Pristine quant-frm demo-annotations

www.edupristine.com

FRM Part I
Quantitative Analysis
21st May 2011

© Neev Knowledge Management – Pristine

Seminar Material
Not for Sale
Agenda
• Introduction and context
• Understanding the FRM Examination Structure
• Introduction to Quantitative Analysis
– Probability Distributions
– Key Concept Checkers
• Complete Offering & Registration
• Next Seminar

© Neev Knowledge Management – Pristine 2 www.edupristine.com

Pristine has been started by professionals with diverse experience in Seminar Material
Not for Sale
financial services, IT and Auto who are alumnus of IITs & IIMs

Innovative content – To improve
understanding & learning capability of
students. VisualizeFRM,
VisualizeCFA as one of the best
selling products

Topic Expert Model (TEM ) – Industry
professionals bring invaluable industry
perspective for students. Pool of 300+
working professionals as active faculty
members with the likes of CFA regional
directors, Presidents of various banks Pristine
Classroom/ Online delivery
(synchronous and
Founded with an aim asynchronous) – To increase
of creating world reach and improve efficiency of
class professionals in learning. Conducted 15+
the area of finance – batches with over 300 hours of
particularly risk recorded content
management and
Testimonial - 53% of the students join us on the investment banking
basis of referral is a testimonial of the effective Effective training methodologies to improve the performance
training methodologies of the students and enhance the employability


Seminar Material
Key Authorization Not for Sale

GARP (2007-10)
CFA Institute (2010-11)
Authorized Training provider -FRM
Authorized Training provider – CFA
Largest player in India in the area of risk
Pristine is now the authorized training provider
management training. Trained 1000+ students
for CFA Exam trainings . Pristine is largest
in risk management
training provider for CFA in India with presence
across seven major cities.

PRMIA (2009-10)
FPSB India (2010-11) Authorized Training provider – PRM/ APRM
Authorized Training provider -CFP Sole authorized training for PRM Training in
An authorized Education Provider for India. Largest player in India in the area of risk
Chartered Financial Planner Charter. management training. Trained 1000+ students
in risk management

© Neev Knowledge Management – Pristine www.edupristine.com

Seminar Material
Key Associations* Not for Sale

Bank Of America J. P. Morgan (2010) Mizuho (2010)
Continuum Solutions (2010) Financial Modeling in Excel Financial Modeling in Excel
Financial Modeling in Excel The Real Assets Group were trained in Bankers were using excel models that
Associates were trained on valuation and Excel for infrastructure and real-estate they could not understand. Conducted
mergers and acquisitions modeling financial modeling in Excel trainings to
bridge the gap

Franklin Templeton Credit-Suisse India (2009) HSBC (2008)
CFA (2010) Risk Management and Quant. Analysis Risk Management and Quant. Analysis
Students were facing a gap in the IT Professionals of Credit-Suisse India New joinees in HSBC had a gap in
overall understanding of finance were trained on risk management. knowledge of Risk Management and
topics like corporate finance, FSA and quantitative skills. Conducted trainings
valuation. Provided training for over (On campus) to bridge the gap
100 hours to bridge the gap

*Indicative List


Seminar Material
…Key Associations Not for Sale

FMS Delhi (2010-11) IIM Calcutta (2010-11) BITS Pilani (2009)
Financial Modeling in Excel Financial Modeling in Excel
Workshops on Basics of Finance
Final Year MBA students of Faculty of Students about to go for internships and
Management Studies, Delhi University Most of the students desire a career
join jobs found a gap in their grasp of
were trained in financial modeling so as knowledge of excel for financial in finance. Conducted training for
to prepare them better for a job in modeling. Conducted training for 75+ 350+ students with an average rating
finance. students with an average rating of 4.5+ of 4.5+

