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Professional Risk Manager (PRM) Certification: Level 2

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Authorized by PRMIA, EduPristine's PRM Training will help you "Build an Awesome Career in Risk Management"

41 students enrolled

Created by
EduPristine Inc

Last updated 7/2013

English

- 7.5 hours on-demand video
- Full lifetime access
- Access on mobile and TV

What Will I Learn?

- Course Objective is to help participants successfully pass the PRM Exam - II
- Lucrative career options in Risk Management, Trading, Structuring, Modeling, etc. PRM holders have positions such as Chief Risk Officer, Senior Risk Analyst, Head of Operational Risk, and Director, Investment Risk Management, to name a few.
- Strong value addition to your skills, credentials and resume
- Complete coverage of risk management concepts
- Globally recognized professional certification for banking and finance professionals by PRMIA (Professional Risk Managers' International Association)

Requirements

- The only prerequisite to attempting the PRM exams is membership in PRMIA. Passing all four exams leads to the PRM designation.

Description

**Why Professional Risk Manager?**

If you are looking for a lucrative finance career in Risk Consultancy Firms, Banks, Insurance companies, Asset Management, Hedge funds, Investment banks etc., then PRM (Professional Risk Manager) is the right catch for you.

PRM is a professional designation awarded by the PRMIA to Professional Risk Managers (PRM) who passes their four online exams.

**PRM-II Curriculum focuses on providing knowledge and understanding of Mathematical Foundation of Risks:**

- Descriptive Statistics & Calculus
- Linear Mathematics & Matrix Algebra
- Probability Theory, Regression Analysis & Numerical Methods

**Professional Recognition & Job Satisfaction**

- A PRM Charter can improve job opportunities, professional reputation & pay.
- Types of Businesses that hire PRMs include: Risk Consultancy Firms, Banks, Insurance Companies, Asset Management, Hedge Funds, Investment Banks etc.

**How to update your CV with Professional Risk Management Skills?**

After qualifying Professional Risk Manager Exam, you can add heavy duty terms in your resume like "Risk Management", "Basel-I, II, III", "Interest Rate Risk ", "Risk Metrics", “Financial Econometrics” etc, which will surely diversify your professional reach.

**EduPristine's PRM Training Program- Unique Offerings:**

- 55+ Lectures covering all topics of PRM-II in depth
- Comprehensive Study Material for easy learning experience
- 150 Topic wise quiz questions with explanatory answers
- 2 Mock Tests
- 24x7 Access to Discussion Forums to interact with faculty & fellow students
- One-to-one doubt clearing session for all participants
- Comprehensive reading material for all topics

Why EduPristine's PRM Training Program??

- One of the leading International Training Provider for Risk Managament Courses.
- Get trained by highly proficient Risk Management Experts
- Indepth Training to make you the best Risk Management Professional in Town
- Complimentary access to Live Webinars on Risk Management

Who is the target audience?

- This program is suitable for Bankers, IT professionals, Analytics and Finance professionals with an interest in risk management.
- It is also beneficial for Btech, MBA, Finance graduates who are interested in financial risk management career.

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Curriculum For This Course

55 Lectures

07:33:45
+
–

Introduction PRM-II
1 Lecture
04:38

**This
is the introductory video for PRM-II which talks about the topics covered under
this course.**

**These
topics are as follows:**

·
**Foundations**

·
**Descriptive
Statistics**

·
**Calculus**

·
**Linear
Mathematics and Matrix Algebra**

·
**Probability
Theory**

·
**Regression
Analysis**

·
**Numerical
Methods**

Preview
04:38

+
–

Foundations
7 Lectures
01:19:27

Lectures(2-8) gives an overview of the basic rules of arithmetic, equations and inequalities, functions and graphs.

These lectures:

· Describes Rules of algebraic operations

· Lists the Order of algebraic operations

· Characterize Sequences, Series, Exponents, Logarithms, Exponential function and Natural Logarithms

· Shows how to solve Linear equalities and inequalities in one unknown

· Demonstrates the Elimination method and the Substitution method

· Shows how to solve Quadratic equations in one unknown

· Characterizes Functions and Graphs

· Differentiates between discrete compounding and continuous compounding

Preview
14:47

Lectures(2-8) gives an overview of the basic rules of arithmetic, equations and inequalities, functions and graphs.

