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FRM Part IQuantitative Analysis
21st May 2011
© Neev Knowledge Management – Pristine
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Agenda
• Introduction and context• Understanding the FRM Examination Structure• Introduction to Quantitative Analysis
– Probability Distributions– Key Concept Checkers
• Complete Offering & Registration• Next Seminar
2
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Pristine has been started by professionals with diverse experience in financial services, IT and Auto who are alumnus of IITs & IIMs
PristineClassroom/ Online delivery (synchronous and asynchronous) – To increase reach and improve efficiency of learning. Conducted 15+ batches with over 300 hours of recorded content
Innovative content – To improve understanding & learning capability of students. VisualizeFRM, VisualizeCFA as one of the best selling products
Founded with an aim of creating world class professionals in the area of finance –particularly risk management and investment banking
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
Testimonial - 53% of the students join us on the basis of referral is a testimonial of the effective training methodologies
Effective training methodologies to improve the performance of the students and enhance the employability
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Seminar MaterialNot for SaleKey Authorization
GARP (2007-10)Authorized Training provider -FRM
Largest player in India in the area of risk management training. Trained 1000+ students in risk management
PRMIA (2009-10)Authorized Training provider – PRM/ APRMSole authorized training for PRM Training in India. Largest player in India in the area of risk management training. Trained 1000+ students in risk management
CFA Institute (2010-11)Authorized Training provider – CFA
Pristine is now the authorized training provider for CFA Exam trainings . Pristine is largest training provider for CFA in India with presence across seven major cities.
FPSB India (2010-11)Authorized Training provider -CFP
An authorized Education Provider for Chartered Financial Planner Charter.
© Neev Knowledge Management – Pristine
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Seminar MaterialNot for SaleKey Associations*
HSBC (2008)Risk Management and Quant. AnalysisNew joinees in HSBC had a gap in knowledge of Risk Management and quantitative skills. Conducted trainings (On campus) to bridge the gap
*Indicative List
Mizuho (2010)Financial Modeling in Excel
Bankers were using excel models that they could not understand. Conducted financial modeling in Excel trainings to
bridge the gap
Bank Of America Continuum Solutions (2010)Financial Modeling in Excel
Associates were trained on valuation and mergers and acquisitions
J. P. Morgan (2010)Financial Modeling in Excel
The Real Assets Group were trained in Excel for infrastructure and real-estate
modeling
Franklin TempletonCFA (2010)
Students were facing a gap in the overall understanding of finance topics like corporate finance, FSA and valuation. Provided training for over 100 hours to bridge the gap
Credit-Suisse India (2009)Risk Management and Quant. AnalysisIT Professionals of Credit-Suisse India were trained on risk management.
© Neev Knowledge Management – Pristine
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Seminar MaterialNot for Sale…Key Associations
IIM Calcutta (2010-11) Financial Modeling in Excel
Students about to go for internships and join jobs found a gap in their grasp of
knowledge of excel for financial modeling. Conducted training for 75+
students with an average rating of 4.5+
BITS Pilani (2009)Workshops on Basics of Finance
Most of the students desire a career in finance. Conducted training for 350+ students with an average rating of 4.5+
IIT Delhi (2009)Corporate finance
Students get placed in finance companies (UBS, GS, MS, etc) with no understanding of the subject/ Job Profile. Conducted workshop to bridge the gap
Sydneham College (2009)Financial Modeling in Excel
Students about to join jobs found a gap in their grasp of excel for financial modeling. Conducted 40+ hours of training and helped students be ready for job
NISM (2008)Derivatives workshop for Hedging
Corporate in Ludhiana incurred huge losses because of derivative trades (for hedging). Conducted trainings for directors and CFOs for better understanding of derivative products
FMS Delhi (2010-11) Financial Modeling in Excel
Final Year MBA students of Faculty of Management Studies, Delhi University were trained in financial modeling so as
to prepare them better for a job in finance.
© Neev Knowledge Management – Pristine
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Seminar MaterialNot for SaleTrainer
Pawan Prabhat, Director and Faculty, Pristine
© Neev Knowledge Management – 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.
