Advanced Risk and Portfolio Management

About the ARPM Lab

The ARPM Lab^® (Advanced Risk and Portfolio Management Lab) is a constantly updated online platform for learning and teaching the entire spectrum of Data Science and its applications to 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

• the most advanced quantitative techniques: factor modeling, machine learning, stochastic processes, portfolio construction, algorithmic trading, investment risk measurement, liquidity modeling, enterprise risk management, and more
• all major asset classes: equities (public/private), fixed-income, credit, currencies, alternatives, high-frequency, enterprise, and more

Learning the ARPM Lab by topic

The ARPM Lab has been and is being developed “bottom-up” to address the practitioners’ needs, but is organized and presented “top-down” in a structured, academic manner.

The Data Science contents are organized around the observation that advanced statistical techniques can be interpreted as generalizations of normal models with linear transformations. Accordingly, we start from linear factor models, then cover static machine learning, then sequential decision problems, refer to the summary of the “Data Science Map”.

The Quantitative finance contents are built around the sequential steps of the business process: financial engineering, risk management, and portfolio management. While most references on quantitative finance focus on risk neutral asset pricing (“$Q$”), the ARPM Lab focuses on the much broader applications to real world probability (“$P$”), directly connected to the recent advances in data science, refer to the summary of the “Quantitative Finance Checklist”.

The exhaustive body of knowledge of the ARPM Lab is most effectively learned in six learning modules, delivered as an overview in the Quant Bootcamp, and in depth in the Quant Marathon:

These six modules are tested in the practical projects of the ARPM Certification^®, which proves proficiency across all the parts of the ARPM Lab.

Learning the ARPM Lab by channel

Different people learn in different ways. To facilitate the different learning styles of disparate audiences, the ARPM Lab is accessible via a variety of 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.

The Learning Channels are (the statistics below refer to the most recent update):

Theory (2,870 pages)
The Theory is the pillar of the ARPM Lab. Explanations with formulas are self-contained, and laid out with grueling attention to a consistent notation, which facilitates connections across disparate topics. Geometrical arguments support intuition, and heuristics are favored over mathematical rigor.

Examples (1,682)
Examples illustrate the theory using analytical, numerical or code-based implementations, in order to consolidate the intuition of abstract theoretical concepts. Examples are wrapped in light blue boxes, and signaled by the image of a bulb .

Data animations (209)
The Data animations, generated from data using code, explain complex models with visualizations in motion. To access the Data animations, users should click on the “play” icon on the right of a figure/still frame.

Code (Python: 65,100 lines, MATLAB: 13,622 lines, R: 4,476 lines)
The Code allows the user to absorb hands-on the contents of the ARPM Lab, understanding all the practical implications behind the Theory. The Code is editable and executable interactively from any browser, without any software installation. The Code is available in Python, MATLAB^® and R. To access the Code, the user can click on the code icon , and then select the language of choice.

Slides (3,907) 
The Slides summarize all the materials. To access the multi-media Slides, the user can click on the slide icon at the top of each page.

Exercises (1,905)
The Exercises support the learning and help the user master the analytical aspects of the Theory. To access the Exercises, the user can click on the exercises icon .

Audience and prerequisites

Access to the ARPM Lab topics (Section 1) through different, interconnected channels (Section 1) enables a diverse audience to benefit.

A solid quantitative background from an undergraduate program in the hard sciences allows for full absorption of the materials. No finance or coding knowledge are necessary, as both are developed during the ARPM experience.

Professional and academic backgrounds

The following categories represent the ideal audience for the ARPM experience.

• Risk managers and portfolio managers with an undergraduate degree in the hard sciences, who wish to learn the principles behind the recipes that they implement every day, and wish to access a comprehensive reference for the most advanced techniques in their field.
• Computer/data scientists, who want to learn the financial applications of their skills.
• Derivative quants and quantitative actuaries, who wish to quickly switch from their field to a new one, leveraging the mathematical knowledge they already possess.
• Students (advanced undergraduates and master level) in quantitative finance and hard sciences.
• Academics in the hard sciences, who wish to learn and/or teach data science for finance, risk management, and portfolio management in the concise, rigorous language to which they are accustomed.

Mathematical proficiency

Throughout the ARPM experience, intuition is supported by visualizations. Heuristic arguments are favored over mathematical rigor. The mathematical formalism is used up to, and not beyond, the point where it eases the comprehension of the subject.

However, users extract the most value from the ARPM experience if they have a hard science undergraduate degree, or working knowledge of the following:

• Linear algebra: matrix/vector notation and manipulations, trace, determinant, eigenvectors, eigenvalues.
• Multivariate calculus: derivatives, integrals, and Taylor expansions.
• Statistics: basic concepts of distributions, probability density function, and cumulative distribution function.

Contact us at info@arpm.co if you are interested in the Mathematics preparation material and/or in brushing up on those topics.

Programming proficiency

No coding experience is needed to fully benefit from the ARPM experience: coding skills can be, although need not be, acquired along the way, by working through the interactive code provided.

However, for users interested in learning to code or brushing up their coding skills, Coding Refreshers in the supported languages are available.

Finance proficiency

No knowledge of finance is necessary to benefit from the ARPM experience, although prior exposure to basic financial products would make the absorption of the materials faster.