Advanced Risk and ℙortfolio Management®

Lab

Multi-channel study/practice materials for advanced
Data Science and Quantitative Finance

Theory

2,680 pages

An encyclopedia of Modern Quantitative Finance
Your resource to learn by abstract principles

Theory 2,680 pages of explanations with formulas

Case studies

77 cases

Applications to practice beyond the Theory
Your tool to learn by real-world examples

Case studies 77 large scale analytics with real data

Code

123,725 lines

Three coding languages, no installation
Your environment to learn by doing

Code 123,725 lines of code in Python, MATLAB and R

Animations

213 clips

Simulations from data using code
Your device to learn by visualization

Data animations 213 visualizations of complex models in motion

Slides

4,014 slides

Summary of all the learnings
Your source to learn by key items

Slides 4,014 multimedia summaries of all the materials

Derivations

1,478 derivations

Derivations to support the analytical results
Your guide to learn by induction

Derivations 1,478 analytical derivations to support the Theory

Video clips on the topics
Your aid to learn by listening

Video lectures 531 walk-throughs of ARPM Lab topics

An encyclopedia of Modern Quantitative Finance
Your resource to learn by abstract principles

Theory 2,680 pages of explanations with formulas

Applications to practice beyond the Theory
Your tool to learn by real-world examples

Case studies 77 large scale analytics with real data

Simulations from data using code
Your device to learn by visualization

Animations 213 visualizations of complex models in motion

Three coding languages, no installation
Your environment to learn by doing

Code 123,725 lines of code in Python, MATLAB and R

One pseudo-code for all coding languages
Your medium to learn step by step

Documentation 694 pages of language-neutral pseudo-code

Summary of all the learnings
Your source to learn by key items

Slides 4,014 multimedia summaries of all the materials

Derivations to support the analytical results
Your guide to learn by induction

Derivations 1,478 analytical derivations to support the Theory

About the ARPM Lab

The ARPM Lab is the most complete repository of study/practice materials for advanced Data Science and Quantitative Finance.

The ARPM Lab’s Theory, Case studies, Code, Animations, and Slides are constantly updated and synced.

The ARPM Lab is the reference used by the instructors of the Quant Bootcamp and Quant Marathon.

Data Science

A solid background in Data Science, from the foundations to the most advanced developments, is essential to successfully implement Advanced Risk and Portfolio Management. The ARPM Lab covers the following areas.

Math primer	Mathematics Primers of linear algebra, calculus, functional analysis and optimization, to master the mathematical foundations of Quantitative Finance.
Static and dynamic models for multivariate randomness	Statistics Thorough coverage of distributions, copulas, location and dispersion features, correlation and statistical decision theory, to understand and model multivariate randomness.
Factor models and machine learning In-depth treatment of linear factor models, and generalizations to supervised and unsupervised machine learning models, for point and probabilistic prediction.
Stochastic processes Definition and properties of the most common mean-covariance and probabilistic processes, to model the dynamics of continuous and discrete-state variables, in continuous and discrete time.
Data and Views	Estimation Non-parametric, parametric, Bayesian, robust and shrinkage estimation techniques, to fit models to data in a meaningful way; estimation assessment methods, to optimize the bias-variance trade-oﬀ
Inference Conditioning, Black-Litterman, relative entropy minimization and generalized approaches to inject information and subjective views in the estimation, portfolio allocation, and other decision making processes

Math primer

Mathematics

Primers of linear algebra, calculus, functional analysis and optimization, to master the mathematical foundations of Quantitative Finance.

Static and dynamic models for multivariate randomness

Statistics

Thorough coverage of distributions, copulas, location and dispersion features, correlation and statistical decision theory, to understand and model multivariate randomness.

Factor models and machine learning

In-depth treatment of linear factor models, and generalizations to supervised and unsupervised machine learning models, for point and probabilistic prediction.

Stochastic processes

Definition and properties of the most common mean-covariance and probabilistic processes, to model the dynamics of continuous and discrete-state variables, in continuous and discrete time.

Data and Views

Estimation

Non-parametric, parametric, Bayesian, robust and shrinkage estimation techniques, to fit models to data in a meaningful way; estimation assessment methods, to optimize the bias-variance trade-oﬀ

Inference

Conditioning, Black-Litterman, relative entropy minimization and generalized approaches to inject information and subjective views in the estimation, portfolio allocation, and other decision making processes

Quantitative Finance

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). The Quantitative Finance contents are built around the sequential steps of the business process: valuation, the “Checklist” (data processing, ex-ante risk/portfolio management), and ex-post performance analysis.

	Valuation Holistic treatment of valuation methods across liquidity buckets: bid/ask or mark-to-market versus mark-to-model; and across models: risk-neutral models for derivative traders versus real-measure models for investment bankers and actuaries.
The “Checklist”: Ten steps for Advanced Risk and Portfolio Management	Data processing Five steps to map past raw market data into the distribution of the market over a future investment horizon, by means of advanced econometric, estimation and financial engineering techniques.
Risk management Three steps to assess the risk profile of a portfolio/firm, by means of risk measures, ex-ante risk/performance attribution models, and advanced stress-testing techniques.
Portfolio management Two steps to optimize the risk profile of the portfolio/firm, which is the ultimate goal of financial practitioners across the industry (asset management, banking, insurance).
	Performance analysis Ex-post attribution of the realized performance to diﬀerent decisions and stakeholders

Valuation

Holistic treatment of valuation methods across liquidity buckets: bid/ask or mark-to-market versus mark-to-model; and across models: risk-neutral models for derivative traders versus real-measure models for investment bankers and actuaries.

The “Checklist”: Ten steps for Advanced Risk and Portfolio Management

Data processing

Five steps to map past raw market data into the distribution of the market over a future investment horizon, by means of advanced econometric, estimation and financial engineering techniques.

Risk management

Three steps to assess the risk profile of a portfolio/firm, by means of risk measures, ex-ante risk/performance attribution models, and advanced stress-testing techniques.

Portfolio management

Two steps to optimize the risk profile of the portfolio/firm, which is the ultimate goal of financial practitioners across the industry (asset management, banking, insurance).

Performance analysis

Ex-post attribution of the realized performance to diﬀerent decisions and stakeholders

The colors in the above figures illustrate the coverage of the topics in the Quant Bootcamp and Quant Marathon courses.