These lectures:

· Describes Rules of algebraic operations

· Lists the Order of algebraic operations

· Characterize Sequences, Series, Exponents, Logarithms, Exponential function and Natural Logarithms

· Shows how to solve Linear equalities and inequalities in one unknown

· Demonstrates the Elimination method and the Substitution method

· Shows how to solve Quadratic equations in one unknown

· Characterizes Functions and Graphs

· Differentiates between discrete compounding and continuous compounding

Foundations_2

18:25

Lectures(2-8) gives an overview of the basic rules of arithmetic, equations and inequalities, functions and graphs.

These lectures:

· Describes Rules of algebraic operations

· Lists the Order of algebraic operations

· Characterize Sequences, Series, Exponents, Logarithms, Exponential function and Natural Logarithms

· Shows how to solve Linear equalities and inequalities in one unknown

· Demonstrates the Elimination method and the Substitution method

· Shows how to solve Quadratic equations in one unknown

· Characterizes Functions and Graphs

· Differentiates between discrete compounding and continuous compounding

Foundations_3

10:57

These lectures:

· Describes Rules of algebraic operations

· Lists the Order of algebraic operations

· Characterize Sequences, Series, Exponents, Logarithms, Exponential function and Natural Logarithms

· Shows how to solve Linear equalities and inequalities in one unknown

· Demonstrates the Elimination method and the Substitution method

· Shows how to solve Quadratic equations in one unknown

· Characterizes Functions and Graphs

· Differentiates between discrete compounding and continuous compounding

Foundations_4

17:26

These lectures:

· Describes Rules of algebraic operations

· Lists the Order of algebraic operations

· Characterize Sequences, Series, Exponents, Logarithms, Exponential function and Natural Logarithms

· Shows how to solve Linear equalities and inequalities in one unknown

· Demonstrates the Elimination method and the Substitution method

· Shows how to solve Quadratic equations in one unknown

· Characterizes Functions and Graphs

· Differentiates between discrete compounding and continuous compounding

Foundations_5

06:11

These lectures:

· Describes Rules of algebraic operations

· Lists the Order of algebraic operations

· Characterize Sequences, Series, Exponents, Logarithms, Exponential function and Natural Logarithms

· Shows how to solve Linear equalities and inequalities in one unknown

· Demonstrates the Elimination method and the Substitution method

· Shows how to solve Quadratic equations in one unknown

· Characterizes Functions and Graphs

· Differentiates between discrete compounding and continuous compounding

Foundations_6

08:21

These lectures:

· Describes Rules of algebraic operations

· Lists the Order of algebraic operations

· Characterize Sequences, Series, Exponents, Logarithms, Exponential function and Natural Logarithms

· Shows how to solve Linear equalities and inequalities in one unknown

· Demonstrates the Elimination method and the Substitution method

· Shows how to solve Quadratic equations in one unknown

· Characterizes Functions and Graphs

· Differentiates between discrete compounding and continuous compounding

Foundations_7

03:20

+
–

Descriptive Statistics
7 Lectures
50:38

Lectures(9-15) gives an overview of the sample moments of returns distributions, ‘downside’ risk statistics, and measures of co-variation (e.g. correlation) between two random variables.

These lectures:

· Describes various forms of Data

· Discusses Graphical representation of data

· Explains the concept of The Moments of a Distribution

· Shows how to calculate the Central Tendency, the Measures of Dispersion, the Historical Volatility from Returns Data

· Shows how to calculate Negative Semi-variance, Negative Semi-deviation, Skewness and Kurtosis

· Discusses Covariance and Covariance Matrix

· Discusses Correlation Coefficient and Correlation Matrix

· Shows how to calculate the volatility of a portfolio

Statistics_1

06:10

Lectures(9-15) gives an overview of the sample moments of returns distributions, ‘downside’ risk statistics, and measures of co-variation (e.g. correlation) between two random variables.

