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Advanced Excel and VBA for Financial Modeling
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Introducing Risk management
Risk Management - Slides and Source Code
Introduction
Factor Risk Models
Case Studies
Mean Variance
Correlations
Outlining an Approach to Risk Management
Overall Approach
Portfolio Mean Variance
Factor Models
Factor Variance Calc
VaR
VaR - Pros and Cons
RIsk Modeling in Excel/VBA
Yahoo Finance
Returns
VBA Cov
Factor Regressions
Factor Model Risk
Scenario Risk
Va R Calc
Factor Analysis and PCA
Factor Analysis and the Link to Regression
Factor Analysis and PCA
Basic Statistics Required for PCA
Mean and Variance
Covariance and Covariance Matrices
Covariance vs Correlation
Diving into Principal Components Analysis
The Intuition Behind Principal Components
Finding Principal Components
Understanding the Results of PCA - Eigen Values
Using Eigen Vectors to find Principal Components
When not to use PCA
PCA in Excel
Setting up the data
Computing Correlation and Covariance Matrices
PCA using Excel and VBA
PCA and Regression
Introducing Numerical Optimisation
Optimisation - Slides and Source Code
Introduction
Balance
Framing the Problem
Solving the problem
Applications
PortfolioAllocation
Regression
Gradient Descent
Linear Programming and the Simplex Method
Wyndor
Standard Dual
Micro Econ
Graphical
Simplex Intuition
Simplex Mechanics
Simplex Extensions
Implementing Linear Programming in Excel
Outlining our Approach
Assembling Data
Linear Estimations
Solver
VBA for Covariance
Quadratic Optimization
Understanding Integer Programming
Integer Programming
LP Relaxation
Flaws Naive LP
Applications
Either Or Constraints
Unusual Forms
Implementing Integer Programming in Excel
Integer Constraints
Leverage and Long-bias Constraints
Solver for Integer Programming
Connect the Dots with Linear Regression
Using Linear Regression to Connect the Dots
Two Common Applications of Regression
Extending Linear Regression to Fit Non-linear Relationships
Basic Statistics Used for Regression
Understanding Mean and Variance
Understanding Random Variables
The Normal Distribution
Simple Regression
Setting up a Regression Problem
Using Simple regression to Explain Cause-Effect Relationships
Using Simple regression for Explaining Variance
Using Simple regression for Prediction
Interpreting the results of a Regression
Mitigating Risks in Simple Regression
Applying Simple Regression
Applying Simple Regression in Excel
Multiple Regression
Introducing Multiple Regression
Some Risks inherent to Multiple Regression
Benefits of Multiple Regression
Introducing Categorical Variables
Interpreting Regression results - Adjusted R-squared
Interpreting Regression results - Standard Errors of Co-efficients
Interpreting Regression results - t-statistics and p-values
Interpreting Regression results - F-Statistic
Applying Multiple Regression using Excel
Implementing Multiple Regression in Excel
Logistic Regression for Categorical Dependent Variables
Understanding the need for Logistic Regression
Setting up a Logistic Regression problem
Applications of Logistic Regression
The link between Linear and Logistic Regression
The link between Logistic Regression and Machine Learning
Solving Logistic Regression
Understanding the intuition behind Logistic Regression and the S-curve
Solving Logistic Regression using Maximum Likelihood Estimation
Solving Logistic Regression using Linear Regression
Binomial vs Multinomial Logistic Regression
Applying Logistic Regression
Predict Stock Price movements using Logistic Regression in Excel
Wyndor
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