NISM (2008)
Derivatives workshop for Hedging
IIT Delhi (2009) Sydneham College (2009)
Corporate in Ludhiana incurred huge
Corporate finance Financial Modeling in Excel
losses because of derivative trades
Students get placed in finance Students about to join jobs found a (for hedging). Conducted trainings for
companies (UBS, GS, MS, etc) with gap in their grasp of excel for financial directors and CFOs for better
no understanding of the subject/ Job modeling. Conducted 40+ hours of understanding of derivative products
Profile. Conducted workshop to training and helped students be ready
bridge the gap for job


Seminar Material
Not for Sale
Trainer
Pawan Prabhat, Director and Faculty, Pristine

Pawan has experience in the area of risk management and investment banking. He has worked in
the area of risk management and investment banking. Pawan is a co-founder of Pristine and has earlier
worked in senior management positions in Crisil – S&P and Standard Chartered Securities. He has
also worked in the IT industry in companies like Geometric Software and Wipro.
Pawan has successfully managed various IPOs worth more than 300 crores and has been the main
point of contact with the promoters and the funds. Some of the IPOs in which has played instrumental
roles are
• Insecticide India
• Nitin Fire
• Nelcast
• Barak Valley
• Precision Pipes
• Indowind
Pawan has done his MBA from IIM Indore and is a B. Tech from IIT Bombay in mechanical
engineering. Pawan has published research paper and has co-authored articles on risk management in
national finance daily- Hindu Business Line
He is an avid reader and has been involved in dramatics, quizzing and bridge.


Seminar Material
Not for Sale
Agenda
• Next Seminar


Seminar Material
Not for Sale
FRM 2011

Area Weight
Foundations of Risk Management 20%
Quantitative Analysis 20%
Financial Markets and Products 30%
Valuation and Risk Models 30%

100 MCQ, 4 hours test in pen and paper, May21, 2011

© Neev Knowledge Management – Pristine Careers 9 www.edupristine.com

Seminar Material
Not for Sale
FRM Exam Statistics

2009 FRM Sample Paper 2010 FRM Sample Paper

12% Foundation of Risk 12% Foundation of Risk
Management Management
28%
35% Quantitative Analysis Quantitative Analysis
23% 20%
Financial Markets and Financial Markets and
Products Products
Valuation and Risk Models Valuation and Risk Models

30% 40%

• From the trend seen in FRM exam, 65%-70% of the questions come from two topics, Financial
Markets and Products, and Valuation and Risk Model.
• In FRM Part-I, our estimate is that if a candidate manages to earn Quartile-1 scores in Financial
Markets and Products, and Valuation and Risk Model, one should comfortably pass the exam even if
the candidate does not do so well in QA and Foundation of Risk Management.

Source: GARP Sample Paper


Seminar Material
Not for Sale
Numerical vs Subjective Questions

2009 2010
9 12
8 10
7
6 8
5 6
4
3 4
2 2
1
0 0
Foundation of Financial Foundation of Financial
Quantitative Valuation and Quantitative Valuation and
Risk Markets and Risk Markets and
Analysis Risk Models Analysis Risk Models
Management Products Management Products
Numerical 3 6 8 4 Numerical 1 4 5 11
Subjective 2 2 8 3 Subjective 4 5 7 3

• There is no fixed ratio of Numerical and subjective questions in any of the subject.
• In valuation and risk models, numerical questions are favorite to GARP
• To score in Quartile-1 in Financial Markets and Products, and Valuation and Risk Models you need to
solve as many questions as you can.



Seminar Material
Not for Sale
Number of Questions Year-wise

18
16
14
Number of Questions

12
10
8
2010
6
2009
4
2
0
Foundation of Quantitative Financial Valuation and
Risk Analysis Markets and Risk Models
Management Products



Seminar Material
Not for Sale
Agenda
• Next Seminar


Seminar Material
Not for Sale


Statistics and Probability Sampling & Regression EWMA
Probability Distributions Hyp. Testing Analysis GARCH

• Basics of • Properties of • Standard • Linear • Estimating
Probability Distributions Error Regression Volatility and
• Population – Discrete/ • Formulating • Multiple Correlation
and Sample Continuous Hypothesis Regressors • Monte Carlo
Statistics • Binomial • Type I and II • OLS • Volatility Term
Distribution Errors • Error Analysis Structures
• Normal • Heteroscedac
Distribution ity

Reference Book - James Stock and Mark
Watson, Introduction to Econometrics, Brief
Edition (Boston: Pearson Education, 2008).


