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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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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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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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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.
Maximum value 166.9094 on 28/4/2000
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
Maximum value 245.879 on 31/2/2000
Minimum value 0.2087 on 15/7/1994
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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.
Maximum value 136.6332 on 31/5/2006
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
Maximum value 90.2802 on 7/11/97
Minimum value 1.1402 on 15/6/90
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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
Maximum value 139.6924 on 31/5/2007
Minimum value 7.31368 on 29/11/1996
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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
Maximum value 84.2867 on 14/12/2006
Minimum value 1.2083 on 8/9/9
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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
Maximum value 0.3392 as on 14/5/2004
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
Maximum value 15.30199 as on 1/24/1997
Minimum value 0.1432 as on 3/24/2005
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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
Maximum value 0.3295 as on 4/29/1999
Minimum value 0.03461 as on 6/30/2004
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
Maximum value 0.2351 as on 10/4/07
Minimum value 0.00154 as on 8/5/2006
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
Minimum value 342666.65 on 6/1/1995
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
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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://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/8/3/2019 Final Project on Volatility_new
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