master degree course in statistics and informatics for business … · · 2014-08-26statistics...

Master Degree Course in

Statistics and Informatics for Business and Finance

Table of Contents

Master Degree Course in Statistics and Informatics for Business and Finance 2 Courses, Lecturers, Semesters 4 Courses Content 6

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Master Degree Course in Statistics and Informatics for Business and Finance (2 years study after Bachelor Degree) Admission requirements The requisites to apply for Master Degree in Statistics and Informatics for Business and Finance are the following: (a) bachelor degree with the duration of studies of three or four years; (b) other academic qualification obtained abroad and recognized as equivalent by the Department on the proposal of Degree Course Council and ratified by the Academic Senate. To be admitted to the Master Degree Course the specific curriculum requisites and adequate initial preparation are required. The initial preparation regards the knowledge of the basics of statistical disciplines, mathematics, informatics and the basics of economics and business disciplines. Course Profile The course aims to provide:

a deep knowledge of the methods and statistical models used by banks and firms for evaluating the risks of markets, credits and operational risks;

a solid preparation of actuarial techniques, financial mathematics, of other methodologies applied to solve insurance, social, and financial problems;

a good preparation in statistical techniques and informatics to support operative decisions and strategic choices of firms.

The course permits to acquire a deep knowledge of statistics methodologies and actuarial and financial mathematics aimed to the analysis of insurance and financial markets as well as informatics methodologies and tools to support the firm administration, market analysis and evaluation of credit and market risks. The course aims to form specialists in the field of actuarial, financial and social security techniques. The specialists will be able to apply the statistical knowledge and knowledge of informatics to decisional models for the administration of firms and markets. Expected achievements Knowledge and understanding The student gains a deep knowledge of statistical methods and models, of actuarial techniques and financial mathematics as well as of the methodologies and techniques of informatics and statistics to support managerial decisions of firms and market analysis. Skills to apply knowledge The student develops abilities in financial and business field to plan research and solve problems under uncertainty conditions, in organisation of databases of big dimensions, in construction of statistical models that are aimed to interpret and forecast firm’s decisions and to manage the financial portfolio. Formulation of personal opinion The student develops ability to formulate personal opinion and approach critically, the skills to work in groups that enforce capacity to evaluate and to manage uncertainty, to conduct surveys, to process and interpret data related to firm and market analysis. Communication skills The student develops professional competences and necessary tools to present rigorously statistical and financial analysis and to synthesise and interpret the obtained results. Learning skills The student develops necessary skills to independently identify adequate instruments, methodologies, sources and references to strengthen the professional competences, to proceed studies and to be involved into the labour market with high degree of autonomy and solid cultural backgrounds. This permits the student to adapt and to update continually. Employment opportunities The employment opportunities for the graduates are open in the field of insurance and reinsurance, societies of brokerage, societies of savings and administration of savings and other institutions operating in the field of finance sphere and social security, banking supervising and pension funds.

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The Master Degree in Statistics and Informatics for Business and Finance are dedicated for those who wish to access through the state examination to the profession of Actuary. Moreover, other employment opportunities are offered by private and public firms and by banks. In the firms to provide: market analysis, sails forecasting, evaluation of customer satisfaction, research and development, administration of ICT systems of the firm. In the banks to provide market risk evaluation and evaluation of credits and operational risk. The Degree Course gives the access to the following professions:

Actuary

Statistician Access to successive studies The master degree gives the access to the third cycle studies (PhD course, Specialisation Courses and second level Master courses). Requisites to obtain final degree To obtain the Bachelor Degree in Statistics and Informatics for Business and Finance the student must acquire 120 credits.

