Please confirm that you want to add Professional Risk Manager (PRM) Certification: Level 2 to your Wishlist.
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:
Professional Recognition & Job Satisfaction
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:
Why EduPristine's PRM Training Program??
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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 MultipliersLectures(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 MultipliersLectures(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 MultipliersLectures(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 MultipliersLectures(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 MultipliersLectures(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 MultipliersLectures(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 MultipliersLectures(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 MultipliersLecture(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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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