Full class

Machine Learning in Asset Pricing:

PhD Class, Autumn 2020

Syllabus:

  1. Basic Econometrics:
    • Linear Model
    • GLM
    • GAM
  2. Machine Learning:
    • Introduction
  3. Unsupervised Learning:
    • Data Preperation
    • Clustering
    • Cluster Validation and other techniques
  4. Supervised Learning:
    • Regularized Learning
    • Support Vector Machines
  5. Asset Pricing:
    • Introduction to Empirical Asset Pricing
    • Factor Models and APT

Advanced Financial Applications in Excel:

MSc Class, Spring 2017 to 2019

Syllabus:

  1. Financial Analysis:
    • Bond Pricing
    • Algorithm Trading
  2. Portfolio Optimization:
    • Markowitz
  3. Option Valuation:
    • Random Sampling
    • Black-Scholes
    • Monte Carlo
  4. High Performance Cluster:
    • C++

Teaching assistant

Probability and Statistics:

BSc Class, Autumn 2017 to 2019

Syllabus:

  1. Data and Structure
  2. Descriptive Statistics
  3. Introduction to Probability
  4. Combinatoric
  5. Probability Distributions
    • discrete
    • continous