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DIDACTIC OFFER LIST OF COURSES ACTIVATED IN 2014/2015

I year (enrolled in 2014/2015)

Course Type of course

Field of course Sector Code

ECTS Lecturer Cycle Code

ECONOMIC WORKSHOP Other activities Other activities SECS-P/01

5 BRUNI Sergio 2°

DISCRETE-TIME MATHEMATICAL FINANCE

1 Characterising

Applied mathematics

SECS-S/06

10 COSTABILE

Massimo 1° - 2°

FISCAL, MONETARY POLICY AND NATIONAL

ACCOUNTING Refining

Refining and integrating activities

SECS-P/01

5 BRUNI Sergio 1°

ENTERPRISE INFORMATION SYSTEMS

Refining Refining and integrating activities

ING-INF/05

10 GARRO Alfredo

3° - 4°

ADVANCED STATISTICAL TECHNIQUESComposed in coordinated modules: a) TIME SERIE ANALYSIS

(5 ECTS) b) GENERALIZED LINEAR MODELS* (5 ECTS)

Characterising

Characterising

Statistics

Statistics

SECS-S/01

SECS-S/01

10

TARSITANO

Agostino

DOMMA Filippo

3° - 4° *b module is

borrowed from Statistic

Models for Economic

Phenomena, Business

Economics Degree Course (0750)

STATISTICAL MODELS (curriculum Statistics,

Finance and Insurance) Characterising Statistics

SECS-S/01

5 COSSARI

Antony 4°

STATISTICS FOR FINANCIAL

MARKET(curriculum

Statistics, Finance and Insurance)

Characterising Statistics SECS-S/01

10 PERRI Pier Francesco

3° - 4°

ACTUARIAL TECHNIQUE FOR LIFE INSURANCE

(curriculum Statistics, Finance and Insurance)


SECS-S/06

10 MENZIETTI Alessandro

1° - 2°

STATISTICAL METHODS FOR MARKETING

RESEARCHES (curriculum Statistics and

Informatics for Decisions and Market analysis)


5 ROMANO Rosaria

4°

STATISTICAL METHODS FOR MARKET ANALYSIS

(curriculum Statistics and Informatics for Decisions and

Market analysis)


10

CONDINO Francesca

3° - 4°

METHODOLOGIES AND TECHNIQUES FOR SAMPLE SURVEYS


Market analysis)


10

PERRI Pier Francesco

1° - 2°

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/bruni/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/costabile/


http://www.unical.it/portale/strutture/dipartimenti_240/disesf/esterni/garro/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/tarsitano/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/domma/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/cossari/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/perri/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/menzietti/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/romano/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/condino/


5

II year (enrolled in 2013/2014)

Course Type of course

Field of course Sector Code

ECTS Lecturer Cycle Code

APPLIED DEMOGRAPHY Characterising Applied statistics

SECS-S/04

5 DE BARTOLO

Giuseppe 1° -2°

CONTINUOUS-TIME MATHEMATICAL FINANCE

(curriculum Statistics, Finance and Insurance)

Characterising Applied

mathematics SECS-S/06

10 STAINO

Alessandro

1° -2°

PENSIONS MATHEMATICS (curriculum Statistics, Finance

and Insurance) Characterising

Applied mathematics

SECS-S/06

5 PIRRA Marco 1° -2°

RISK THEORY (curriculum Statistics, Finance

and Insurance) Characterising

Applied mathematics

SECS-S/06

5 CERCHIARA

Rocco

1° -2°

LOGISTICS SYSTEMS PLANNING


Market analysis)

Characterising Applied

mathematics MAT/09 10

PALETTA Giuseppe

1° -2°

COMPUTING SYSTEMS (curriculum Statistics and

Informatics for Decisions and Market analysis)


ING-INF/05

10 MASTROIANNI

Carlo

1° -2°

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/debartolo/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/staino/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/pirra/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/cerchiara/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/paletta/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/esterni/mastroianni/

6

Master Degree content course in Statistics and Informatics for Business and Finance 1st year

Course Code 27003271

Course Name Economic Workshop

ISCED Code

CFU (ECTS) 5

Course Year 1st Year Master Degree in Statistics and Informatics for Business and Finance

Semester Winter

Lecturer Prof. BRUNI Sergio

Activity Type Teaching

Total Hours / Hours per Week

30 / 6

Apprenticeship NO

Language of Instruction

Italian

Course Contents: National Income Accounting; The relation between inflation and the labour market in Italy: the estimation of di Phillips curve. The growth of public debt in European countries: the dynamics of debt on GDP.