Seminar Material
Not for Sale
Probability Distribution
• A Random Variable is a function, which assigns unique numerical values to all possible outcomes of
a random experiment under fixed conditions. A random variable is not a variable but rather a function
that maps events to numbers
– Probability distribution describes the values and probabilities that a random event can take place. The
values must cover all of the possible outcomes of the event, while the total probabilities must sum to
exactly 1, or 100%
• Example
– Suppose you flip a coin twice.
– There are four possible outcomes: HH, HT, TH, and TT.
– Let the variable X represent the number of Heads that result from this experiment
– It can take on the values 0, 1, or 2.
– X is a random variable (its value is determined by the outcome of a statistical experiment)
– A probability distribution is a table or an relation that links each outcome of a statistical experiment with
its probability of occurrence
Number of heads (X) Probability P(X=x)

0 0.25

1 0.50

2 0.25


Seminar Material
Not for Sale
Continuous & Discrete Probability Distributions
• If a variable can take on any value between two specified values, it is called a continuous variable
– otherwise, it is called a discrete variable
• If a random variable is a discrete variable, its probability distribution is called a discrete probability
– For example, tossing of a coin & noting the number of heads (random variable) can take a discrete value
– Binomial probability distribution, Poisson probability distribution
• If a random variable is a continuous variable, its probability distribution is called a continuous
probability distribution
– The probability that a continuous random variable will assume a particular value is zero
– A continuous probability distribution cannot be expressed in tabular form.
– An equation or formula is used to describe a continuous probability distribution (called a probability
density function or density function or PDF)
– The area bounded by the curve of the density function and the x-axis is equal to 1, when computed over
the domain of the variable
– Normal probability distribution, Student's t distribution are examples of continuous probability
distributions


Seminar Material
Not for Sale
Normal Distribution

68% of Data

95% of Data

99.7% of Data

Only Mean and Standard Deviation is required to fully understand a distribution


Seminar Material
Not for Sale
Normal (Gaussian) Distribution
• The normal distribution is defined by first two moments, mean() and variance(2)
• The probability density function P(x) of normally distributed variable is given by:
1  ( x   )2 
P( x)  exp   
2 2
 2 2 

• The probability of the value lying between a and b is given by:
b
P (a  X  b )   P ( x ). dx
a

• The expected value of a normally distributed variable: E[X]= ,
• The variance of normally distributed variable: Var(X)= 2
• If two variables are individually normally distributed, then the linear combination of the both is
also normally distributed.
– Lets take an example of two variable X1 and X2 which are normally distributed such that:
– X1~N(1,1) and X2~N(2,2)
– Then X= a.X1+ b.X2 is also normally distributed.

The skewness of normal distribution is = 0 and the kurtosis is =3


Seminar Material
Not for Sale
FRM Exam 2008

• Which type of distribution produces the lowest probability for a variable to exceed a specified extreme
value ‘X’ which is greater than the mean assuming the distributions all have the same mean and
variance?
A. A leptokurtic distribution with a kurtosis of 4.
B. A leptokurtic distribution with a kurtosis of 8.
C. A normal distribution.
D. A platykurtic distribution


Seminar Material
Not for Sale
Answer

• ANSWER: D
– By definition, a platykurtic distribution has thinner tails than both the normal distribution and any leptokurtic
distribution. Therefore, for an extreme value X, the lowest probability of exceeding it will be found in the
distribution with the thinner tails.
– A. Incorrect. A leptokurtic distribution has fatter tails than the normal distribution. The kurtosis indicates
the level of fatness in the tails, the higher the kurtosis, the fatter the tails. Therefore, the probability of
exceeding a specified extreme value will be higher .
– B. Incorrect. Since answer A. has a lower kurtosis, a distribution with a kurtosis of 8 will necessarily
produce a larger probability in the tails.
– C. Incorrect. By definition, a normal distribution has thinner tails than a leptokurtic distribution and larger
tails than a platykurtic distribution.
0.45 Platykurtic
Mesokurtic 0.4
K<3
K=3 0.35