These lectures:

· Describes various forms of Data

· Discusses Graphical representation of data

· Explains the concept of The Moments of a Distribution

· Shows how to calculate the Central Tendency, the Measures of Dispersion, the Historical Volatility from Returns Data

· Shows how to calculate Negative Semi-variance, Negative Semi-deviation, Skewness and Kurtosis

· Discusses Covariance and Covariance Matrix

· Discusses Correlation Coefficient and Correlation Matrix

· Shows how to calculate the volatility of a portfolio

Statistics_2

16:12

Lectures(9-15) gives an overview of the sample moments of returns distributions, ‘downside’ risk statistics, and measures of co-variation (e.g. correlation) between two random variables.

These lectures:

· Describes various forms of Data

· Discusses Graphical representation of data

· Explains the concept of The Moments of a Distribution

· Shows how to calculate the Central Tendency, the Measures of Dispersion, the Historical Volatility from Returns Data

· Shows how to calculate Negative Semi-variance, Negative Semi-deviation, Skewness and Kurtosis

· Discusses Covariance and Covariance Matrix

· Discusses Correlation Coefficient and Correlation Matrix

· Shows how to calculate the volatility of a portfolio

Statistics_3

05:04

These lectures:

· Describes various forms of Data

· Discusses Graphical representation of data

· Explains the concept of The Moments of a Distribution

· Shows how to calculate Negative Semi-variance, Negative Semi-deviation, Skewness and Kurtosis

· Discusses Covariance and Covariance Matrix

· Discusses Correlation Coefficient and Correlation Matrix

· Shows how to calculate the volatility of a portfolio

Statistics_4

04:06

These lectures:

· Describes various forms of Data

· Discusses Graphical representation of data

· Explains the concept of The Moments of a Distribution

· Shows how to calculate Negative Semi-variance, Negative Semi-deviation, Skewness and Kurtosis

· Discusses Covariance and Covariance Matrix

· Discusses Correlation Coefficient and Correlation Matrix

· Shows how to calculate the volatility of a portfolio

Statistics_5

08:39

These lectures:

· Describes various forms of Data

· Discusses Graphical representation of data

· Explains the concept of The Moments of a Distribution

· Shows how to calculate Negative Semi-variance, Negative Semi-deviation, Skewness and Kurtosis

· Discusses Covariance and Covariance Matrix

· Discusses Correlation Coefficient and Correlation Matrix

· Shows how to calculate the volatility of a portfolio

Statistics_6

05:31

These lectures:

· Describes various forms of Data

· Discusses Graphical representation of data

· Explains the concept of The Moments of a Distribution

· Shows how to calculate Negative Semi-variance, Negative Semi-deviation, Skewness and Kurtosis

· Discusses Covariance and Covariance Matrix

· Discusses Correlation Coefficient and Correlation Matrix

· Shows how to calculate the volatility of a portfolio

Statistics_7

04:56

+
–

Calculus
8 Lectures
01:18:43

Lectures(16-23) talks about on differentiation and integration, Taylor expansion, financial applications and optimization.

These lectures:

· Explains the concept of differentiation

· Demonstrates the application of the rules of differentiation to polynomial, exponential and logarithmic functions

· Shows how to calculate the modified duration of a bond

· Discusses Taylor Approximations

· Demonstrates the concept of convexity, delta, gamma and vega, Partial Differentiation, Total Differentiation

· Discusses the Fundamental Theorem of Analysis

· Discusses Optimisation of Univariate and Multivariate functions

. Demonstrates Constrained Optimisation using Lagrange Multipliers
Calculus_1

21:06

Lectures(16-23) talks about on differentiation and integration, Taylor expansion, financial applications and optimization.

