Advanced Risk and ℙortfolio Management®
Multi-channel study materials for advanced Data Science and Quantitative Finance
Your aid to learn by listening
Your resource to learn by abstract principles
Your tool to learn by real-world examples
Your device to learn by visualization
Your medium to learn step by step
Your source to learn by key items
About the ARPM Lab
The Advanced Risk and Portfolio Management (ARPM) Lab is a repository of study materials to learn and teach advanced data science, quantitative risk management and portfolio construction.
The theory is general, mathematically rigorous, and uses homogenous notation across disparate topics. The intuition is provided by data animations. The applications span asset management, banking and insurance.
The ARPM Lab is constantly updated across topics and learning channels.
The topics of the ARPM Lab span the entire spectrum of Modern Quantitative Finance, across asset management, banking, and insurance, from the foundations to the most advanced developments.
In one framework and relying on one consistent notation, the ARPM Lab facilitates connections across disparate topics, and covers:
- All the major asset classes: equities (public/private), fixed income, credit, currencies, alternatives, high-frequency, enterprise, etc.
- The most advanced techniques: data science and machine learning, factor modeling, portfolio construction, algorithmic trading, investment risk measurement, liquidity modeling, enterprise risk management, etc.
While most materials on quantitative finance focus on asset pricing and risk neutral valuation (“Q”), the ARPM Lab focuses on the much broader applications to real world probability (“P”, learn more).
Different people learn in different ways. To facilitate the different learning styles of disparate audiences, the ARPM Lab is accessible via 8 interconnected Learning Channels:
The interconnections among the channels maximize the effectiveness of unstructured, “bottom-up” learning, which does not follow the recommended dependencies of the seven Learning Modules.
For instance, one may land on a piece of interactive code, and follow the code forward and backward across different topics; or switch to the theory on that topic to deepen understanding, or watch a video first, and then try an exercise.