Tutorial: R-Adamant: Applied Financial Analysis and Risk
De Filippo, R-Adamant
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:
Explanatory Data Analysis
Trend Analysis (WMA, EMA)
Spectral Analysis (Fourier transform, Periodogram)
Technical Analysis (Indicators, Oscillators)
Modelling and Forecasting
Exploit data relationships(Correlation,
Parameters Estimation (OLS, ML, VAR, GARCH regression
models, Sensitivity Analysis)
Time Series Forecasting (ARIMA, Monte-Carlo Simulation)
Performance evaluation (Sharpe's, Treynor's, Jensen's
Portfolio Optimization (MVO, Asset Diversification)
Risk Evaluation (Value at Risk, Expected Shortfall,
Macro Economic Stress Testing
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
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.
 Markowitz, Harry M., Portfolio Selection, second
edition, Blackwell (1991).
 Bernstein, William J. and Wilkinson, David,
Diversification, Rebalancing and the Geometric Mean
Frontier, research manuscript (November 1997).
 Granger, Clive (1991). Modelling Economic Series:
Readings in Econometric Methodology. Oxford University
Press. ISBN 978-0198287360.
 Davidson, Russell; James G. MacKinnon (1993).
Estimation and Inference in Econometrics. Oxford
University Press. ISBN 978-0195060119.
 Giovanni Petris, Sonia Petrone, Patrizia Campagnoli,
Dynamic Linear Models with R (Use R), August 10, 2007.
 Steven M. Kay, Fundamentals of Statistical Signal
Processing, Volume 2: Detection Theory [Hardcover], 1993.
 James D. Hamilton, Time Series Analysis.