Recommended or Required Reading

V. Siesto, La contabilità nazionale italiana. Bologna, Il mulino (latest edition). O. Blanchard, Macroeconomics: A European Perspective, Harlow, Pearson Education Limited , (latest edition).

Prerequisites Contents of Principles of Economics

Teaching Methods

Lectures, home works, group works.

Assessment Methods

Written and oral exam.

More Information

Lecturer’s webpage: http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/bruni/


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Course Name Discrete-Time Mathematical Finance

ISCED Code

CFU (ECTS) 10


Semester Winter

Lecturer Prof. COSTABILE Massimo



60 / 6

Apprenticeship NO


Italian

Course Contents: Choice theory under uncertainty. The expected utility theorem. The Allais paradox. Risk aversion. Investment decision of risk-averse individuals. First-order and second-order stochastic dominance. Mean-variance portfolio selection. Efficient frontier. The separation theorem. The Capital Asset Pricing Model. A multiperiod security market model. Information structures. Stochastic process models of security prices. Trading strategies. Value processes and gains processes. Self-financing trading strategies. Discounted prices. Conditional expectations and martingales. Arbitrage opportunities. Risk-neutral probability measure. The first fundamental theorem of asset pricing. The binomial model of Cox-Ross-Rubinstein. Convergence of the binomial formula to the Black-Scholes-merton formula for the value of a European option. Futures and forward contracts. Market completeness. The second fundamental theorem of asset pricing. The basic term structure model. The Black-Derman-Toy model. Interest rate swaps, caps and floors.


- Huang-Litzenberger, Foundations for Financial Economics, Prentice Hall. - Danthine-Donaldson, Intermediate Financial Theory, second edition, Elsevier Academic Press. - Pliska, Introduction to Mathematical Finance – Discrete Time Models, Blackwell Publishers. - Cox-Rubinstein, Option Markets, Prentice Hall. - Hull, Options, Futures, and other derivative securities, Prentice Hall.

Prerequisites Advanced calculus, basic probability theory, basic mathematical finance

Teaching Methods

60 hours of teaching

Assessment Methods

Oral

More Information

Lecturer’s webpage: http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/costabile/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/costabile/

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Course Name Fiscal, Monetary Policy and National Accounting

ISCED Code

CFU (ECTS) 5


Semester Winter

Lecturer Prof. BRUNI Sergio



30 / 6

Apprenticeship NO


Italian

Course Contents: National income accounting; fiscal policy and monetary policy in the IS-LM model. AD-AS model end labour market. The expectations and rational expectations. Solow's model.


O. Blanchard, Scoprire la Macroeconomia. Un passo in più, Bologna, Il Mulino, 2006; C. Imbriani, A. Lopes, Aggregati macroeconomici e struttura finanziaria, Torino, Utet, 2007; V. Siesto, La contabilità nazionale italiana. Il sistema dei conti del 2000. Bologna, Il Mulino.

Prerequisites basic knowledge of Macro- and Microeconomics

Teaching Methods

Lectures and laboratory, home works, group works.

Assessment Methods

Written and oral exam.

More Information

Lecturer’s webpage: http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/bruni/


9


Course Name Enterprise Information Systems

ISCED Code

CFU (ECTS) 10


Semester Spring

Lecturer Prof. GARRO Alfredo



60 / 6

Apprenticeship NO


Italian

Course Contents: Part One: • Introduction to Business Information Systems. • Decision Support Systems and data warehousing. • The life cycle of data warehouse systems. • The design of Data Warehouse (analysis and reconciliation of data sources, analysis of user requirements; conceptual modeling, logical modeling, ETL, notes on physical modeling). • Project Management of Data Warehousing project, project documentation. Part Two: • Business Intelligence and Business Performance Management; • Data Mining: • Fundamental Models and Techniques: Decision Trees (C4.5 alg.); Associative Rules (a priori alg.), Clustering (K-means alg.); genetic algorithms. • Knowledge Discovery in Data Base. • Advanced Techniques: Statistical techniques: Bayesian classifier; specialized techniques: Test and Web Mining. • Evaluation Techniques. The concepts listed above will be illustrated also by appropriate case studies and concretely experienced through the use of the most common environments for data warehousing (such as Spago BI, MS Analysis. Services, Oracle, DW, Business Object) and date mining (such as WEKA, MS Analysis Services, Oracle DM).