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© Neev Knowledge Management – Pristine
Agenda
• Introduction and context• Understanding the FRM Examination Structure• Introduction to Quantitative Analysis
– Probability Distributions– Key Concept Checkers
• Complete Offering & Registration• Next Seminar
8
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© Neev Knowledge Management – Pristine Careers 9
FRM 2011
Area WeightFoundations 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
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FRM Exam Statistics
12%
23%
30%
35%
2009 FRM Sample Paper
Foundation of Risk Management
Quantitative Analysis
Financial Markets and Products
Valuation and Risk Models
12%
20%
40%
28%
2010 FRM Sample Paper
Foundation of Risk Management
Quantitative Analysis
Financial Markets and Products
Valuation and Risk Models
• 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
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Foundation of Risk
Management
Quantitative Analysis
Financial Markets and
Products
Valuation and Risk Models
Numerical 1 4 5 11
Subjective 4 5 7 3
0
2
4
6
8
10
12
2010
Foundation of Risk
Management
Quantitative Analysis
Financial Markets and
Products
Valuation and Risk Models
Numerical 3 6 8 4
Subjective 2 2 8 3
0123456789
2009
Numerical vs Subjective Questions
• 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.
Source: GARP Sample Paper
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0
2
4
6
8
10
12
14
16
18
Foundation of Risk
Management
Quantitative Analysis
Financial Markets and
Products
Valuation and Risk Models
Num
ber o
f Que
stio
ns
2010
2009
Number of Questions Year-wise
Source: GARP Sample Paper
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© Neev Knowledge Management – Pristine
Agenda
• Introduction and context• Understanding the FRM Examination Structure• Introduction to Quantitative Analysis
– Probability Distributions– Key Concept Checkers
• Complete Offering & Registration• Next Seminar
13
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Seminar MaterialNot for SaleQuantitative Analysis
© Neev Knowledge Management – Pristine
Quantitative Analysis
Statistics and Probability
Probability Distributions
Sampling & Hyp. Testing
Regression Analysis
EWMAGARCH
• Basics of Probability
• Population and Sample Statistics
• Properties of Distributions – Discrete/ Continuous
• Binomial Distribution
• Normal Distribution
• Standard Error
• Formulating Hypothesis
• Type I and II Errors
• Linear Regression
• Multiple Regressors
• OLS• Error Analysis• Heteroscedac
ity
• Estimating Volatility and Correlation
• Monte Carlo• Volatility Term
Structures
Reference Book - James Stock and Mark Watson, Introduction to Econometrics, Brief Edition (Boston: Pearson Education, 2008).
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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
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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
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Normal Distribution
Only Mean and Standard Deviation is required to fully understand a distribution
68% of Data
95% of Data
99.7% of Data
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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:
• The probability of the value lying between a and b is given by:
• 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.
2
2
2 2)(exp
21)(
xxP
b
a
dxxPbXaP ).()(
The skewness of normal distribution is = 0 and the kurtosis is =3
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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
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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
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
-4 -3 -2 -1 0 1 2 3 4
PlatykurticK<3Mesokurtic
K=3
LeptokuticK>3
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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.
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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.
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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:
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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.
-4 -3 -2 -1 0 1 2 3 4
68% of Data
95% of Data
99.7% of Data
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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. 0.083B. 0.2166C. 0.6826D. 0.7494
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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
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
-4 -3 -2 -1 0 1 2 3 4
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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.
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00.05
0.10.15
0.20.25
0.30.35
0.4
0 2 4 6 8 10 12 14 16 18 20 22 240
0.020.040.060.080.1
0.120.140.160.180.2
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28
0
0.02
0.04
0.06
0.08
0.1
0.12
0 2 4 6 8 10 12 14 16 18 20 22 240
0.010.020.030.040.050.060.070.080.09
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54
λ=1 λ=5
λ=25λ=15
Plots of Poisson Distribution
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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.