These lectures:

· Explains the concept of differentiation

· Demonstrates the application of the rules of differentiation to polynomial, exponential and logarithmic functions

· Shows how to calculate the modified duration of a bond

· Discusses Taylor Approximations

· Demonstrates the concept of convexity, delta, gamma and vega, Partial Differentiation, Total Differentiation

· Discusses the Fundamental Theorem of Analysis

· Discusses Optimisation of Univariate and Multivariate functions

. Demonstrates Constrained Optimisation using Lagrange Multipliers
Calculus_2

05:40

Lectures(16-23) talks about on differentiation and integration, Taylor expansion, financial applications and optimization.

These lectures:

· Explains the concept of differentiation

· Demonstrates the application of the rules of differentiation to polynomial, exponential and logarithmic functions

· Shows how to calculate the modified duration of a bond

· Discusses Taylor Approximations

· Demonstrates the concept of convexity, delta, gamma and vega, Partial Differentiation, Total Differentiation

· Discusses the Fundamental Theorem of Analysis

· Discusses Optimisation of Univariate and Multivariate functions

. Demonstrates Constrained Optimisation using Lagrange Multipliers

Calculus_3

06:04

These lectures:

· Explains the concept of differentiation

· Shows how to calculate the modified duration of a bond

· Discusses Taylor Approximations

· Discusses the Fundamental Theorem of Analysis

· Discusses Optimisation of Univariate and Multivariate functions

. Demonstrates Constrained Optimisation using Lagrange Multipliers

Calculus_4

07:45

These lectures:

· Explains the concept of differentiation

· Shows how to calculate the modified duration of a bond

· Discusses Taylor Approximations

· Discusses the Fundamental Theorem of Analysis

· Discusses Optimisation of Univariate and Multivariate functions

. Demonstrates Constrained Optimisation using Lagrange Multipliers

Calculus_5

15:19

These lectures:

· Explains the concept of differentiation

· Shows how to calculate the modified duration of a bond

· Discusses Taylor Approximations

· Discusses the Fundamental Theorem of Analysis

· Discusses Optimisation of Univariate and Multivariate functions

. Demonstrates Constrained Optimisation using Lagrange Multipliers

Calculus_6

12:36

These lectures:

· Explains the concept of differentiation

· Shows how to calculate the modified duration of a bond

· Discusses Taylor Approximations

· Discusses the Fundamental Theorem of Analysis

· Discusses Optimisation of Univariate and Multivariate functions

. Demonstrates Constrained Optimisation using Lagrange Multipliers

Calculus_7

01:42

These lectures:

· Explains the concept of differentiation

· Shows how to calculate the modified duration of a bond

· Discusses Taylor Approximations

· Discusses the Fundamental Theorem of Analysis

· Discusses Optimisation of Univariate and Multivariate functions

. Demonstrates Constrained Optimisation using Lagrange Multipliers

Calculus_8

08:31

+
–

Matrix Algebra
7 Lectures
52:43

Lecture(23-29) talks about operations, special types of matrices and the laws of matrix algebra.

These lectures:

· Demonstrates basic operations of Matrix Algebra

· Shows how to solve two Linear Simultaneous Equations using Matrix Algebra

· Demonstrates Portfolio Construction, Hedging of a Vanilla Option Position

· Describes Quadratic Forms

· Demonstrates Cholesky Decomposition, Eigenvalues, Eigenvectors and Principal Components

Linear Mathematics and Matrix Algebra_1

13:48

Lecture(24-30) talks about operations, special types of matrices and the laws of matrix algebra.

These lectures:

· Demonstrates basic operations of Matrix Algebra

· Shows how to solve two Linear Simultaneous Equations using Matrix Algebra

· Demonstrates Portfolio Construction, Hedging of a Vanilla Option Position

· Describes Quadratic Forms

· Demonstrates Cholesky Decomposition, Eigenvalues, Eigenvectors and Principal Components

Linear Mathematics and Matrix Algebra_2

17:20

Lecture(24-30) talks about operations, special types of matrices and the laws of matrix algebra.

These lectures:

· Demonstrates basic operations of Matrix Algebra

· Shows how to solve two Linear Simultaneous Equations using Matrix Algebra

· Demonstrates Portfolio Construction, Hedging of a Vanilla Option Position

· Describes Quadratic Forms

· Demonstrates Cholesky Decomposition, Eigenvalues, Eigenvectors and Principal Components

Linear Mathematics and Matrix Algebra_3

04:51

Lecture(24-30) talks about operations, special types of matrices and the laws of matrix algebra.