- Matteo Golfarelli, Stefano Rizzi, Data Warehouse - Teoria e pratica della progettazione, seconda edizione, ISBN: 8838662916, Gennaio 2006, McGraw-Hill. - Richard J. Roiger, Michael W. Geatz, Introduzione al data mining, ISBN: 88 386 6167-7, Ottobre 2003, McGrawHill. - Carlo Vercellis, Business Intelligence – modelli matematici e sistemi per le decisioni, ISBN 88-386-6346-7, 2006, McGraw-Hill. - Lecture Notes.

Prerequisites None

Teaching Methods

Autonomous study concerning learning of the concepts covered in the course, conducting of exercises, practical activities at the LDI.

Assessment Methods

Students have to pass an oral exam in which also to present and discuss a simple project.

More Information

Lecturer’s webpage: http://www.unical.it/portale/strutture/dipartimenti_240/disesf/esterni/garro/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/esterni/garro/

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INTEGRATED SUBJECT


Course Name Advanced Statistical Techniques

ISCED Code

CFU (ECTS) 10


Semester Spring

Prerequisites None

Assessment Methods

Written and oral exam

MODULE


Course Name Time Serie Analysis

ISCED Code

CFU (ECTS) 5


Semester Spring

Lecturer Prof. TARSITANO Agostino



30 / 6

Apprenticeship NO


Italian

Course Contents: preliminary analyses of time series, stochastic processes, BJ approach, Reg-Arima.


Handouts available from the webpage; Di Fonzo T., Lisi F. (2005), Serie storiche economiche: analisi statistiche e applicazioni, Carocci; Piccolo D. (1990), Introduzione all’analisi delle serie storiche, Carocci; Santamaria L. (2000), Analisi statistica delle serie storiche economiche, Carocci

Prerequisites R language, infererence

Teaching Methods

Lectures/laboratory Advances with the Arima, Srima and Reg-Arima techniques through intensive practical experiences.

Assessment Methods

Intermediate tests and final examination

More Information

Other optional Teaching Units: economic ad business statistics Lecturer’s Page: http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/tarsitano/

MODULE


Course Name Generalized Linear Models

ISCED Code

CFU (ECTS) 5


Semester Spring

Lecturer Prof. DOMMA Filippo



30 / 6

Apprenticeship NO


Italian

Course Contents: This course deals with statistical models for the analysis of quantitative and qualitative data usually encountered in economic and social science. The statistical methods studied are the general linear model for quantitative responses (multiple regression), regression models for

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/tarsitano/

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binary data (including logistic regression and probit models), models for count data (Poisson regression) and models for survival data. All of these techniques are covered as special cases of the Generalized Linear Statistical Model, which provides a central unifying statistical framework for the entire course. A set of lecture notes is distributed.


“Generalized Linear Models”, Chapman and Hall; J.K. Lindsey (1997): “Applying Generalized Linear Models”, Springer; Hosmer D. Lemeshow S. (2000) “Applied Logistic Regression”, Wiley. Hosmer, D., Lemeshow, S., and May, S. (2008), Applied Survival Analysis: Regression Modeling of Time-to-Event Data, Second Edition, John Wiley & Sons; Piet de Jong and Gillian Z. Heller (2008): “Generalized Linear Models for Insurance data”, Cambridge University Press; Lecturer's slides.

Prerequisites Probability theory, statistical estimation and testing theory, multiple regression analysis. Some familiarity with matrix algebra and calculus is necessary. Computer literacy is essential.

