Quant

Time Series Workshop

Topic: An introduction to time series methods with a focus on macroeconomic and financial applications
Language: R
Materials
- Slides are here. Please excuse typos. This is a work in progress.
- QMD R File is here
- Compiled Code here
Intended Audience and Seminar Details
- Users are expected to have a working knowledge of statistics, econometrics, and some machine-learning
- Users are expected to be comfortable with R.
Content:
- Time series EDA
- Explanatory methods (e.g., regression with time series data)
- Forecasting (e.g. linear and nonlinear ML techniques; simulation methods; Vector valued time series)

Topic: Common scripts used in quantitative finance / financial economics
Code: (github)
Language: R
Intended Audience:
- Used in some of my daytime MBA classes at KFBS (UNC)
- No prior knowledge of R is expected
- Users are expected to have an understanding of asset pricing, asset allocation, and a working knowledge of basis statistics
Content:
- Covers 4 key stages of any empirical project in this field
1. Explore (data acquisition, cleaning, EDA),
2. Explain (model development for CAPM, APT, Event Studies, etc..),
3. Predict (forecasting techniques),
4. Protect (via portfolio construction and risk measurement)

Topic: Lecture notes for basic quantitative methods used in asset allocation
Slides:
- Step 1: Explore the data
- Step 2: Explain the asset movement
- Step 3: Forecast returns / vol / comovement
- Step 4: Protect via diversification
Intended Audience:
- Slides in a 14 session daytime MBA course at KFBS (UNC)
- Users are expected to have some working knowledge of asset allocation (e.g. CAPM, MPT), basic statistics and regression

Topic: Primer on statistics and regression needed for causal inference
Slides:
- Statistical Inference
- Regression
Intended Audience:
- Primer on use for students across various programs
- Users are expected to have some exposure to statistics