0.3

0.25

0.2
Leptokutic
0.15
K>3
0.1

0.05

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


Seminar Material
Not for Sale
FRM Exam 2006

• Which of the following statements is the most accurate about the relationship between a normal
distribution and a Student’s t-distribution that have the same mean and standard deviation?
A. They have the same skewness and the same kurtosis.
B. The Student’s t-distribution has larger skewness and larger kurtosis.
C. The kurtosis of a Student’s t -distribution converges to that of the normal distribution as the number of
degrees of freedom increases.
D. The normal distribution is a good approximation for the Student’s t-distribution when the number of
degrees of freedom is small.


Seminar Material
Not for Sale
Answer

• ANSWER: C
– The skewness of both distributions is zero and the kurtosis of the Student’s t distribution converges to that
of the normal distribution as the number of degrees of freedom increases.


Seminar Material
Not for Sale
FRM Exam 2006

• Which one of the following statements about the normal distribution is NOT accurate?
A. Kurtosis equals 3.
B. Skewness equals 1.
C. The entire distribution can be characterized by two moments, mean and variance.
D. The normal density function has the following expression:


Seminar Material
Not for Sale
Answer

• ANSWER: B
– The skewness of the normal distribution is 0, not 1.
– The kurtosis of the normal distribution is 3, the normal distribution can be completely described by its mean
and variance, and the density function of the normal distribution is as shown.

68% of Data

95% of Data

99.7% of Data

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


Seminar Material
Not for Sale
FRM Exam 2007

• Let Z be a standard normal random variable. An event X is defined to happen if either z takes a value
between –1 and 1 or z takes any value greater than 1.5. What is the probability of event X happening
if N(1) = 0.8413, N(0.5) = 0.6915 and N(-1.5) = 0.0668, where N() is the cumulative distribution function
of a standard normal variable?
A. 0.083
B. 0.2166
C. 0.6826
D. 0.7494


Seminar Material
Not for Sale
Answer
• ANSWER: D
– Let A be the event that z takes a value between 1 and –1 and B be the event that z takes a value greater
than 11/2 . The probability of z being between 1 and –1 is the area under the standard normal curve
between 1 and -1. From the properties of a standard normal distribution, we know that:
N(-1) = 1.0 - N(1) = 1.0 – 0.8413 = 0.1587
– Therefore, the probability of z being between 1 and –1 = P(A) = N(1) - N(-1) = 0.6826
– The probability of z being greater than 11/2 = P(B) = 1 - N(11/2) = N(-11/2) = 0.0668
– Event X = A U B and P(X) = P(A) + P(B) since A and B are mutually exclusive.
– Hence, P(X) = 0.7494 0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1

0.05

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


Seminar Material
Not for Sale
FRM Exam 2007

• When can you use the Normal distribution to approximate the Poisson distribution, assuming you have
"n" independent trials each with a probability of success of "p"?
A. When the mean of the Poisson distribution is very small.
B. When the variance of the Poisson distribution is very small.
C. When the number of observations is very large and the success rate is close to 1.
D. When the number of observations is very large and the success rate is close to 0.


Seminar Material
Not for Sale
Plots of Poisson Distribution

0.4 λ=1 0.2
0.35 0.18 λ=5
0.16
0.3
0.14
0.25 0.12
0.2 0.1
0.15 0.08
0.1 0.06
0.04
0.05
0.02
0 0
0 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28

0.12 λ=15 0.09
λ=25
0.1 0.08
0.07
0.08 0.06
0.05
0.06
0.04
0.04 0.03
0.02
0.02
0.01
0 0
0 2 4 6 8 10 12 14 16 18 20 22 24 0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54


Seminar Material
Not for Sale
Answer

• ANSWER: C
– The Normal distribution can approximate the distribution of a Poisson random variable with a large lambda
parameter (λ). This will be the case when both the number observations (n) is very large and the success
rate (p) is close to 1 since λ = n*p.
– INCORRECT: A, The mean of a Poisson distribution must be large to allow approximation with a Normal
distribution.
– INCORRECT: B, The variance of a Poisson distribution must be large to allow approximation with a
Normal distribution.
– INCORRECT: D, The Normal distribution can approximate the distribution of a Poisson random variable
with a large lambda parameter (λ). But since λ = n*p, where n is the number observations and p is the
success rate, λ will not be large if p is close to 0.


