Programme Content
Module 1: Building blocks
Financial markets, products and institutions
Principle of no-arbitrage and pricing of forwards, futures and options
Essentials of calculus, linear algebra, probability theory and stochastic processes for quantitative finance
Properties of Brownian motion and applications of Ito’s lemma
Module 2: The Black-Scholes model, its applications and extensions
Different ways to get to the Black-Scholes model and their equivalence
The idea of option Greeks, volatility smile and local and stochastic volatility models
Early exercise feature, barriers and design of structured products
The numeraire change toolkit and the Libor Market Model
Module 3: Machine learning applications in finance
PAC learning framework for machine learning
Support vector machines, Neural networks and LASSO
Online learning and reinforcement learning
Importance sampling and Markov Chain Monte Carlo methods
Module 4: Advanced topics in risk management
Value at risk, conditional value at risk and other tail risk measures
Probability of default, expected loss and credit risk
The Merton and the Moody’s-KMV model of credit risk
Backtesting, stress testing and model risk management
Counterparty risk: CVA, DVA and other adjustments
Pedagogy: The pedagogy will be highly interactive, consisting of a blend of lectures, real life case studies, in-class exercises, assignments and a supervised group project. In the final campus module, all groups would be required to present their project and findings to the entire class.
