The examined part of the CQF program comprises 6 modules and Advanced Electives. Each module covers a different aspect of quantitative finance and consists of lectures and discussions. Below is a summary of what is covered in each module, please click to see the full module content.
1. Building Blocks of Quantitative Finance
- Random Behavior of Assets
- Important Mathematical Tools and Results
- Taylor Series
- Central Limit Theorem
- Partial Differential Equations
- Transition Density Functions
- Fokker-Planck and Kolmogorov
- Stochastic Calculus and Itô’s Lemma
- Manipulating Stochastic Differential Equations
- Martingales
- The Binomial Model for Asset Prices
2. Quantitative Risk & Return
- Risk Regulation and Basel III
- Value at Risk and Expected Shortfall
- Modern Portfolio Theory
- Portfolio Optimization for Portfolio Selection
- Asset Returns: Key, Empirical Stylised Facts
- Capital Asset Pricing Model
- Volatility Filtering (GARCH Family)
- Volatility Models: The ARCH Framework
- Collateral and Margins
- Sharpe Ratio and Market Price of Risk
- Arbitrage Pricing Theory
- The Black-Litterman Model
- Liquidity Asset Liability Management
- High Frequency Data
3. Equities & Currencies
- The Black-Scholes Model
- Hedging and the Greeks
- Option Strategies
- Early Exercise and American Options
- Finite-Difference Methods
- Monte Carlo Simulations
- Exotic Options
- Volatility Arbitrage Strategies
- Martingale Theory for Pricing
- Girsanov’s Theorem
- Advanced Greeks
- Derivatives Market Practice
- Advanced Volatility Modeling in Complete Markets
- Non-probabilistic Volatility Models
4. Data Science and Machine Learning
- An Introduction to Machine Learning
- Statistical Methods for Data Analysis
- Big Data in Finance
- Classification and Clustering
- Dimension Reduction and PCA
- Filtering and Trading Signals
- Machine Learning
- Predictive Analytics (Regression Family)
- Partial Least Squares
- Alternating Least Squares
- Bayesian Models and Inference
- Markov Networks
- Cointegration and Long-Term Relationships
- Statistical Methods for Data Analysis
- Data Science Lab
- AI Based Algo Trading Strategies Using Python
- Digital Signal Processing for Finance
- Reinforcement Learning
5. Fixed Income
- Fixed-Income Products and Market Practices
- Yield, Duration and Convexity
- OIS Discounting
- Stochastic Spot-Rate Models
- Affine Stochastic Models
- Probabilistic Methods for Interest Rates
- Change of Numéraire
- Heath, Jarrow and Morton
- Calibration
- Data Analysis
- Libor Market Model
- SABR Model
- Monte Carlo Methods, Brownian Bridge, Advances Schemes
- Quasi-Monte Carlo Methods, Sobol and more
- Multiple Curve Interest Rate Modeling
6. Credit Products & Risk
- Structural Models
- Reduced-Form Model and the Hazard Rate
- Credit Risk and Credit Derivatives
- CDS Pricing, Market Approach
- Risk of Default, Structural and Reduced Form
- Implementation of Copula Models
- Correlation Sensitivity and State Dependence
- Synthetic CDO Pricing
- X-Valuation Adjustment (CVA, DVA, FVA, MVA)
Advanced Electives
Choose two from the following online electives to specialize in your area of interest.
You will also be required to complete a practical project relating to one of the electives you have chosen.
- Algorithmic Trading
- Advanced Computational Methods
- Advanced Risk Management
- Advanced Volatility Modeling
- Advanced Portfolio Management
- Counterparty Credit Risk Modeling
- Behavioural Finance for Quants
- Data Analytics with Python
- Python Applications
- Machine Learning with Python
- R for Quant Finance
- Risk Budgeting: Risk-Based Approaches to Asset Allocation
- Fintech
- C++
