Financial Risk Management and Engineering with Python

About

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

Course leader

Yuriy Zabolotnyuk

Target group

To apply for the course, you must have passed a relevant Bachelor's degree.
The applicant must have completed courses in Basic Math and Statistics. Prior exposure to Financial Derivatives is recommended. Prior experience with Python or other programming languages (e.g., R, Stata, Matlab) is not required but recommended.

Fee info