Intensive 2-day training (14 hours) – Theoretical approach and practical case studies

◆ Quantitative Finance Certification ◆

◉ Current Context

In an environment of increasing complexity of structured products, this training addresses key challenges:

• ► Increased volatility in financial markets
• ► Rapid evolution of regulations (FRTB, Basel IV)
• ► Growing need for advanced quantitative skills
• ► Increasing complexity of derivative and structured products

Quantitative Analysts Derivatives Traders Risk Managers Finance Students Financial Consultants

Practical Benefits by Profession

◉ In-person/remote training ◉ Teaching materials provided ◉ Training certificate ◉ Real case studies

Frequently Asked Questions

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I. Introduction and Foundations

• Definition of Quantitative Finance: Understanding its objectives and applications in financial markets.
• Different "Quant" Profiles: Trading, risk management, quantitative research, and algorithm development.
• Essential Mathematical Concepts:
  • Differential and integral calculus: fundamentals and financial applications
  • Linear algebra: matrices, determinants, and eigenvectors
  • Probability: fundamental laws, conditional expectation, and independence
  • Statistics: regressions, hypothesis testing, and parameter estimation

Practical Case Study:

Solving a system of equations to determine the expected return of 3 distinct assets.

II. Introduction to Stochastic Calculus

• Symmetric Random Walk, Brownian Motion, Stochastic Processes, and Itô's Lemma: Understanding the basics of continuous random processes.
• Black-Scholes Model and Binomial Tree:
  • Explanation of assumptions and the Black-Scholes formula
  • Binomial tree methodology for option valuation

Practical Case Study:

• Using Itô's lemma to solve the Black-Scholes partial differential equation
• Building a binomial tree to evaluate a European option

III. Interest Rate Models

• Vasicek Model - Application to bond markets and rate forecasting
• Cox-Ingersoll-Ross (CIR) Model - Rate evolution under variable volatility
• Hull-White Model - Incorporating deterministic terms in interest rate models
• Heath-Jarrow-Morton (HJM) Model - Comprehensive modeling of the yield curve
• Libor Market Model (LMM) - Applications in interest rate derivative valuation

Practical Case Study:

Using the Vasicek model to estimate interest rate evolution.

IV. Risk Management

• Risk Measurement and Management: Identifying different risk types (market, credit, counterparty, liquidity).
• Value at Risk (VaR) and Conditional VaR (CVaR): Tools for measuring extreme potential losses.
• Stress Testing and Scenario Analysis: Anticipating and simulating extreme market shocks.

Practical Case Study:

Calculating VaR and CVaR.

V. Portfolio Management

• Efficient Frontier and Capital Allocation Line - Foundations of modern portfolio theory
• Sharpe Ratio and Optimization - Maximizing risk-adjusted returns
• Capital Asset Pricing Model (CAPM) and Multi-Factor Models - Explanation and implementation of asset valuation models
• Mean-Variance Optimization (MVO) and Black-Litterman Model - Traditional and advanced portfolio optimization approaches
• Introduction to Entropy Pooling

Practical Case Study:

Building an asset portfolio using different models.

VI. Valuation and Pricing

• Exotic Options:
  • Understanding complex options and their volatility
  • Developing customized hedging strategies
• Exotic Option Pricing:
  • Analytical models: binomial trees, Black-Scholes
  • Numerical models: Monte Carlo, finite differences
• Credit Derivative Pricing and Valuation:
  • Introduction to correlation, dependence structures, and marginal distributions
  • Copula models, CDOs and first-to-default
  • Hedging techniques for extreme risks

Practical Case Study:

Using Excel-based pricers with copula models.

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