The course focuses on developing and applying quantitative risk management and financial engineering models using Python. Students will gain a strong understanding of key risk frameworks and financial models and learn to use computational tools for real- world data analysis, modelling, and visualization.

The course will cover the following topics:

• Financial risks, data handling, and regulatory frameworks.
• Volatility estimation, return distributions, Value-at-Risk (VaR), and Expected Shortfall (ES).
• Stress testing, scenario analysis, and risk factor mapping.
• Credit risk modelling, default probability, structural models, and credit valuation adjustments.
• Option pricing: binomial, Black-Scholes models, and the Greeks.
• Hedging strategies, derivatives applications, and portfolio management.
• Mean-variance optimization, asset allocation, and risk parity strategies.
• Monte Carlo simulation, GARCH models, and stochastic processes.
• Python implementation of risk models, calibration, and backtesting.
• Regulatory capital frameworks, systemic risk, and climate/ESG risk integration