These lectures:

· Demonstrates basic operations of Matrix Algebra

· Shows how to solve two Linear Simultaneous Equations using Matrix Algebra

· Demonstrates Portfolio Construction, Hedging of a Vanilla Option Position

· Describes Quadratic Forms

· Demonstrates Cholesky Decomposition, Eigenvalues, Eigenvectors and Principal Components

Linear Mathematics and Matrix Algebra_4

04:08

Lecture(24-30) talks about operations, special types of matrices and the laws of matrix algebra.

These lectures:

· Demonstrates basic operations of Matrix Algebra

· Shows how to solve two Linear Simultaneous Equations using Matrix Algebra

· Demonstrates Portfolio Construction, Hedging of a Vanilla Option Position

· Describes Quadratic Forms

· Demonstrates Cholesky Decomposition, Eigenvalues, Eigenvectors and Principal Components

Linear Mathematics and Matrix Algebra_5

07:04

Lecture(24-30) talks about operations, special types of matrices and the laws of matrix algebra.

These lectures:

· Demonstrates basic operations of Matrix Algebra

· Shows how to solve two Linear Simultaneous Equations using Matrix Algebra

· Demonstrates Portfolio Construction, Hedging of a Vanilla Option Position

· Describes Quadratic Forms

· Demonstrates Cholesky Decomposition, Eigenvalues, Eigenvectors and Principal Components

Linear Mathematics and Matrix Algebra_6

02:24

Lecture(24-30) talks about operations, special types of matrices and the laws of matrix algebra.

These lectures:

· Demonstrates basic operations of Matrix Algebra

· Shows how to solve two Linear Simultaneous Equations using Matrix Algebra

· Demonstrates Portfolio Construction, Hedging of a Vanilla Option Position

· Describes Quadratic Forms

· Demonstrates Cholesky Decomposition, Eigenvalues, Eigenvectors and Principal Components

Linear Mathematics and Matrix Algebra_7

03:08

+
–

Probability Theory
12 Lectures
01:44:35

Lectures(31-42) explains the concept of probability and the rules that govern it.

These lectures:

· Explains the concept of probability

· Describes the different approaches to defining and measuring probability

· Demonstrates the rules of probability

· Defines the discrete and continuous random variable

· Describes Probability density functions and histograms

· Describes the Algebra of Random variables

· Defines the Expected Value and Variance of a discrete random variable

· Demonstrates Joint Probability Distributions

· Discusses covariance, correlation, linear combinations of random variables, Binomial Distribution, Poisson distribution, Uniform Continuous Distribution, Normal Distribution

· Discuss the Lognormal Probability Distribution and its use in derivative pricing

· Discuss the Student’s t Distribution

· Discuss the Bivariate Normal Joint Distribution

Probability Theory_1

18:42

Lectures(31-42) explains the concept of probability and the rules that govern it.

These lectures:

· Explains the concept of probability

· Describes the different approaches to defining and measuring probability

· Demonstrates the rules of probability

· Defines the discrete and continuous random variable

· Describes Probability density functions and histograms

· Describes the Algebra of Random variables

· Defines the Expected Value and Variance of a discrete random variable

· Demonstrates Joint Probability Distributions

· Discusses covariance, correlation, linear combinations of random variables, Binomial Distribution, Poisson distribution, Uniform Continuous Distribution, Normal Distribution

· Discuss the Lognormal Probability Distribution and its use in derivative pricing

· Discuss the Student’s t Distribution

· Discuss the Bivariate Normal Joint Distribution

Probability Theory_2

14:29

Lectures(31-42) explains the concept of probability and the rules that govern it.