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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. 15.10B. 3.21C. 227.90D. 1
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• ANSWER: A
© Neev Knowledge Management – Pristine 3131
Answer
Lognormal Distribution
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Seminar MaterialNot for SaleWhat 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
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© Neev Knowledge Management – Pristine
Agenda
• Introduction and context• Understanding the FRM Examination Structure• Introduction to Quantitative Analysis
– Probability Distributions– Key Concept Checkers
• Complete Offering & Registration• Next Seminar
33
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Seminar MaterialNot for SaleAbout 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
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Seminar MaterialNot for SaleAbout School for FRM Prep
School for FRM Prep is
a 100 Hrs extensive training program*
that can enable you
to prepare for and crack FRM Part I Exam
• Extensive 100 Hours coverage
• 10 days of regular classes
• 3 days of revision classes
• 2 Mock tests
• Extensive Question Bank to
prepare and Practice
• 2 Hrs of one-to-one doubt
clearing sessions*
• Qualified faculty with extensive
industry and teaching experience
=
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Seminar MaterialNot for SaleAbout School for FRM Prep
• Proven credentials in successfully
training FRM aspirants
• Actionable and Innovative Material
• Complete Slide Pack
• Each Session followed by Quiz
• Adaptive feedback based on Quiz
• Mock tests and feedback
• Individual doubt solving session
• FRM Visualized Formula Charts
• Summarized Recordings for
revision
=School for FRM Prep is
a 100 Hrs extensive training program*
that can enable you
to prepare for and crack FRM Part I Exam
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Seminar MaterialNot for SaleTentative Schedule – Feb-March
* Indicative list – Subject to Change
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 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
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Seminar MaterialNot for SaleHow 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
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1 2 3 4 5
Start Preparation with material and Live Interactive Class
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1 2 3 4 5
Work on the Problem sets/ Quizzes adapting preparation Style
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1 2 3 4 5
Give Mock Tests/ Ask Doubts/ Revise and Complete Preparation
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1 2 3 4 5
Plan and Achieve Success in FRM Part I Exam
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Seminar MaterialNot for SaleMethodology
Each topic will be explained through
Conceptual Discussion, Examples, Tests, Quizzes, Actionable
Presentations, Visualized Charts and Q&A
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Seminar MaterialNot for SaleSample Innovative Material
© Neev Knowledge Management – Pristine 44
Probability Distributions
Normal Distribution Binomial Distribution
Normal Distribution
• Described by mean & variance• Symmetric about its mean• Standard Normal Distribution
- Mean = 0; Variance =1
Z-Score Skewness and Kurtosis
No. of σ a givenobservation is awayfrom population mean.
Z=(x-µ)/σ
Q. At a particular time, the market valueof assets of the firm is $100 Mn and themarket value of debt is $80 Mn. Thestandard deviation of assets is $ 10 Mn.What is the distance to default?Ans. z = (A-K) / σA
= (100-80)/10= 2
If Z is a standard normal R.V. An event X is defined to happen if either -1< Z < 1 orZ > 1.5. What is the prob. of event X happening if N (1) =0.8413, N (0.5) = 0.6915and 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)
= N(1)-(1-N(1)) + N(-1.5)= 2*0.8413-1 + 0.0668= 0.7494
-1 +1 1.5
-4 -3 -2 -1 0 1 2 3 4
68% of Data
95% of Data
99.7% of Data
Q. Which of the following is likely to be a probability distributionfunction?For X=[1,2,3,4,5], Prob[Xi]= 49/(75-Xi
2)For X=[0,5,10,15], Prob[Xi]= Xi/30For X=[1,4,9,16,25], Prob[Xi]= [(X i)1/2 – 1]/5
Ans. The correct answer is For X=[0,5,10,15], Prob[Xi]= Xi/30For all values of X, probability lies within [0,1] and sum of all theprobabilities is equal to 1.
