## Advanced Risk and ℙortfolio Management®

# Lab

The online reference materials for advanced data science and quantitative finance

An encyclopedia of Modern Quantitative Finance

Your resource to learn by abstract principles

Your resource to learn by abstract principles

Theory 2,190 pages of explanations with formulas

Applications to practice beyond the Theory

Your tool to learn by real-world examples

Your tool to learn by real-world examples

Case studies 343 large scale analytics with real data

Simulations from data using code

Your device to learn by visualization

Your device to learn by visualization

Data animations 207 visualizations of complex models in motion

One pseudo-code for all coding languages

Your medium to learn step by step

Your medium to learn step by step

Documentation 679 pages of language-neutral pseudo-code

The code "documentation"

Full control over your scripts

Each function, script and param explained in detail

Summary of all the learnings

Your source to learn by key items

Your source to learn by key items

Slides 3,422 multimedia summaries of all the materials

Video clips on the topics

Your aid to learn by listening

Your aid to learn by listening

Video lectures 464 walk-throughs of ARPM Lab topics

Derivations to support the analytical results

Your guide to learn by induction

Your guide to learn by induction

Exercises 1,319 analytical derivations to support the Theory

## Overview

The ARPM (Advanced Risk and Portfolio Management) Lab is a constantly updated online platform for learning and teaching modern quantitative finance. The ARPM Lab spans 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:

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.

#### Organization of the ARPM Lab

The ARPM Lab is developed "bottom-up" to address the practitioners' needs, but is organized and presented "top-down" in a structured, academic manner, built around the sequential steps of the business process (see the figure): Valuation, ex-ante Risk/Portfolio Management (the "Checklist"), and ex-post Performance Analysis.

The quantitative business process is enabled by Factor Models and Learning and supported by a Quant Toolbox of techniques necessary to tackle these topics.

The quantitative business process is enabled by Factor Models and Learning and supported by a Quant Toolbox of techniques necessary to tackle these topics.

#### Learning the ARPM Lab by topic

The exhaustive body of knowledge of the ARPM Lab is best learned through seven Learning Modules
This is what we do in the ARPM Quant Bootcamp and in the ARPM Quant Marathon. The same seven modules are tested in the exams of the ARPM Certificate, 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 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.

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.

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