Tutorial: R-Adamant: Applied Financial Analysis and Risk Management

Fausto Molinari, R-Adamant
Enrico Branca, R-Adamant
Francesco De Filippo, R-Adamant
Rocco Claudio Cannizzaro, R-Adamant


Quantitative models and financial econometrics techniques are extensively used to analyse and forecast data trends, exploit hidden relationships and, ultimately, quantify and effectively manage risks. The workshop will introduce R-Adamant functionalities, walking users through the analysis of stock market data and the creation of risk efficient portfolios, evaluating their performance and testing the effects of stressed macro economic scenarios on the underlying assets.


Topics will include:

Intended Audience

This tutorial is designed for everybody, from university students and researchers to experienced professionals and managers. Even if you have never programmed with the R language or have no extensive experience in programming, you will be able to successfully complete the tutorial's workshops and understand how R-adamant can help in financial analysis.


Elementary knowledge of general statistical concepts and models is assumed; basic knowledge of R programming and general financial background is beneficial, although not necessary. We expect participants to bring their own laptops with a recent version of R and the R-Adamant package already installed. There are no particular requirements on the operating system.

Workshop Materials

The R-Adamant package and sample data for the tutorial, together with the slides will be made available on the R-Adamant website.
Please check here for up to date tutorial resources.


[1] Markowitz, Harry M., Portfolio Selection, second edition, Blackwell (1991).

[2] Bernstein, William J. and Wilkinson, David, Diversification, Rebalancing and the Geometric Mean Frontier, research manuscript (November 1997).

[3] Granger, Clive (1991). Modelling Economic Series: Readings in Econometric Methodology. Oxford University Press. ISBN 978-0198287360.

[4] Davidson, Russell; James G. MacKinnon (1993). Estimation and Inference in Econometrics. Oxford University Press. ISBN 978-0195060119.

[5] Giovanni Petris, Sonia Petrone, Patrizia Campagnoli, Dynamic Linear Models with R (Use R), August 10, 2007.

[6] Steven M. Kay, Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory [Hardcover], 1993.

[7] James D. Hamilton, Time Series Analysis.