Meucci Project

Background

Attilio Meucci runs an annual Advanced Risk and Portfolio Management bootcamp in New York City. The bootcamp attracts academics and professionals within the industry, and over 6 intense days, topics and techniques in Risk Management and Portfolio Management are discussed in depth. This project will help develop the core functions that underlie the R scripts on the "ARPM Lab". The goal for this project is to enable the use of R code on the "ARPM Lab" by taking existing scripts, and potentially code in other languages, and then writing or converting them into R.

Related work

This project takes its inspiration from previous GSoC projects in 2012, 2013 and 2014, as well as the new interactive "ARPM Lab". Meucci's innovations include Entropy Pooling (technique for fully flexible portfolio construction), Factors on Demand (on-the-fly factor model for optimal hedging), Effective Number of Bets (entropy-eigenvalue statistic for diversification management), Fully Flexible Probabilities (technique for on-the-fly stress-test and estimation without re-pricing), and Copula-Marginal Algorithm (algorithm to generate panic copulas). The development version of the functions backing the "ARPM Lab" scripts is available at https://github.com/R-Finance/Meucci.

We envision the following steps for this project:

1. Review:

• Familiarization with the theory.
• Create examples using normal distribution, student-t distribution, and skew student-t assumptions for steps 1-4 of the Checklist
• Complete documentation.

2. Implementation:

• Work with the GSoC mentor(s) and write core script functions, which will be used on the ARPM Lab scripts

The project will be developed with https://www.rstudio.com/ and stored on https://github.com. In order to efficiently manage the development of the package, the various tasks and deadlines will be managed via https://asana.com/.

Mentors

Erol Biceroglu, Prof. Brian Peterson and Prof. Dr. David Ardia.

Tests

Applicants have to be able to show that they have:

• A very good working knowledge of programming in R, (with the potential to use Rcpp and C++).
• A very good working knowledge of Roxygen for the documentation.
• A very good working knowledge of knitr/LaTeX for the vignette.
• Familiarities with the construction of R packages.
• Good coding standards (Google’s C++ and R style guide).
• Good knowledge of Meucci's method and familiarity with "The Checklist" (formerly "The Prayer")
• Experience with Risk Management and Portfolio Optimization
• Experience with PerformanceAnalytics and PortfolioAnalytics packages
• Experience with GitHub.

Students should show their motivation by following the points below:

• Easy: Reproduce all steps in "The Checklist" for equities only, using a multivariate distribution and Minimum-Variance optimization.
• Medium: Reproduce all steps in "The Checklist" for equities and bonds, using marginal-distributions and a copula, as well as Minimum-Variance optimization or Minimum-CVaR optimization.
• Hard: Reproduce all steps in "The Checklist" for equities bonds, and options, using one of Meucci's innovations, such as Entropy Pooling.

Solutions of tests

Students, please post a link to your test results here.

References

Meucci, Attilio. 2005. “Risk and Asset Allocation.” Springer Finance Textbooks. https://www.arpm.co/book/.

Meucci, Attilio, Fully Flexible Views: Theory and Practice (August 8, 2008). Fully Flexible Views: Theory and Practice, Risk, Vol. 21, No. 10, pp. 97-102, October 2008. Available at SSRN: https://ssrn.com/abstract=1213325