Lessons
- CFA L2: Quantitative Methods - Introduction
- Quants: Correlation Analysis
- Quants: Single Variable Linear Regression Analysis
- Standard Error of the Estimate or SEE
- Confidence Intervals (CI) for Dependent Variable Prediction
- Coefficient of Determination (R-Squared)
- Analysis of Variance or ANOVA
- Multiple Regression Analysis
- Multiple Regression and Coefficient of Determination (R-Squared)
- Fcalc – the Global Test for Regression Significance
- Regression Analysis and Assumption Violations
- Qualitative and Dummy Variables in Regression Modeling
- Time Series Analysis: Simple and Log-linear Trend Models
- Auto-Regressive (AR) Time Series Models
- Auto-Regressive Models - Random Walks and Unit Roots
- ARMA Models and ARCH Testing
- How to Select the Most Appropriate Time Series Model?
ARMA Models and ARCH Testing
- Autoregressive Moving Average Model (ARMA) = calculates an average value over a period of time to smooth fluctuations in a time series.
- ARMA models are very sensitive to minor changes and may rarely forecast well.
- Auto Regressive Conditional Heteroskedasticity (ARCH) testing = can be used to determine if an AR, MA, or ARMA model suffers from conditional heteroskedasticity.
- The ARCH test models the error terms and if its slope is statistically significant, then the predictive AR, MA, or ARMA model under scrutiny is not valid.
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- Python for Data Science
- Machine Learning in Finance using Python
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