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© Neev Knowledge Management – Pristine 45
H0: σ2 = cHA: σ2 ≠ c
Inference Based on
Sample Data
Real State of Affairs
H0 is True H0 is False
H0 is True Correct decision Confidence level = 1-
Type II error
P (Type II error) =
H0 is False Type I error Significance level = *
Correct decision
Power = 1-
*Term represents the maximum probability of committing a Type I error
Null HYPOTHESIS:H0
Alternative Hypothesis: Ha
One tailed Test Two Tailed test
Hypothesis thatthe researcherwants to reject
Concluded if there issignificant evidenceto reject H0
Test if the value is greaterthan or less than KH0; µ<=K vs. Ha: µ>K
Test if the value isdifferent from KH0; µ=0 vs. Ha: µ≠ 0
Type 1 error: rejection of H0 when itis actually true
Type 2 error :Fail to reject H0 whenit is actually false
Q. Co. ABC would give bonus to employees, if they get arating higher than 7/10 from customers. A random sampleof 30 customers is conducted with rating of 7.1/10.Formulate Hypothesis?• Null Hypothesis: H0: Mean<=7• Alternate Hypothesis : H1: Mean>7• Statistic to be measured: t-statistic, with 29 DoF
Range of values within whichH0 Cannot be rejected (say90% or 95%).Known variance, 2 Tailed test,CI is: X”± zα/2(σ/√t)
Confidence Intervals (CI)
Do not reject H0 Reject H0
2
2
H0: σ2 ≤ σ02
HA: σ2 > σ02
Upper tail test:
2
22
σ1)s(n
F
/2
F/2Reject H0Do not
reject H0
H0: σ12 – σ2
2 = 0HA: σ1
2 – σ22 ≠ 0
Hypothesis Testsfor Variances
Tests for a SinglePopulation Variances
Tests for a twoPopulation Variances
F testChi-Square test
H0: σ12 – σ2
2 = 0HA: σ1
2 – σ22 ≠ 0
22
21
ssF
Q. If standard deviation of anormal population is known to be10 & the mean is hypothesizedto be 8. Suppose a sample sizeof 100 is considered. What is therange of sample means in whichhypothesis can be accepted atsignificance level of 0.05?Ans: SE = = 10/√100 =1
z = (x-µ)/ SE= (x-8)/1
At 95% -1.96<z<1.96Therefore 6.04<x<9.96
n
0
0.05
0.1
0.15
0.2
-5 0 5Z=0 Z=2.5
Do not Reject H0Reject H0
α= 0.05
Z=0
Do not Reject H0
Reject H0
α= 0.025
0
0.05
0.1
0.15
0.2
-5
α= 0.025
Reject H0
Hypothesis Testing
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Seminar MaterialNot for SaleWhat 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
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Seminar MaterialNot for SaleAbout the Program
Venue: Online
Starting Date: 26 Feb, 2011
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Seminar MaterialNot for SaleCost of the Program
USD 595For individual registrations
USD 475For participants joining in groups of 5 or more
USD 525For registrations before 10th February
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Seminar MaterialNot for SaleContact Details
Questions & Doubts?
Please e-mail me at pawan@edupristine.com
or visit http://www.edupristine.com
or call +91 986 762 5422
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Seminar MaterialNot for SaleTo 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
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Seminar MaterialNot for Sale
Course Classroom Trainings Online Trainings
Content Crash Course/Mock Test
Hours of Training
Accreditation
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, Bangalore
Yes Original NA 50 -
Finance for Lawyers Mumbai No Original NA 50 -
CFP Mumbai, Delhi Yes Original NA 120 Under Process
Placement Oriented Training
Colleges No Original NA 150 Not Required
*All cities include Mumbai, Delhi, Kolkata, Chennai, Bangalore, Pune and Hyderabad ; ** Singapore class room trainings to commence from June 2010
Other Pristine Offerings
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© Neev Knowledge Management – Pristine
Agenda
• Introduction and context• Understanding the FRM Examination Structure• Introduction to Quantitative Analysis
– Descriptive Statistics– Key Concept Checkers
• Complete Offering & Registration• Next Seminar
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Value at Risk (VaR)13 Feb, 2011
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Seminar MaterialNot for SaleContact
Contact Phone Email
Pawan Prabhat +91 986 762 5422 pawan@edupristine.com
Paramdeep Singh +91 989 298 0608 paramdeep@edupristine.com
© Neev Knowledge Management – Pristine