/2000

6/

18/2001

6/

18/2002

6/

18/2003

6/

18/2004

6/

18/2005

6/

18/2006

6/

18/2007

Date

Value

S.D.@

t2-t1



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Reliance monthly :

Reliance monthly

0

50

100

150

1/29/1988

1/29/1990

1/29/1992

1/29/1994

1/29/1996

1/29/1998

1/29/2000

1/29/2002

1/29/2004

1/29/2006

Date

Value S.D.

@t2-

t1 cl.


Minimum value 3.3075 on 29/9/2000.

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Reliance weekly

Reliance weekly

0

20

40

60

80

100

1/8/1

988

1/8/1

990

1/8/1

992

1/8/1

994

1/8/1

996

1/8/1

998

1/8/2

000

1/8/2

002

1/8/2

004

1/8/2

006

Date

Value

S.D.@

t2-t1 cl



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SBI monthly

SBI monthly

0

20

40

60

80

100

120

140

160

3/31

/1994

3/31

/1995

3/31

/1996

3/31

/1997

3/31

/1998

3/31

/1999

3/31

/2000

3/31

/2001

3/31

/2002

3/31

/2003

3/31

/2004

3/31

/2005

3/31

/2006

3/31

/2007

Date

Value S.D.@

t2-t1

cl



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SBI weekly

SBI weekly

0

10

20

30

40

50

60

70

80

90

7/10/1994

26/5/1995

12/1/1996

23/8/1996

4/4/1997

1

4/11/1997

26/6/1998

5/2/1999

17/9/1999

28/4/2000

8/12/2000

20/7/2001

1/3/2002

1

1/10/2002

23/5/2003

2/1/2004

13/8/2004

1/4/2005

1

1/11/2005

25/6/2006

2/2/2007

Date

Value S.D.@

t2-t1 cl



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(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:

Bse Monthly

0

0.05

0.1

0.15

0.2

0.25

0.3

1/29/1988

1/29/1989

1/29/1990

1/29/1991

1/29/1992

1/29/1993

1/29/1994

1/29/1995

1/29/1996

1/29/1997

1/29/1998

1/29/1999

1/29/2000

1/29/2001

1/29/2002

1/29/2003

1/29/2004

1/29/2005

1/29/2006

1/29/2007

Date

Value

S.D.@t2/t1

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.

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Cipla weekly:

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

Cipla weekly

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Date

4/10/1990

12/7/1991

13/4/1992

5/3/1993

10/12/1993

23/9/1994

7/7/1995

18/4/1996

24/1/1997

30/10/1997

7/8/1998

14/5/1999

18/2/2000

24/11/2000

31/8/2001

7/6/2002

13/3/2003

19/12/2003

5/11/2004

19/8/2005

26/5/2006

Value S.D.

@t2/t1


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.

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Infosys weekly:

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

Infosys weekly

0

2

4

6

8

10

12

14

16

18

6/18/1993

6/18/1994

6/18/1995

6/18/1996

6/18/1997

6/18/1998

6/18/1999

6/18/2000

6/18/2001

6/18/2002

6/18/2003

6/18/2004

6/18/2005

6/18/2006

6/18/2007

Date

Value S.D.@

t2/t1



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Infosys Monthly:

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

monthly

Infosys monthly

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

6/30/1993

6/30/1994

6/30/1995

6/30/1996

6/30/1997

6/30/1998

6/30/1999

6/30/2000

6/30/2001

6/30/2002

6/30/2003

6/30/2004

6/30/2005

6/30/2006

6/30/2007

Date

Value S.D.@

t2/t1



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 Relianceweekly.

RIL weekly

0

0.05

0.1

0.15

0.2

0.25

1/8/1988

1/8/1989

1/8/1990

1/8/1991

1/8/1992

1/8/1993

1/8/1994

1/8/1995

1/8/1996

1/8/1997

1/8/1998

1/8/1999

1/8/2000

1/8/2001

1/8/2002

1/8/2003

1/8/2004

1/8/2005

1/8/2006

1/8/2007

Date

Value

S.D.@

t2/t1



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.

RIL monthly

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

1/29/1988

1/29/1989

1/29/1990

1/29/1991

1/29/1992

1/29/1993

1/29/1994

1/29/1995

1/29/1996

1/29/1997

1/29/1998

1/29/1999

1/29/2000

1/29/2001

1/29/2002

1/29/2003

1/29/2004

1/29/2005

1/29/2006

1/29/2007

Date

Value S.D.