These lectures:

· Explains the concept of probability

· Describes the different approaches to defining and measuring probability

· Demonstrates the rules of probability

· Defines the discrete and continuous random variable

· Describes Probability density functions and histograms

· Describes the Algebra of Random variables

· Defines the Expected Value and Variance of a discrete random variable

· Demonstrates Joint Probability Distributions

· Discusses covariance, correlation, linear combinations of random variables, Binomial Distribution, Poisson distribution, Uniform Continuous Distribution, Normal Distribution

· Discuss the Lognormal Probability Distribution and its use in derivative pricing

· Discuss the Student’s t Distribution

· Discuss the Bivariate Normal Joint Distribution

Probability Theory_3

06:59

Lectures(31-42) explains the concept of probability and the rules that govern it.

These lectures:

· Explains the concept of probability

· Describes the different approaches to defining and measuring probability

· Demonstrates the rules of probability

· Defines the discrete and continuous random variable

· Describes Probability density functions and histograms

· Describes the Algebra of Random variables

· Defines the Expected Value and Variance of a discrete random variable

· Demonstrates Joint Probability Distributions

· Discuss the Lognormal Probability Distribution and its use in derivative pricing

· Discuss the Student’s t Distribution

· Discuss the Bivariate Normal Joint Distribution

Probability Theory_4

14:18

Lectures(31-42) explains the concept of probability and the rules that govern it.

These lectures:

· Explains the concept of probability

· Describes the different approaches to defining and measuring probability

· Demonstrates the rules of probability

· Defines the discrete and continuous random variable

· Describes Probability density functions and histograms

· Describes the Algebra of Random variables

· Defines the Expected Value and Variance of a discrete random variable

· Demonstrates Joint Probability Distributions

· Discuss the Lognormal Probability Distribution and its use in derivative pricing

· Discuss the Student’s t Distribution

· Discuss the Bivariate Normal Joint Distribution

Probability Theory_5

09:33

Lectures(31-42) explains the concept of probability and the rules that govern it.

These lectures:

· Explains the concept of probability

· Describes the different approaches to defining and measuring probability

· Demonstrates the rules of probability

· Defines the discrete and continuous random variable

· Describes Probability density functions and histograms

· Describes the Algebra of Random variables

· Defines the Expected Value and Variance of a discrete random variable

· Demonstrates Joint Probability Distributions

· Discuss the Lognormal Probability Distribution and its use in derivative pricing

· Discuss the Student’s t Distribution

· Discuss the Bivariate Normal Joint Distribution

Probability Theory_6

11:07

Lectures(31-42) explains the concept of probability and the rules that govern it.

These lectures:

· Explains the concept of probability

· Describes the different approaches to defining and measuring probability

· Demonstrates the rules of probability

· Defines the discrete and continuous random variable

· Describes Probability density functions and histograms

· Describes the Algebra of Random variables

· Defines the Expected Value and Variance of a discrete random variable

· Demonstrates Joint Probability Distributions

· Discuss the Lognormal Probability Distribution and its use in derivative pricing

· Discuss the Student’s t Distribution

· Discuss the Bivariate Normal Joint Distribution

Probability Theory_7

05:40

Lectures(31-42) explains the concept of probability and the rules that govern it.

These lectures:

· Explains the concept of probability

· Describes the different approaches to defining and measuring probability

· Demonstrates the rules of probability

· Defines the discrete and continuous random variable

· Describes Probability density functions and histograms

· Describes the Algebra of Random variables

· Defines the Expected Value and Variance of a discrete random variable

· Demonstrates Joint Probability Distributions

· Discuss the Lognormal Probability Distribution and its use in derivative pricing

· Discuss the Student’s t Distribution

· Discuss the Bivariate Normal Joint Distribution

Probability Theory_8

02:35

Lectures(31-42) explains the concept of probability and the rules that govern it.

These lectures:

· Explains the concept of probability

· Describes the different approaches to defining and measuring probability

· Demonstrates the rules of probability

· Defines the discrete and continuous random variable

· Describes Probability density functions and histograms

· Describes the Algebra of Random variables

· Defines the Expected Value and Variance of a discrete random variable

· Demonstrates Joint Probability Distributions

· Discuss the Lognormal Probability Distribution and its use in derivative pricing

· Discuss the Student’s t Distribution

· Discuss the Bivariate Normal Joint Distribution

Probability Theory_9

08:23

Lectures(31-42) explains the concept of probability and the rules that govern it.

