Investment Portfolio Downside Risk Analysis in Python

Master downside risk analysis for investment portfolios with Python in this hands-on, project-based bundle. Start with an introduction to time series manipulation and drawdown analysis, where you'll learn how to load and preprocess financial data efficiently. You'll explore the nuances of working with timestamp and datetime data, ensuring you're well-equipped to handle time-indexed datasets. As you move forward, you'll gain a deep understanding of drawdowns, a key concept in risk analysis, and learn to calculate them in Python, enabling you to assess portfolio performance during market downturns.

In the next section, dive into advanced downside risk measures. You’ll work with hedge fund data and explore semideviation, a crucial metric that captures the volatility of negative returns. The series also covers the Value at Risk (VaR) metric, guiding you through its calculation and interpretation for risk assessment. Finally, you’ll explore Conditional Value at Risk (CVaR) and Parametric Gaussian VaR, powerful tools for assessing extreme market risk, as well as the Cornish-Fisher modification, which adjusts for non-normal return distributions. By the end of this bundle, you’ll have a comprehensive toolkit for analyzing and managing downside risk in investment portfolios using Python.