@

t2/t1

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

on 8/15/2000.

SBI weekly:

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SBI weekly

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

4/3/1994

3/11/1994

14/7/1995

22/3/1996

22/11/1996

25/7/1997

27/3/1998

27/11/1998

30/7/1999

31/3/2000

1/12/2000

3/8/2001

5/4/2002

6/12/2002

8/8/2003

8/4/2004

10/12/2004

19/8/2005

21/4/2006

22/12/2006

Date

Valu

eS.D. @

t2/t1

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

SBI monthly

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

3/31/199

4

3/31/199

5

3/31/199

6

3/31/199

7

3/31/199

8

3/31/199

9

3/31/200

0

3/31/200

1

3/31/200

2

3/31/200

3

3/31/200

4

3/31/200

5

3/31/200

6

3/31/200

7Date

Value

S.D.@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 intra day trading.

2.1 BSE monthly:

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BSE monthly

0

50

100

150

200

250

300

350

400

450

1/29/1988

1/29/1990

1/29/1992

1/29/1994

1/29/1996

1/29/1998

1/29/2000

1/29/2002

1/29/2004

1/29/2006Date

Valu

e

chaikinsvolatility

High value of chaikins volatility indicates that there are wide fluctuations

during intra day.

2.2 BSE weekly:

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bse weekly

0

100

200

300

400

12/1/1989

12/1/1990

12/1/1991

12/1/1992

12/1/1993

12/1/1994

12/1/1995

12/1/1996

12/1/1997

12/1/1998

12/1/1999

12/1/2000

12/1/2001

12/1/2002

12/1/2003

12/1/2004

12/1/2005

12/1/2006

Va

lue

chai

kins

volat

ility

2.3 Cipla weekly:

CIPLA weekly

0

400

800

1200

1600

Date

27/9/1990

28/6/1991

27/3/1992

5/2/1993

5/11/1993

12/8/1994

19/5/1995

23/2/1996

22/11/1996

22/8/1997

22/5/1998

19/2/1999

19/11/1999

18/8/2000

18/5/2001

15/2/2002

15/11/2002

14/8/2003

25/6/2004

1/4/2005

30/12/2005

29/9/2006

chaikins%

ch

aik

ins

(%

)

2.4 Infosys weekly:

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Infosys weekly

0

100

200

300

400

500

6/18/93

6/18/94

6/18/95

6/18/96

6/18/97

6/18/98

6/18/99

6/18/00

6/18/01

6/18/02

6/18/03

6/18/04

6/18/05

6/18/06

6/18/07

Date

value

chaikins

2.5 Infosys monthly:

INFOSYS monthly

0

100

200

300

400

500

600

6/30/1993

6/30/1994

6/30/1995

6/30/1996

6/30/1997

6/30/1998

6/30/1999

6/30/2000

6/30/2001

6/30/2002

6/30/2003

6/30/2004

6/30/2005

6/30/2006

6/30/2007

Date

valu

echaikins(%)

Ma

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

31/8/1995

3.Volume volatility:

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BSE SENSEX volume data are not available.

3.1 cipla S.D. @ volume

CIPLA weekly

0

500000

1000000

1500000

2000000

2500000

Date

18/10/1990

9/8/1991

29/5/1992

30/4/1993

27/2/1994

16/12/1994

13/10/1995

9/8/1996

30/5/1997

20/3/1998

8/1/1999

29/10/1999

18/8/2000

8/6/2001

28/3/2002

17/1/2003

7/11/2003

9/10/2004

5/8/2005

26/5/2006

standarddev.value

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.

3.2 Infosys weekly:

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Infosys weekly

0

500000

1000000

1500000

2000000

6/18/1993

6/18/1994

6/18/1995

6/18/1996

6/18/1997

6/18/1998

6/18/1999

6/18/2000

6/18/2001

6/18/2002

6/18/2003

6/18/2004

6/18/2005

6/18/2006

6/18/2007

Date

va

lue

S.D.@

volume

3.3 Infosys monthly:

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infosys monthly

0

500000

1000000

1500000

2000000

2500000

3000000

3500000

4000000

4500000

6/30/1993

6/30/1994

6/30/1995

6/30/1996

6/30/1997

6/30/1998

6/30/1999

6/30/2000

6/30/2001

6/30/2002

6/30/2003

6/30/2004

6/30/2005

6/30/2006

6/30/2007

Date

value

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.

