Course Modules

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
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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


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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
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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
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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
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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)
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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++
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