Alessandra AMENDOLA | STATISTICS FOR FINANCE AND INSURANCE
Alessandra AMENDOLA STATISTICS FOR FINANCE AND INSURANCE
cod. 0222400017
STATISTICS FOR FINANCE AND INSURANCE
| 0222400017 | |
| DEPARTMENT OF ECONOMICS AND STATISTICS | |
| EQF7 | |
| STATISTICAL SCIENCES FOR FINANCE | |
| 2024/2025 |
| OBBLIGATORIO | |
| YEAR OF COURSE 1 | |
| YEAR OF DIDACTIC SYSTEM 2014 | |
| SPRING SEMESTER |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| SECS-S/01 | 5 | 30 | LESSONS |
| Exam | Date | Session | |
|---|---|---|---|
| AMENDOLA | 19/12/2024 - 09:30 | SESSIONE ORDINARIA | |
| AMENDOLA | 19/12/2024 - 09:30 | SESSIONE DI RECUPERO |
| Objectives | |
|---|---|
| THE COURSE FOCUSES ON REGRESSION TOOLS FOR FINANCIAL AND INSURANCE DATA ANALYSIS. PARTICULAR EMPHASIS WILL BE GIVEN TO REGRESSION MODELS AND INFERENCE TOOLS FOR THE ANALYSIS CONTINUOUS, DISCRETE AND MIXED TYPE DATA. KNOWLEDGE AND UNDERSTANDING: - KNOWLEDGE OF ANALYSIS OF THE STATISTICAL TOOLS USEFUL FOR THE QUANTITATIVE STUDY OF REAL PHENOMENA OF INTEREST IN FINANCE AND INSURANCE, FOR THE UNDERSTANDING OF PROBLEMS AND IMPROVEMENT OF DECISION-MAKING PROCESSES. - KNOWLEDGE OF DESCRIPTIVE-EXPLORATORY AND INFERENTIAL PROCEDURES USEFUL TO SUPPORT DECISIONS REGARDING PHENOMENA AND/OR FINANCIAL AND ACTUARIAL SYSTEMS WHERE LARGE AMOUNTS OF DATA, VARIABILITY AND UNCERTAINTY IMPLY A LEVEL OF COMPLEXITY UNMANAGEABLE USING OTHER TECHNIQUES. - ABILITY TO ANALYZE AND INTERPRET QUANTITATIVE INFORMATION, AND TO PRODUCE INDICATORS AND REPORTS SUPPORTING CONTROL AND MANAGEMENT POLICIES OF COMPANIES, BOTH IN PUBLIC OR PRIVATE SECTORS, OPERATING IN THE FIELD OF FINANCE AND INSURANCE. APPLYING KNOWLEDGE AND UNDERSTANDING: - STUDENTS WILL BE ABLE TO INDEPENDENTLY ANALYZE AND EVALUATE DOCUMENTS AND REPORTS THAT INCLUDE QUANTITATIVE INFORMATION; FORMULATE CRITICAL JUDGMENTS ON HOW TO COLLECT DATA AND HOW TO PROCESS THE INFORMATION COLLECTED; EVALUATE THE VALIDITY, INTERNAL AND EXTERNAL, OF THE CONCLUSIONS. - STUDENTS WILL GAIN ABILITY TO PRESENT WITH PROPERTIES OF LANGUAGE, EFFECTIVELY AND CLEARLY, THE INFORMATION OF A QUANTITATIVE NATURE, BOTH IN ORAL AND WRITTEN FORM. - STUDENTS WILL ACQUIRE THE LOGICAL-CONCEPTUAL STRUCTURE NECESSARY FOR THE ANALYSIS AND PROCESSING OF QUANTITATIVE INFORMATION, WHILE PROVIDING THE ABILITY TO LINK THE SKILLS ACQUIRED WITH THOSE LEARNED IN THE COURSES OF STUDY MORE SIMILAR (ECONOMICS, FINANCE, MATHEMATICS, ASSET-PRICING). |
| Prerequisites | |
|---|---|
| IT IS REQUIRED A BASIC KNOWLEDGE OF MATRIX ALGEBRA AND STATISTICAL INFERENCE. |
| Contents | |
|---|---|