Teaching Methods

Theoretical lectures and analysis of a data set

Assessment Methods

Oral exam and preparation of a short dissertation

More Information

Lecturer’s webpage: http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/leccadito/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/leccadito/

12


Course Name Statistical Models

ISCED Code

CFU (ECTS) 5


Semester Spring

Lecturer Prof. COSSARI Anthony



30 / 6

Apprenticeship NO


Italian

Course Contents: the course aims to extend the methodology of regression models to particular situations, such as the presence of heteroskedasticity in the data, serial correlation or restrictions on the parameters, the use of explanatory variables and indicator of techniques for the selection of variables relevant.


Montgomery-Peck, Introduction to linear regression analysis, Wiley Cappuccio-Orsi, Econometria, il Mulino; Draper-Smith, Applied regression analysis, Wiley.

Prerequisites None

Teaching Methods

Lectures

Assessment Methods

Home work and oral examination

More Information

Lecturer’s webpage: http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/cossari/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/cossari/

13


Course Name Statistics for Financial Market

ISCED Code

CFU (ECTS) 10

Course Year First Year Master Degree in Statistics and Informatics for Business and Finance

Semester Spring

Lecturer Prof. PERRI Pier Francesco



60 / 6

Apprenticeship NO


Italian

Course Contents: The course aims to describe relevant statistical methodologies conceived for the analysis of financial returns financial in order to evaluate the market risk. The theoretical part of the course will be focused on a critical review of the so-called "stylized financial facts" and will be supported with empirical analysis of real financial series by using Excel and the R open source language. For this reason, part of the course will be held in the computer laboratory. Descriptive and inferential analysis of financial returns. Definition of financial return. Financial time series. Centrality, variability and shape of returns. The autocorrelation function of different forms of returns. Test and graphical methods for assessing autocorrelation. The empirical analysis of the returns and the stylized facts. Tests of normality. Distributional models for returns. Conditional heteroscedastic models. Definition and characteristics of volatility. Structure of a model. The GARH models and their use in finance: ARCH(p), GARCH(p,q), I-GARCH, GARCH-M, E-GARCH, T-GARCH. Multivariate extension High-frequency data analysis. The duration models. The auto regressive conditional duration model Parameter estimation The extreme values theory. The order statistics and the distribution of maxima. The extreme value distributions: Gumbel, Frèchet and Weibull types and related domain of attraction problem . The POT approach and the generalized Pareto distribution. The EVT for financial time series.


J. Franken, W. Hardle, C.M. Hafner 2008). Statistics of Financial Markets. Springer G. M. Gallo, B. Pacini (2002) Metodi quantitativi per i mercati finanziari. Carocci, Roma. D. Ruppert (2004) Statistics and Finance. An Introduction. Springer

Prerequisites Statistics, Probability Inference, Time series and use of R software

Teaching Methods

Theoretical lectures and analysis of study cases in laboratory

Assessment Methods

Oral exam and preparation of a short dissertation

More Information

Lecturer’s webpage: http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/perri/


14


Course Name Life insurance

ISCED Code 46 Mathematics and statistics

CFU (ECTS) 10


Semester Winter

Lecturer Prof. MENZIETTI Massimiliano



60 / 6

Apprenticeship NO


Italian

Course Contents: 1. Aggravated mortality and selected mortality 2. Mortality projection models: Lee-Carter family models and Cairns-Blake-Dowd family models, cohort effect. 3. Biometric risks: longevity and disability risks assessment, measurement and management 4. Life insurance with variable benefits: with profit, unit-linked, equity linked 5. Actuarial models for the evaluation of a portfolio of life insurance 6. Reinsurance and others risk transfer tools in life insurance 7. Risk models for life insurance and solvency capital requirements 8. Solvency II project for life insurance 9. Health insurance: traditional policies, permanent health insurance, dread disease, long term care.