These lectures:

· Explains the concept of probability

· Describes the different approaches to defining and measuring probability

· Demonstrates the rules of probability

· Defines the discrete and continuous random variable

· Describes Probability density functions and histograms

· Describes the Algebra of Random variables

· Defines the Expected Value and Variance of a discrete random variable

· Demonstrates Joint Probability Distributions

· Discuss the Lognormal Probability Distribution and its use in derivative pricing

· Discuss the Student’s t Distribution

· Discuss the Bivariate Normal Joint Distribution

Probability Theory_10

01:54

Lectures(31-42) explains the concept of probability and the rules that govern it.

These lectures:

· Explains the concept of probability

· Describes the different approaches to defining and measuring probability

· Demonstrates the rules of probability

· Defines the discrete and continuous random variable

· Describes Probability density functions and histograms

· Describes the Algebra of Random variables

· Defines the Expected Value and Variance of a discrete random variable

· Demonstrates Joint Probability Distributions

· Discuss the Lognormal Probability Distribution and its use in derivative pricing

· Discuss the Student’s t Distribution

· Discuss the Bivariate Normal Joint Distribution

Probability Theory_11

01:55

Lectures(31-42) explains the concept of probability and the rules that govern it.

These lectures:

· Explains the concept of probability

· Describes the different approaches to defining and measuring probability

· Demonstrates the rules of probability

· Defines the discrete and continuous random variable

· Describes Probability density functions and histograms

· Describes the Algebra of Random variables

· Defines the Expected Value and Variance of a discrete random variable

· Demonstrates Joint Probability Distributions

· Discuss the Lognormal Probability Distribution and its use in derivative pricing

· Discuss the Student’s t Distribution

· Discuss the Bivariate Normal Joint Distribution

Probability Theory_12

09:00

+
–

Regression Analysis
6 Lectures
40:25

Lectures(43-48) explains the concept of simple and multiple regression models, with applications to the CAPM and APT.

These lectures:

· Defines Regression Analysis and the different types of regression

· Demonstrates Simple Linear Regression, Multiple Linear Regression

· Discusses the evaluation of the Regression Model, Confidence Intervals, Hypothesis Testing

· Demonstrates Significance Tests for the Regression Parameters

· Describe Type I and Type II Errors

· Demonstrate the concept of Prediction

· Describes the OLS Assumptions, Random Walks and Mean Reversion, and Maximum Likelihood Estimation

Regression Analysis_1

05:34

Lectures(43-48) explains the concept of simple and multiple regression models, with applications to the CAPM and APT.

These lectures:

· Defines Regression Analysis and the different types of regression

· Demonstrates Simple Linear Regression, Multiple Linear Regression

· Discusses the evaluation of the Regression Model, Confidence Intervals, Hypothesis Testing

· Demonstrates Significance Tests for the Regression Parameters

· Describe Type I and Type II Errors

· Demonstrate the concept of Prediction

· Describes the OLS Assumptions, Random Walks and Mean Reversion, and Maximum Likelihood Estimation

Regression Analysis_2

14:49

Lectures(43-48) explains the concept of simple and multiple regression models, with applications to the CAPM and APT.

These lectures:

· Defines Regression Analysis and the different types of regression

· Demonstrates Simple Linear Regression, Multiple Linear Regression

· Discusses the evaluation of the Regression Model, Confidence Intervals, Hypothesis Testing

· Demonstrates Significance Tests for the Regression Parameters

· Describe Type I and Type II Errors

· Demonstrate the concept of Prediction

· Describes the OLS Assumptions, Random Walks and Mean Reversion, and Maximum Likelihood Estimation

Regression Analysis_3

08:57

These lectures:

· Defines Regression Analysis and the different types of regression

· Demonstrates Simple Linear Regression, Multiple Linear Regression

· Discusses the evaluation of the Regression Model, Confidence Intervals, Hypothesis Testing