Seminar Material
Not for Sale
Questions 14 – FRM Exam 2006

• If Y = ln(X) and Y is normally distributed with zero mean and 2.33 standard deviation. What is the
expected value of X?
A. 15.10
B. 3.21
C. 227.90
D. 1


Seminar Material
Not for Sale
Answer

• ANSWER: A

Lognormal Distribution


Seminar Material
Not for Sale
What is VaR ?

• Value at Risk (VaR) has become the standard measure that financial analysts use to
quantify this risk

• VAR represents maximum potential loss in value of a portfolio of financial instruments
with a given probability over a certain horizon

• In simpler words, it is a number that indicates how much a financial institution can lose
with probability θ over a given time horizon

www.edupristine.com

Seminar Material
Not for Sale
Agenda
• Next Seminar


Seminar Material
Not for Sale
About FRM Prep School

School for FRM Part I Prep is
a 100 Hrs extensive training program*
that can enable you
to prepare for and crack FRM Part I Examination

www.edupristine.com

Seminar Material
Not for Sale
About School for FRM Prep

• Extensive 100 Hours coverage
• 10 days of regular classes

• 3 days of revision classes
School for FRM Prep is
• 2 Mock tests

that can enable you = • Extensive Question Bank to
prepare and Practice
to prepare for and crack FRM Part I Exam • 2 Hrs of one-to-one doubt
clearing sessions*

• Qualified faculty with extensive
industry and teaching experience

www.edupristine.com

Seminar Material
Not for Sale
About School for FRM Prep

• Proven credentials in successfully
training FRM aspirants

• Actionable and Innovative Material
School for FRM Prep is
• Complete Slide Pack

that can enable you = •

•
Each Session followed by Quiz

Adaptive feedback based on Quiz
to prepare for and crack FRM Part I Exam • Mock tests and feedback

• Individual doubt solving session

• FRM Visualized Formula Charts

• Summarized Recordings for
revision

www.edupristine.com

Seminar Material
Not for Sale
Tentative Schedule – Feb-March

Date Day Course Topic
26/Feb/11 Sat FRM-Part-I Quantitative Analysis - I
27/Feb/11 Sun FRM-Part-I Quantitative Analysis – II
05/Mar/11 Sat FRM-Part-I Quantitative Analysis – III
06/Mar/11 Sun FRM-Part-I Quantitative Analysis – IV
12/Mar/11 – Subject to Change
* Indicative list Sat FRM-Part-I FMP -I
13/Mar/11 Sun FRM-Part-I FMP-II
26/Mar/11 Sat FRM-Part-I FMP-III
27/Mar/11 Sun FRM-Part-I FMP-IV
02/Apr/11 Sat FRM-Part-I VaR- I
03/Apr/11 Sun FRM-Part-I VaR- II

www.edupristine.com

Seminar Material
Not for Sale
How it works?

1 2 3 4 5

You signup for the program by making payment of USD 600*

*Early Bird Discount of USD
100 for registrations before
10thFeb

www.edupristine.com

Seminar Material
Not for Sale
How it works?

1 2 3 4 5

Start Preparation with material and Live Interactive Class

www.edupristine.com

Seminar Material
Not for Sale
How it works?

1 2 3 4 5

Work on the Problem sets/ Quizzes adapting preparation Style

www.edupristine.com

Seminar Material
Not for Sale
How it works?