Lecture notes Olivieri A., Pitacco E., La valutazione nelle assicurazioni vita, Egea, 2005. Pitacco E., Matematica e Tecnica Attuariale delle assicurazioni sulla durata di vita, Edizioni LINT, 2002. Pitacco E., Denuit M., Haberman S., Olivieri A., Modelling longevity dynamics for pensions and annuity business, Oxford University Press, 2009. Pitacco E., Modelli attuariali per le assicurazioni sulla salute, Egea, 1995.

Prerequisites None

Teaching Methods

Lectures and exercises

Assessment Methods

Oral exam

More Information

Lecturer’s webpage: http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/menzietti/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/menzietti/

15


Course Name Statistical Methods for Marketing Researches

ISCED Code

CFU (ECTS) 5


Semester Spring

Lecturer Prof. ROMANO Rosaria



30 / 6

Apprenticeship NO


Italian

Course Contents: - Basic concepts: sampling survey, the questionnaire, the sampling. - Internal and External Preference Mapping: - Linear Preference Mapping: principal component regression, partial least squares regression. - Introduction to non-linear Preference Mapping: ideal point models. - Case studies. - Statistical techniques for the analysis of causal relations among latent variables measured by observed indicators: - Exploratory Factor Analysis. - Confirmative Factor Analysis. - Structural Equation Modelling: PLS approach (PLS path modelling). - Case studies.


T. Naes, P.B Brockoff, O. Tomic (2010). Statistics for sensory and consumer science. Wiley. de Lillo, G. Argentin, M. Lucchini, S. Sarti, M. Terrano (2007). Analisi Multivariata per le scienze sociali. Pearson. J.O, Kim, C.W. Mueller (1978). Factor analysis: Statistical methods and practical issues. Sage. J.O, Kim, C.W. Mueller (1978): Introduction to factor analysis: What it is and how to do it. Sage. Materials prepared by the lecturer.

Prerequisites None

Teaching Methods

The course includes 24 hours of lectures and 6 hours of laboratory tutorials.

Assessment Methods

Written and oral

More Information

Lecturer’s webpage: http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/romano/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/romano/

16


Course Name Statistical Methods for Market Analysis

ISCED Code

CFU (ECTS) 10


Semester Spring

Lecturer Prof. CONDINO Francesca



60 / 6

Apprenticeship NO


Italian

Course Contents: - Introduction: marketing strategy, primary and secondary data sources (panel data and scan data), surveys and data collection (probability and non-probability sampling). - Positioning (product, brand, organization ..) and perceptual maps using Multidimensional Scaling. - Correspondence analysis and multidimensional mapping. - Market segmentation using classification and regression trees. - A posteriori market segmentation using cluster analysis. - Conjoint analysis of consumer preferences.


B. Bracalente, M. Cossignani, A. Mulas (2009). Statistica aziendale. McGraw-Hill S. Brasini, F. Tassinari, G. Tassinari (1999). Marketing e pubblicità. Il Mulino M. Mazzocchi (2008) - Statistics for marketing and consumer research. Sage Pub. S. Zani (2000). Analisi dei dati statistici. Giuffrè Ed. Materials prepared by the lecturer.

Prerequisites None

Teaching Methods

Lectures and laboratory tutorials.

Assessment Methods

Written and oral

More Information

Lecturer’s webpage: http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/condino/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/condino/

17


Course Name Survey Sampling Techniques

ISCED Code

CFU (ECTS) 10


Semester Spring

Lecturer Prof. PERRI Pier Francesco



60 / 6

Apprenticeship NO


Italian

Course Contents: The survey process and basic concept (the survey objectives, the target population, the sampling frame, sampling, estimation, inclusion probabilities). Sampling designs and estimation: simple random sampling, stratified, systematic, unequal probability sampling and cluster sampling. The use of auxiliary information at the estimation stage: the ratio estimator, the regression estimator. Measurement errors


Cicchitelli, G., Herzel, A., Montanari G.E. (1997). Il Campionamento Statistico. Il Mulino, Bologna. Conti P.L., Marella D. (2012). Campionamento da Popolazioni Finite. Il Disegno Campionario. Springer Cochran, W.G. (1977). Sampling Techniques. 3a Ed., John Wiley & Sons, New York. Frosini, B.V., Montanaro, M., Nicolini, G. (1999). Il Campionamento da Popolazioni Finite. Metodi e Applicazioni. UTET Università, Torino. Nicolini F., Marasini D., Montanari G.E., Pratesi M. Ranalli G. Rocco E. (2013). Metodi di Stima in Presenza di Errori Campionari. Springer

