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Advanced Excel and VBA for Financial Modeling
You, This Course and Us
Promo_CareerInFintech (3:16)
Introducing Risk management
Risk Management - Slides and Source Code
Introduction (12:30)
Factor Risk Models (10:26)
Case Studies (12:52)
Mean Variance (11:52)
Correlations (12:58)
Outlining an Approach to Risk Management
Overall Approach (12:53)
Portfolio Mean Variance (9:24)
Factor Models (9:17)
Factor Variance Calc (10:29)
VaR (11:34)
VaR - Pros and Cons (10:28)
RIsk Modeling in Excel/VBA
Yahoo Finance (10:33)
Returns (10:38)
VBA Cov (7:36)
Factor Regressions (10:12)
Factor Model Risk (5:56)
Scenario Risk (9:23)
Va R Calc (6:46)
Factor Analysis and PCA
Factor Analysis and the Link to Regression (8:05)
Factor Analysis and PCA (7:02)
Basic Statistics Required for PCA
Mean and Variance (6:05)
Covariance and Covariance Matrices (11:47)
Covariance vs Correlation (3:20)
Diving into Principal Components Analysis
The Intuition Behind Principal Components
Finding Principal Components (7:12)
Understanding the Results of PCA - Eigen Values (4:07)
Using Eigen Vectors to find Principal Components (2:31)
When not to use PCA (2:26)
PCA in Excel
Setting up the data (6:52)
Computing Correlation and Covariance Matrices (3:27)
PCA using Excel and VBA (5:51)
PCA and Regression (2:56)
Introducing Numerical Optimisation
Optimisation - Slides and Source Code
Introduction (6:51)
Balance (3:33)
Framing the Problem (8:27)
Solving the problem (10:19)
Applications (6:46)
PortfolioAllocation (5:58)
Regression (6:57)
Gradient Descent (5:54)
Linear Programming and the Simplex Method
Wyndor (7:27)
Standard Dual (7:04)
Micro Econ (6:13)
Graphical (7:37)
Simplex Intuition (7:47)
Simplex Mechanics (8:48)
Simplex Extensions (7:43)
Implementing Linear Programming in Excel
Outlining our Approach (3:55)
Assembling Data (3:52)
Linear Estimations (6:51)
Solver (5:28)
VBA for Covariance (5:49)
Quadratic Optimization (7:30)
Understanding Integer Programming
Integer Programming (6:03)
LP Relaxation (4:53)
Flaws Naive LP (7:00)
Applications (7:23)
Either Or Constraints (5:42)
Unusual Forms (7:18)
Implementing Integer Programming in Excel
Integer Constraints (4:29)
Leverage and Long-bias Constraints (3:29)
Solver for Integer Programming (4:45)
Connect the Dots with Linear Regression
Using Linear Regression to Connect the Dots (9:06)
Two Common Applications of Regression (5:26)
Extending Linear Regression to Fit Non-linear Relationships (2:37)
Basic Statistics Used for Regression
Understanding Mean and Variance (6:05)
Understanding Random Variables (11:27)
The Normal Distribution (9:31)
Simple Regression
Setting up a Regression Problem (11:38)
Using Simple regression to Explain Cause-Effect Relationships (4:59)
Using Simple regression for Explaining Variance (8:09)
Using Simple regression for Prediction (4:06)
Interpreting the results of a Regression (7:27)
Mitigating Risks in Simple Regression (7:58)
Applying Simple Regression
Applying Simple Regression in Excel (11:57)
Multiple Regression
Introducing Multiple Regression (7:05)
Some Risks inherent to Multiple Regression (10:08)
Benefits of Multiple Regression (3:49)
Introducing Categorical Variables (7:00)
Interpreting Regression results - Adjusted R-squared (7:04)
Interpreting Regression results - Standard Errors of Co-efficients (8:14)
Interpreting Regression results - t-statistics and p-values (5:34)
Interpreting Regression results - F-Statistic (2:53)
Applying Multiple Regression using Excel
Implementing Multiple Regression in Excel (8:54)
Logistic Regression for Categorical Dependent Variables
Understanding the need for Logistic Regression (9:26)
Setting up a Logistic Regression problem (6:04)
Applications of Logistic Regression (9:57)
The link between Linear and Logistic Regression (8:15)
The link between Logistic Regression and Machine Learning (4:18)
Solving Logistic Regression
Understanding the intuition behind Logistic Regression and the S-curve (6:23)
Solving Logistic Regression using Maximum Likelihood Estimation (10:04)
Solving Logistic Regression using Linear Regression (5:34)
Binomial vs Multinomial Logistic Regression (5:23)
Applying Logistic Regression
Predict Stock Price movements using Logistic Regression in Excel (9:52)
Interpreting Regression results - t-statistics and p-values
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