1 2 3 4 5

Give Mock Tests/ Ask Doubts/ Revise and Complete Preparation

www.edupristine.com

Seminar Material
Not for Sale
How it works?

1 2 3 4 5

Plan and Achieve Success in FRM Part I Exam

www.edupristine.com

Seminar Material
Not for Sale
Methodology

Each topic will be explained through

Conceptual Discussion, Examples, Tests, Quizzes, Actionable
Presentations, Visualized Charts and Q&A

www.edupristine.com

Seminar Material
Not for Sale
Sample Innovative Material
Probability
Distributions

Normal Distribution Binomial Distribution

Normal Skewness and
Z-Score
Distribution Kurtosis

• Described by mean & variance No. of σ a given
• Symmetric about its mean observation is away
• Standard Normal Distribution from population mean.
- Mean = 0; Variance =1 Z=(x-µ)/σ

Q. At a particular time, the market value
of assets of the firm is $100 Mn and the
market value of debt is $80 Mn. The
68% of Data standard deviation of assets is $ 10 Mn.
What is the distance to default?
Ans. z = (A-K) / σA
95% of Data = (100-80)/10
=2
99.7% of Data

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

Q. Which of the following is likely to be a probability distribution
function?
For X=[1,2,3,4,5], Prob[Xi]= 49/(75-Xi2)
If Z is a standard normal R.V. An event X is defined to happen if either -1< Z < 1 or For X=[0,5,10,15], Prob[Xi]= Xi/30
Z > 1.5. What is the prob. of event X happening if N (1) =0.8413, N (0.5) = 0.6915 For X=[1,4,9,16,25], Prob[Xi]= [(X i)1/2 – 1]/5
and N (-1.5) = 0.0668, where N is the CDF of a standard normal variable?
Ans. P(X)= P(-1< Z < 1) + P(Z > 1.5) Ans. The correct answer is For X=[0,5,10,15], Prob[Xi]= Xi/30
= N(1)-(1-N(1)) + N(-1.5)
= 2*0.8413-1 + 0.0668 For all values of X, probability lies within [0,1] and sum of all the
= 0.7494 probabilities is equal to 1.
-1 +1 1.5


Seminar Material
Not for Sale
Sample Innovative Material
Hypothesis
Testing

Null Alternative Confidence Intervals Hypothesis Tests One tailed
Two Tailed test
HYPOTHESIS:H0 Hypothesis: Ha (CI) for Variances Test

Hypothesis that Concluded if there is Range of values within which Test if the value is greater Test if the value is
the researcher significant evidence H0 Cannot be rejected (say than or less than K different from K
wants to reject to reject H0 90% or 95%). H0; µ<=K vs. Ha: µ>K H0; µ=0 vs. Ha: µ≠ 0
Known variance, 2 Tailed test,
Type 1 error: rejection of H0 when it CI is: X”± zα/2(σ/√t) 0.2 0.2

is actually true 0.15 0.15

Type 2 error :Fail to reject H0 when 0.1
α= 0.05
0.1

it is actually false α= 0.025
α= 0.025
0.05 0.05
Inference Real State of Affairs
Based on 0 0
Sample Data H0 is True H0 is False -5 Z=0 0 Z=2.5
5
Reject H0 -5
Z=0
Reject H0
Do not Reject H0
Type II error Do not Reject H 0
Reject H0

Correct decision
H0 is True
Confidence level = 1- 
P (Type II error) = 
Correct decision
Tests for a Single Tests for a two
Type I error Population Variances Population Variances Q. If standard deviation of a
H0 is False
Significance level = * Power = 1- normal population is known to be
10 & the mean is hypothesized
*Term  represents the maximum probability of committing a
Type I error Chi-Square test F test to be 8. Suppose a sample size
of 100 is considered. What is the
range of sample means in which
hypothesis can be accepted at
Q. Co. ABC would give bonus to employees, if they get a H0: σ2 = c H0: σ12 – σ22 = 0 significance level of 0.05?
HA: σ2 ≠ c HA: σ12 – σ22 ≠ 0 Ans: SE =  = 10/√100 =1
rating higher than 7/10 from customers. A random sample n
of 30 customers is conducted with rating of 7.1/10. (n  1)s 2 s2 z = (x-µ)/ SE
2  F 1
Formulate Hypothesis? s2 = (x-8)/1
σ2 2
At 95% -1.96<z<1.96
• Null Hypothesis: H0: Mean<=7
Therefore 6.04<x<9.96
• Alternate Hypothesis : H1: Mean>7 Upper tail test: H0: σ12 – σ22 = 0
H0: σ2 ≤ σ02 HA: σ12 – σ22 ≠ 0
• Statistic to be measured: t-statistic, with 29 DoF HA: σ2 > σ02