Prerequisites Statistics, Inference, Use of R software

Teaching Methods

Theoretical lectures and analysis of study cases in laboratory

Assessment Methods

Oral exam

More Information

Lecturer’s webpage: http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/perri/


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2nd year


Course Name Applied Demography

ISCED Code

CFU (ECTS) 5

Course Year 2nd Year Master Degree in Statistics and Informatics for Business and Finance

Semester Winter

Lecturer Prof. DE BARTOLO Giuseppe



30 / 6

Apprenticeship NO


Italian

Course Contents: Basic concepts of demographic analysis. Measures of demographic growth. Some population models. Small area estimates. Demographic projections. Demography and Marketing. Demography and human resources management. Case study of applied demography.


Giuseppe De Bartolo, Elementi di analisi demografica e demografia applicata, Centro Editoriale e Librario, Università della Calabria, 1997. Giuseppe De Bartolo, Invecchiamento Welfare Povertà Immigrazione, Edizioni Scientifiche Calabresi, 2013 Th. Merrick, S.J. Tordella, Demographics: People and Market, Population Bulletin, vol. 43, n.1, Population Reference Bureau, Washington, 1988 L. Pol, Business Demography, Greenwood Press Inc. Westport, 1987

Prerequisites none

Teaching Methods

Lessons and tutorials

Assessment Methods

written test+ oral test

More Information

Lecturer’s webpage: http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/debartolo/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/debartolo/

19


Course Name Continuous-Time Mathematical Finance

ISCED Code

CFU (ECTS) 10


Semester Spring

Lecturer Prof. STAINO Alessandro



60 / 6

Apprenticeship NO


Italian

Course Contents: The first part of the course focuses on stochastic calculus and partial differential equations of parabolic type. After a brief introduction of the Lebesgue integral and some concepts of the probability theory, such as random variables, expected values and conditional expected values, the course deals with topics of stochastic processes. Martingales, Brownian motion, Ito integral, Ito processes, Ito formula, and stochastic differential equations are some important topics studied in this first part. The second part of the course focuses on the option pricing theory. The Black-Scholes-Merton model and how to obtain the celebrated Black-Scholes formula are explained. Other topics are the fundamental theorems of the asset pricing, the Greek letters, the implied volatility and the pricing of options written on assets paying dividends. The course finishes by dealing with the theory of the term structure of interest rates. In particular, some short rate models are studied: Vasicek, Ho-Lee and Cox-Ingersoll-Ross.


S. E. Shreve. Stochastic calculus for finance 2: Continuous time models. Springer finance textbook (2004). P. Glasserman. Montecarlo Methods in Financial Engineering. Springer (2004)

Prerequisites Exam of Discrete-Time Mathematical Finance

Teaching Methods

60 hours of lectures and tutorials by use of PC

Assessment Methods

Project and oral final exam

More Information

Lecturer’s webpage: http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/staino/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/staino/

20


Course Name Pension Mathematics

ISCED Code 46 Mathematics and statistics

CFU (ECTS) 5


Semester Winter

Lecturer Prof. PIRRA Marco



30 / 6

Apprenticeship NO


Italian

Course Contents: 1 Social protection Social insurance and social security. The social security evolution in Italy, United Kingdom and Germany. The funding systems. Mutuality e solidarity. Insurance and assistance. The three pillars. Taxonomy of the pension plan. 2 Collectivity theory Population exposed to different elimination causes. Pure and relative probability. Karup theorem. The theory of population divided in groups. The IVS model. 3 Actuarial present value Capitalization Coefficient: I, II and III type. Actuarial present value for a cohort or for one year. 4 Premium Average premium. Average premium for a cohort, PAYG premium, General average premium (GAP). Convergence theorem. Comparison between premium systems under different demographics, economics and financial hypothesis. 5 Mathematical reserves Mathematical reserves for a population: insured and pensioners reserves. Individual reserves. Capitalization of a funding system. 6 Actuarial evaluation Release of contribution periods. Transition between pension plans. Closure of a pension plan. Change of funding system.