3.4 Reliance weekly:

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Reliance weekly

0

5000000

10000000

15000000

20000000

25000000

30000000

35000000

1/8/1988

1/8/1990

1/8/1992

1/8/1994

1/8/1996

1/8/1998

1/8/2000

1/8/2002

1/8/2004

1/8/2006

Date

value S.D.@

volume

Maximum value 3160102.65 on 13/4/2006


3.5 Reliance monthly

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Reliance monthly

0

10000000

20000000

30000000

40000000

50000000

60000000

70000000

1/29/88

1/29/90

1/29/92

1/29/94

1/29/96

1/29/98

1/29/00

1/29/02

1/29/04

1/29/06

Date

value

S.D.@

volume

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

considered to be the outliners.

4. CORRELATION ANALYSIS:

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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.

R2= bxy*byx

5.1 BSE Weekly:

Bse weekly

0

0.2

0.4

0.6

0.8

1

1.2

12/1/1989

12/1/1990

12/1/1991

12/1/1992

12/1/1993

12/1/1994

12/1/1995

12/1/1996

12/1/1997

12/1/1998

12/1/1999

12/1/2000

12/1/2001

12/1/2002

12/1/2003

12/1/2004

12/1/2005

12/1/2006

Date

Value

R2 @ cl

n std

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 R2 is 0.93368, on 11/26/93 R2 is 0.965475, on

1/9/04 R2 is 0.963724. This shows strong relationship between closing

price and its standard deviation.

On the other hand on 5/25/90 R2 is 0.002199, on 12/1/00 R2 is

0.000151 this shows far away relationship between closing price and its

standard deviation.

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BSE WEEKLY

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

12/1/1989

12/1/1990

12/1/1991

12/1/1992

12/1/1993

12/1/1994

12/1/1995

12/1/1996

12/1/1997

12/1/1998

12/1/1999

12/1/2000

12/1/2001

12/1/2002

12/1/2003

12/1/2004

12/1/2005

12/1/2006

date

R2 R2 @

t2-t1n std

The above is the graph of R2 between 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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BSE weekly

0

0.2

0.4

0.6

0.8

1

1.2

12/1/1989

12/1/1990

12/1/1991

12/1/1992

12/1/1993

12/1/1994

12/1/1995

12/1/1996

12/1/1997

12/1/1998

12/1/1999

12/1/2000

12/1/2001

12/1/2002

12/1/2003

12/1/2004

12/1/2005

12/1/2006

Date

R2

R2

ratiostd cl

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 closingprice 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:

Bse monthly

0

0.2

0.4

0.6

0.8

1

1.2

1/

29/1988

1/

29/1989

1/

29/1990

1/

29/1991

1/

29/1992

1/

29/1993

1/

29/1994

1/

29/1995

1/

29/1996

1/

29/1997

1/

29/1998

1/

29/1999

1/

29/2000

1/

29/2001

1/

29/2002

1/

29/2003

1/

29/2004

1/

29/2005

1/

29/2006

1/

29/2007

date

R2

R2

cl

std

The above is the graph of the R2 between closing price and its standard

deviation.

BSE monthly

0

0.2

0.4

0.6

0.8

1

1/29/1988

1/29/1990

1/29/1992

1/29/1994

1/29/1996

1/29/1998

1/29/2000

1/29/2002

1/29/2004

1/29/2006

Date

R2

R2 @

(T2-T1)

n its

std

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The above is the graph of R2 between absolute value i. e. t2-t1 and its

standard deviation.

BSE MONTHLY

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1/29/1988

1/29/1989

1/29/1990

1/29/1991

1/29/1992

1/29/1993

1/29/1994

1/29/1995

1/29/1996

1/29/1997

1/29/1998

1/29/1999

1/29/2000

1/29/2001

1/29/2002

1/29/2003

1/29/2004

1/29/2005

1/29/2006

1/29/2007

DATE

R2

R2 @

t2/t1 n

std

The above is the graph of the R2 between t2/t1 and its standard deviation.

