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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.

 



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