Professional Risk Manager (PRM) Certification: Level 2

Authorized by PRMIA, EduPristine's PRM Training will help you "Build an Awesome Career in Risk Management"
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  • Lectures 55
  • Contents Video: 7.5 hours
  • Skill Level All Levels
  • Languages English
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About This Course

Published 7/2013 English

Course 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

Website: http://www.edupristine.com/ca/courses/professional-risk-manager/

Facebook:https://www.facebook.com/edupristine

Twitter:https://twitter.com/edupristine

Blogs:http://www.edupristine.com/blog/

What are the requirements?

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

What am I going to get from this course?

  • 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)

What 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.

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Introduction PRM-II
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

Section 2: Foundations
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

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

10:57

    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


    17:26

      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


      06:11

        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


        08:21

          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


          03:20

            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


            Section 3: Descriptive Statistics
            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

            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


              05:04

                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


                04:06

                  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


                  08:39

                    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


                    05:31

                      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


                      04:56

                        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


                        Section 4: Calculus
                        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
                        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
                            06:04

                                • 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

                            07:45

                                • 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

                            15:19

                                • 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

                            12:36

                                • 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

                            01:42

                                • 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

                            08:31

                                • 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

                            Section 5: Matrix Algebra
                            13:48

                            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

                            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

                            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

                            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

                            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

                            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

                            03:08

                                • 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

                            Section 6: Probability Theory
                            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

                            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

                            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

                                  ·  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

                            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

                                  ·  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

                            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

                                  ·  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

                            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

                                  ·  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

                            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

                                  ·  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

                            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

                                  ·  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

                            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

                                  ·  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

                            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

                                  ·  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

                            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

                                  ·  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

                            09:00

                                • 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

                            Section 7: Regression Analysis
                            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

                            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

                            08:57

                                • 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

                            03:54

                                • 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

                            03:49

                                • 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

                            03:22

                                • 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

                            Section 8: Numerical Methods
                            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

                            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

                            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

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

                            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

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

                            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

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

                            06:21

                                • 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

                            Section 9: Conclusion: PRM-II
                            Conclusion PRM-II
                            04:35
                            Section 10: PRM-II : QUIZ
                            PRM-II : QUIZ
                            15 questions

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                            Instructor Biography

                            EduPristine Inc, Leading International Finance Training Provider

                            Trusted by Fortune 500 Companies and 10,000 Students from 40+ countries across the globe, EduPristine is one of the leading International Training providers for Finance Certifications like FRM®, CFA®, PRM®, Business Analytics, HR Analytics, Financial Modeling, Operational Risk Modeling etc. It was founded by industry professionals who have worked in the area of investment banking and private equity in organizations such as Goldman Sachs, Crisil - A Standard & Poors Company, Standard Chartered and Accenture. EduPristine has conducted corporate training for various leading corporations and colleges like JP Morgan, Bank of America, Ernst & Young, Accenture, HSBC, IIM C, NUS Singapore etc. EduPristine has conducted more than 500,000 man-hours of quality training in finance.

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