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5.3 Cipla weekly:

cipla weekly

0

0.2

0.4

0.6

0.8

1

1.2

Date

7/9/1990

17/5/1991

24/1/1992

30/1

0/1992

22/7/1993

8/4/1994

16/1

2/1994

1/9/1995

17/5/1996

24/1/1997

3/1

0/1997

12/6/1998

19/2/1999

29/1

0/1999

7/7/2000

16/3/2001

23/1

1/2001

2/8/2002

11/4/2003

19/1

2/2003

9/1

0/2004

24/6/2005

3/3/2006

10/1

1/2006

r2ofclosenstddev

The above is the graph of R2 between close price and its standard

deviation.

Cipla weekly

0

0.2

0.4

0.6

0.8

1

1.2

Date

18/10/1990

9/8/1991

29/5/1992

30/4/1993

27/2/1994

16/12/1994

13/10/1995

9/8/1996

30/5/1997

20/3/1998

8/1/1999

29/10/1999

18/8/2000

8/6/2001

28/3/2002

17/1/2003

7/11/2003

9/10/2004

5/8/2005

26/5/2006

R2 R2 cl

ratio n

std

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The above is the graph of R2 between t2/t1 and its standard deviation.

The maximum values are between 0.1 and 0.2.

Cipla weekly

0

0.2

0.4

0.6

0.8

1

1.2

Date

1/11/19

90

6/9/19

91

7/8/19

92

25/6/19

93

6/5/19

94

17/3/19

95

25/1/19

96

29/11/19

96

3/10/19

97

7/8/19

98

11/6/19

99

13/4/20

00

16/2/20

01

21/12/20

01

25/10/20

02

29/8/20

03

13/8/20

04

24/6/20

05

29/4/20

06

R2

R2 cl.

Diff n

std

The above is the graph of the R2 between t2-t1 and its standard deviation.

Maximum values falls between 0.1 and 0.2.

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5.4 Infuses weekly:

Infosys weekly

0

0.2

0.4

0.6

0.8

1

1.2

6

/18/1993

6

/18/1994

6

/18/1995

6

/18/1996

6

/18/1997

6

/18/1998

6

/18/1999

6

/18/2000

6

/18/2001

6

/18/2002

6

/18/2003

6

/18/2004

6

/18/2005

6

/18/2006

6

/18/2007

Date

R2 R2

cl

std

The above is the graph of R2 between close price and its standard

deviation. Maximum value falls between 0.2 and 0.6.

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5.5 Reliance weekly:

RELIANCE weekly

0

0.2

0.4

0.6

0.8

1

1.2

1/8/1988

1/8/1989

1/8/1990

1/8/1991

1/8/1992

1/8/1993

1/8/1994

1/8/1995

1/8/1996

1/8/1997

1/8/1998

1/8/1999

1/8/2000

1/8/2001

1/8/2002

1/8/2003

1/8/2004

1/8/2005

1/8/2006

1/8/2007

Date

value

R2 @close

n S.d

The above is the graph of the R2 between t2/t1 and its standard deviation.

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

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regression represent stock well with the closing price and standard

deviation.

CHAPTER 6

RECOMMENDATION

S

&

FINDING

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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

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.

Short term market players, can grasped the opportunity by selling cipla

stocks when volatility of cipla was highest in 2004 and can buy when

volatility was lowest during year 1990.

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Findings:

BSE SENSEX index faced highest volatility in the year 2010

Reliance monthly does not shows wide fluctuations as weekly, after

the period of 2009 in monthly data, volatility does not reaches

below maximum bottom. There is increase in volatility at adiminishing rate.

The cipla is considered to be attaining the highest volatility

i.e.541.1841 in the year 2008 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.

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CHAPTER 7

CONCLUSION

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CONCLUSIONS:

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.

1. Chaikins Volatility peak occurs as the market retreats from a new high

and enters a trading range.

2. 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.

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

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

5. 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. An increase in

volatility may well mark a turn in the direction of a bearish trend and over

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longer term, a decrease in volatility would indicate the possibility of a

significant bull market top being approached.

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.

Volatility approaches bottom immediately after top being

approached. So when volatility is low than it can be said that trend

fluctuations has dried out.

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.

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CHAPTER 8

BIBLIOGRAPHY

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Websites:

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

http://en.wikipedia.org/wiki/standard deviation

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

ts/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

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

lecharts.com/technical/chaikin_volatility.htm

www.matstat.com

www.statisticxl.com

70
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://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://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://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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