· Demonstrates Significance Tests for the Regression Parameters

· Describe Type I and Type II Errors

· Demonstrate the concept of Prediction

· Describes the OLS Assumptions, Random Walks and Mean Reversion, and Maximum Likelihood Estimation

Regression Analysis_4

03:54

These lectures:

· Defines Regression Analysis and the different types of regression

· Demonstrates Simple Linear Regression, Multiple Linear Regression

· Discusses the evaluation of the Regression Model, Confidence Intervals, Hypothesis Testing

· Demonstrates Significance Tests for the Regression Parameters

· Describe Type I and Type II Errors

· Demonstrate the concept of Prediction

· Describes the OLS Assumptions, Random Walks and Mean Reversion, and Maximum Likelihood Estimation

Regression Analysis_5

03:49

These lectures:

· Defines Regression Analysis and the different types of regression

· Demonstrates Simple Linear Regression, Multiple Linear Regression

· Discusses the evaluation of the Regression Model, Confidence Intervals, Hypothesis Testing

· Demonstrates Significance Tests for the Regression Parameters

· Describe Type I and Type II Errors

· Demonstrate the concept of Prediction

· Describes the OLS Assumptions, Random Walks and Mean Reversion, and Maximum Likelihood Estimation

Regression Analysis_6

03:22

+
–

Numerical Methods
6 Lectures
38:01

Lecture(49-54) explains how to solve implicit equations, finite differences and simulation.

These lectures:

· Demonstrates the Bisection method for solving Non-differential Equations

· Demonstrate the Newton-Raphson method for solving Non-differential Equations

· Demonstrates Unconstrained Numerical Optimisation, Constrained Numerical Optimisation

· Demonstrates Binomial Lattices for valuing options, Finite Difference Methods for valuing options and Simulation

Numerical Methods_1

08:30

Lecture(49-54) explains how to solve implicit equations, finite differences and simulation.

These lectures:

· Demonstrates the Bisection method for solving Non-differential Equations

· Demonstrate the Newton-Raphson method for solving Non-differential Equations

· Demonstrates Unconstrained Numerical Optimisation, Constrained Numerical Optimisation

· Demonstrates Binomial Lattices for valuing options, Finite Difference Methods for valuing options and Simulation

Numerical Methods_2

07:27

Lecture(49-54) explains how to solve implicit equations, finite differences and simulation.

These lectures:

· Demonstrates the Bisection method for solving Non-differential Equations

· Demonstrate the Newton-Raphson method for solving Non-differential Equations

· Demonstrates Unconstrained Numerical Optimisation, Constrained Numerical Optimisation

· Demonstrates Binomial Lattices for valuing options, Finite Difference Methods for valuing options and Simulation

Numerical Methods_3

03:33

Lecture(49-54) explains how to solve implicit equations, finite differences and simulation.

These lectures:

· Demonstrates the Bisection method for solving Non-differential Equations

· Demonstrate the Newton-Raphson method for solving Non-differential Equations

· Demonstrates Unconstrained Numerical Optimisation, Constrained Numerical Optimisation

Numerical Methods_4

06:25

Lecture(49-54) explains how to solve implicit equations, finite differences and simulation.

These lectures:

· Demonstrates the Bisection method for solving Non-differential Equations

· Demonstrate the Newton-Raphson method for solving Non-differential Equations

· Demonstrates Unconstrained Numerical Optimisation, Constrained Numerical Optimisation

Numerical Methods_5

05:45

Lecture(49-54) explains how to solve implicit equations, finite differences and simulation.

These lectures:

· Demonstrates the Bisection method for solving Non-differential Equations

· Demonstrate the Newton-Raphson method for solving Non-differential Equations

· Demonstrates Unconstrained Numerical Optimisation, Constrained Numerical Optimisation

Numerical Methods_6

06:21

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Conclusion: PRM-II
1 Lecture
04:35

Conclusion PRM-II

04:35

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–

PRM-II : QUIZ
0 Lectures
00:00

PRM-II : QUIZ

15 questions

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