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.