# ECONOMICS | LABORATORIO DI STATISTICA E DATA MINING

## ECONOMICS LABORATORIO DI STATISTICA E DATA MINING

 0222210013 DIPARTIMENTO DI SCIENZE ECONOMICHE E STATISTICHE EQF7 ECONOMICS 2017/2018

 YEAR OF DIDACTIC SYSTEM 2016 PRIMO SEMESTRE
SSD CFU HOURS ACTIVITY TYPE OF ACTIVITY SECS-S/01 10 60 LESSONS OPTIONAL SUBJECTS
 CIRA PERNA T GIUSEPPE STORTI MICHELE LA ROCCA FRANCESCO GIORDANO
Objectives
KNOWLEDGE AND UNDERSTANDING
PROVIDE THE STUDENTS WITH ADVANCED METHODOLOGICAL AND COMPUTATIONAL TOOLS FOR FORECASTING FINANCIAL TIME SERIES.

APPLYING KNOWLEDGE AND UNDERSTANDING
MAKE THE STUDENTS ABLE TO IMPLEMENT IN R LANGUAGE ADVANCED TECHNIQUES FOR ANALYZING AND FORECASTING FINANCIAL TIME SERIES.
Prerequisites
BASIC KNOWLEDGES OF STATISTICS
Contents
PART I
ELEMENTS OF SIMULATION THEORY. THE BOOTSTRAP PROCEDURE. BOOTSTRAP FOR DEPENDENT DATA. BOOTSTRAP ESTIMATION OF THE FORECAST DENSITY OF RETURNS IN ARMA-GARCH MODELS. FORECAST INTERVALS FOR VAR AND ES. ASSESSING DENSITY FORECASTS: PITS, BERKOWITZ'S TEST, SCORING RULES. CASE STUDIES.

PART II
NON PARAMETRIC ESTIMATORS AND DEPENDENT DATA, MAIN PROPERTIES, APPLICATIONS FOR FINANCIAL TIME SERIES.
Teaching Methods
LECTURES AND COMPUTER LABORATORY SESSIONS
Verification of learning
ORAL EXAM WITH DEFENSE OF A PROJECT WORK
Texts
GNEITING, TILMANN AND RAFTERY, ADRIAN E., (2007), STRICTLY PROPER SCORING RULES, PREDICTION, AND ESTIMATION, JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 102, ISSUE , P. 359-378.

MORGAN B. (1984) ELEMENTS OF SIMULATION (CHAPMAN & HALL/CRC TEXTS IN STATISTICAL SCIENCE).

LORENZO PASCUAL, JUAN ROMO, ESTHER RUIZ (2006) BOOTSTRAP PREDICTION FOR RETURNS AND VOLATILITIES IN GARCH MODELS, COMPUTATIONAL STATISTICS & DATA ANALYSIS, VOLUME 50, ISSUE 9,PAGES 2293-2312, ISSN 0167-9473, HTTP://DX.DOI.ORG/10.1016/J.CSDA.2004.12.008.
(HTTP://WWW.SCIENCEDIRECT.COM/SCIENCE/ARTICLE/PII/S0167947304004001)
More Information
FURTHER MATERIAL (DATA, SOFTWARE, LECTURE NOTES) WILL BE PUBLISHED ON THE INSTRUCTOR'S WEBSITE.
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