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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.
PRMII 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", "BaselI, II, III", "Interest Rate Risk ", "Risk Metrics", “Financial Econometrics” etc, which will surely diversify your professional reach.
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Section 1: Introduction PRMII  

Lecture 1  04:38  
This is the introductory video for PRMII 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  
Lecture 2  14:47  
Lectures(28) 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 

Lecture 3  18:25  
Lectures(28) 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 

Lecture 4  10:57  
Lectures(28) 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 

Lecture 5  17:26  
Lectures(28) 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 

Lecture 6  06:11  
Lectures(28) 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 

Lecture 7  08:21  
Lectures(28) 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 

Lecture 8  03:20  
Lectures(28) 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  
Lecture 9  06:10  
Lectures(915) gives an overview of the sample moments of returns distributions, ‘downside’ risk statistics, and measures of covariation (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 Semivariance, Negative Semideviation, Skewness and Kurtosis · Discusses Covariance and Covariance Matrix · Discusses Correlation Coefficient and Correlation Matrix · Shows how to calculate the volatility of a portfolio 

Lecture 10  16:12  
Lectures(915) gives an overview of the sample moments of returns distributions, ‘downside’ risk statistics, and measures of covariation (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 Semivariance, Negative Semideviation, Skewness and Kurtosis · Discusses Covariance and Covariance Matrix · Discusses Correlation Coefficient and Correlation Matrix · Shows how to calculate the volatility of a portfolio 

Lecture 11  05:04  
Lectures(915) gives an overview of the sample moments of returns distributions, ‘downside’ risk statistics, and measures of covariation (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 Semivariance, Negative Semideviation, Skewness and Kurtosis · Discusses Covariance and Covariance Matrix · Discusses Correlation Coefficient and Correlation Matrix · Shows how to calculate the volatility of a portfolio 

Lecture 12  04:06  
Lectures(915) gives an overview of the sample moments of returns distributions, ‘downside’ risk statistics, and measures of covariation (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 Semivariance, Negative Semideviation, Skewness and Kurtosis · Discusses Covariance and Covariance Matrix · Discusses Correlation Coefficient and Correlation Matrix · Shows how to calculate the volatility of a portfolio 

Lecture 13  08:39  
Lectures(915) gives an overview of the sample moments of returns distributions, ‘downside’ risk statistics, and measures of covariation (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 Semivariance, Negative Semideviation, Skewness and Kurtosis · Discusses Covariance and Covariance Matrix · Discusses Correlation Coefficient and Correlation Matrix · Shows how to calculate the volatility of a portfolio 

Lecture 14  05:31  
Lectures(915) gives an overview of the sample moments of returns distributions, ‘downside’ risk statistics, and measures of covariation (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 Semivariance, Negative Semideviation, Skewness and Kurtosis · Discusses Covariance and Covariance Matrix · Discusses Correlation Coefficient and Correlation Matrix · Shows how to calculate the volatility of a portfolio 

Lecture 15  04:56  
Lectures(915) gives an overview of the sample moments of returns distributions, ‘downside’ risk statistics, and measures of covariation (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 Semivariance, Negative Semideviation, 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  
Lecture 16  21:06  
Lectures(1623) 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 

Lecture 17  05:40  
Lectures(1623) 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 

Lecture 18  06:04  


Lecture 19  07:45  


Lecture 20  15:19  


Lecture 21  12:36  


Lecture 22  01:42  


Lecture 23  08:31  


Section 5: Matrix Algebra  
Lecture 24  13:48  
Lecture(2329) 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 25  17:20  


Lecture 26  04:51  


Lecture 27  04:08  


Lecture 28  07:04  


Lecture 29  02:24  


Lecture 30  03:08  


Section 6: Probability Theory  
Lecture 31  18:42  
Lectures(3142) 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 

Lecture 32  14:29  


Lecture 33  06:59  


Lecture 34  14:18  


Lecture 35  09:33  


Lecture 36  11:07  


Lecture 37  05:40  


Lecture 38  02:35  


Lecture 39  08:23  


Lecture 40  01:54  


Lecture 41  01:55  


Lecture 42  09:00  


Section 7: Regression Analysis  
Lecture 43  05:34  
Lectures(4348) 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 44  14:49  


Lecture 45  08:57  


Lecture 46  03:54  


Lecture 47  03:49  


Lecture 48  03:22  


Section 8: Numerical Methods  
Lecture 49  08:30  
Lecture(4954) explains how to solve implicit equations, finite differences and simulation. These lectures: · Demonstrates the Bisection method for solving Nondifferential Equations · Demonstrate the NewtonRaphson method for solving Nondifferential Equations · Demonstrates Unconstrained Numerical Optimisation, Constrained Numerical Optimisation · Demonstrates Binomial Lattices for valuing options, Finite Difference Methods for valuing options and Simulation 

Lecture 50  07:27  


Lecture 51  03:33  


Lecture 52  06:25  


Lecture 53  05:45  


Lecture 54  06:21  


Section 9: Conclusion: PRMII  
Lecture 55 
Conclusion PRMII

04:35  
Section 10: PRMII : QUIZ  
Quiz 1 
PRMII : QUIZ

15 questions 
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