/2
2
Do not reject H0 Reject H0 F
2 Do not Reject H0
reject H 0 F/2


Seminar Material
Not for Sale
What to expect at the end?

Towards the end of School for FRM Prep*
You will be able to learn the topics related to FRM Part I Exam

You will know how to solve the questions asked in FRM Part I Exam

You will get an industry perspective of the topics

*assuming you follow the program and practice

www.edupristine.com

Seminar Material
Not for Sale
About the Program

Venue: Online

Starting Date: 26 Feb, 2011

www.edupristine.com

Seminar Material
Not for Sale
Cost of the Program

USD 475
For participants joining in groups of 5 or more
USD 595
For individual registrations USD 525
For registrations before 10th February

www.edupristine.com

Seminar Material
Not for Sale
Contact Details

Questions & Doubts?

Please e-mail me at pawan@edupristine.com

or visit http://www.edupristine.com

or call +91 986 762 5422

www.edupristine.com

Seminar Material
Not for Sale
To Register

• Wire Transfer
– Bank Name: HDFC Bank
– Country: India
– Swift Code: HDFCINBB
– Account Name: Neev Knowledge Management Pvt Ltd
– Account Number: 00602560008449
• Paypal to Paypal (Preferred)
– Create a Personal paypal account (it is free)
– After Logging in, click on tab "My Account" and then on "Profile". Link Paypal account with your credit card
or bank account
– Click on the tab "Send Money"
– In the "To" tab enter the email id – paypal@edupristine.com
– Pay the fees as per package required.
• Credit Card to Paypal
– You can make the payment from your credit card to Paypal account.
– Please make the payment to email id – paypal@edupristine.com


Seminar Material
Not for Sale
Other Pristine Offerings

Course Classroom Trainings Online Content Crash Hours Accreditation
Trainings Course/ of
Mock Test Training
CFA Level I All* + Singapore** Yes Original Yes 100 CFA Institute

CFA Level II Mumbai, Delhi From 2010 Original From 2010 80 CFA Institute

FRM Level I All + Singapore Yes Original Yes 75 GARP

FRM Level II Mumbai, Delhi Yes Original Yes 60 GARP

PRM All + Singapore Yes Original Yes 135 PRMIA

APRM Corporate From 2010 Original Yes 80 PRMIA
Financial Modeling Mumbai, Delhi, Yes Original NA 50 -
Bangalore
Finance for Lawyers Mumbai No Original NA 50 -

CFP Mumbai, Delhi Yes Original NA 120 Under Process

Placement Oriented Colleges No Original NA 150 Not Required
Training

*All cities include Mumbai, Delhi, Kolkata, Chennai, Bangalore, Pune and Hyderabad ; ** Singapore class room trainings to commence from June 2010

Seminar Material
Not for Sale
Agenda
– Descriptive Statistics
• Next Seminar


Seminar Material
Not for Sale

Value at Risk (VaR)
13 Feb, 2011


Seminar Material
Not for Sale
Contact

Contact Phone Email
Pawan Prabhat +91 986 762 5422 pawan@edupristine.com
Paramdeep Singh +91 989 298 0608 paramdeep@edupristine.com


Pristine quant-frm demo-annotations

More Related Content

What's hot (18)

Similar to Pristine quant-frm demo-annotations (20)

Recently uploaded (20)

Pristine quant-frm demo-annotations