This course provides an introduction to Quantitative Finance. In particular, the course will cover the following topics:
- Market Risk including metrics for measuring market risk at both the individual security and the portfolio level including the Greeks, Value-at-Risk, Stress Testing, Expected Shortfall etc.
- Credit Risk at both the individual instrument and portfolio level. Models covered will include Logisitc Regression, CreditMetrics, KMV etc.
- Valuation Theory: Here we will cover various models for valuing derivative instruments ranging from the standard Black-Scholes model to more complex models such as jump-diffusion, Hull-White and the LIBOR Market Model.
- Introduction to Fintech Models used in Quantitative Finance including K-Nearest Neighbours, Neural Networks and Kalman Filter for modelling dynamic Beta.
- Examples will be provided using Python code and Excel Examples. Fincad Analytics Suite we also be used for the section on Valuation theory.