| EDA TOOLS. SCATTER AND BUBBLE PLOTS. GRAPHICAL REPRESENTATION FOR MULTIVARIATE DATA MATRICES. CORRELATION MATRIX. REGRESSION MODELLING. EXPLORATORY DATA ANALYSIS. SUMMARY STATISTICS AND GRAPHICAL VISUALIZATIONS OF DATASETS. (4 HOURS). INTRODUCTION TO THE LOGIC OF STATISTICAL MODELS. RELATIONSHIP BETWEEN STATISTICAL VARIABLES. CORRELATION MATRICES AND THEIR GRAPHICAL REPRESENTATION. (2 HOURS). THE LINEAR REGRESSION MODEL. INFERENCE, VALIDATION AND USE OF THE MODEL. MULTICOLLINEARITY. HETEROSKEDASTICITY AND AUTOCORRELATION: TESTS FOR HETEROSKEDASTICITY AND FIRST-ORDER AUTOCORRELATION. VARIABLE SELECTION. CASE STUDIES AND APPLICATIONS WITH THE SOFTWARE R (14 HOURS) LOGISTIC REGRESSION: MODEL ASSUMPTIONS, MODEL ESTIMATION, VALIDATION AND USE OF THE MODEL. CASE STUDIES AND APPLICATIONS WITH THE SOFTWARE R (10 HOURS) |
| Teaching Methods | |
|---|---|
| THE COURSE COMPRISES 30 HOURS OF IN-PERSON TEACHING SESSIONS THAT INCLUDE EXERCISES. THE LESSONS WILL BE SUPPORTED BY MULTIMEDIA AND WILL COVER THEORETICAL TOPICS. REAL-LIFE EXAMPLES AND CASE STUDIES WILL BE PRESENTED WITH THE HELP OF R SOFTWARE TO AID IN UNDERSTANDING THE CONCEPTS. THIS WILL HELP IN APPLYING THE THEORETICAL KNOWLEDGE PRACTICALLY AND INTERPRETING THE RESULTS IN REAL-LIFE SCENARIOS. |
| Verification of learning | |
|---|---|
| PROJECT WORK WITH DISCUSSION AND ORAL TEST. AS PART OF THE FINAL EXAM, STUDENTS WILL NEED TO DISCUSS THEIR PROJECT WORK AND TAKE AN ORAL TEST ON THE TOPICS COVERED IN THE COURSE. THE PROJECT WORK SHOULD BE DONE IN GROUPS OF 1-3 STUDENTS AND SHOULD INVOLVE THE APPLICATION OF THE METHODS AND TECHNIQUES PRESENTED DURING THE COURSE TO REAL DATA USING THE R SOFTWARE. CONTENTS AND METHODS OF CARRYING OUT THE PROJECT WORK MUST BE AGREED UPON WITH THE TEACHER DURING THE COURSE FOLLOWING THE DETAILED GUIDELINES THAT WILL BE PROVIDED AT THE BEGINNING OF THE COURSE AND UPON REQUEST OF THE STUDENTS. THE FINAL GRADE WILL BE BASED ON THE GROUP'S ACTIVE PARTICIPATION IN THE PROJECT WORK, THE INDIVIDUAL PRESENTATION OF THE PROJECT WORK, AND THE ORAL TEST EVALUATION. THE FINAL MARK WILL BE OUT OF THIRTY. |
| Texts | |
|---|---|
| 1. M. VEERBEK, A GUIDE TO MODERN ECONOMETRICS, WILEY 2. LECTURE NOTES AND JOURNAL PAPERS SUGGESTED BY THE INSTRUCTOR AVAILABLE ON INSTRUCTOR’S WEB PAGE |
| More Information | |
|---|---|
| LECTURE WILL BE GIVEN IN ENGLISH. GIVEN THE CHARACTERISTICS OF THE DISCIPLINE AND ITS THEORETICAL AND PRACTICAL APPROACH, THE ATTENDANCE IS STRONGLY RECOMMENDED, ALTHOUGH NOT MANDATORY. STUDENTS WHO DECIDE NOT TO ATTEND CLASSES ARE ADVISED TO FOLLOW THE SYLLABUS AND THE SCHEDULED TOPICS. |
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