Tomassetti A. et alii, 1995, Tecnica attuariale per collettività, vol. 1 e 2, Ed. Kappa, Roma. Winklevoss H. E., 1993, Pension Mathematics, Ed. Pension Research Council of Warthon School of the University of Pennsylvania, Philadelphia. Lecture notes

Prerequisites None

Teaching Methods

Self-study, lectures and exercises

Assessment Methods

Written and oral examination

More Information

Lecturer’s webpage: http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/pirra/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/pirra/

21


Course Name Risk Theory

ISCED Code

CFU (ECTS) 5


Semester Winter

Lecturer Prof. CERCHIARA Rocco Roberto



30 / 6

Apprenticeship NO


Italian and/or English

Course Contents: 1. Introduction about Risk Theory 2. Claim Number Distribution 3. Severity Distribution 4. Aggregate Loss Distribution 5. Risk Reserve and solvency ratios 6. Solvency II directive


- Daykin C., Pentikainen T., Pesonen M. (1994): “Practical Risk Theory for Actuaries”, Ed. Chapman & Hall, Pagg. 1-154; 155-178; 357-363; 397-404 - Daboni L. (1993), Lezioni di tecnica attuariale delle assicurazioni contro i danni, LINT, Trieste, pagg. 189- 197 - Pitacco E., Matematica e Tecnica Attuariale delle assicurazioni sulla durata di vita (2000), Appendice B, LINT, Trieste, pagg. 753- 790 - Nuovo codice delle Assicurazioni (2005) - Klugman S. A. et al. (1998), “Loss Models: from data to decisions”, First Edition, John Wiley - Savelli N. (1993): “Un modello di Teoria del rischio per la valutazione della solvibilità di una compagnia di assicurazioni sulla vita”, Edizioni LINT - Useful websites: www.iasb.org; www.actuaries.org; www.eiopa.org

Prerequisites None

Teaching Methods

Lectures and laboratory

Assessment Methods

Oral

More Information

Lecturer’s webpage: http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/cerchiara/

http://www.iasb.org/

http://www.actuaries.org/

http://www.eiopa.org/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/cerchiara/

22


Course Name LOGISTICS SYSTEMS PLANNING

ISCED Code

CFU (ECTS) 10


Semester Winter

Lecturer Prof. PALETTA Giuseppe



60 / 6

Apprenticeship NO


Italian

Course Contents: • Introduction to Logistics. • Combinatorial optimization problems. Introduction to the theory of complexity: classes P and NP, NP-complete problems, exact and heuristic methods, approximation algorithms. Introduction to graph theory. • Transportation. Models and algorithms for shortest path, bin packing, the traveling salesman and vehicle routing problems. • Location. Models and algorithms for location problems based on the concept of p-median, p-center and covering. Models and algorithms for Simple and Capacitated plant location problems. • Inventory. Economic Order Quantity (EOQ) model. Models and algorithms for dynamic lot sizing problems. • Scheduling. Problems of scheduling on a single machine and parallel machines: Shortest Processing Time first (SPT), Smith, Earliest Due Date first (EDD), Extended Jackson's Rule (EJR), V-shaped, Critical Ratio rules. Moore algorithm. Lawler algorithm.


1. Supplementary notes of the lecturer. 2. G. Ghiani, R. Musmanno, Modelli e Metodi per l'Organizzazione dei Sistemi Logistici, Pitagora Editrice, Bologna, 2000 3. G. Bruno, Operations Management, Edizioni Scientifiche Italiane, Napoli, 2003 4. David Simchi-Levi, Julien Bramel, Xin Chen, The Logic of Logistics: Theory, Algorithms, and Applications for Logistics and Supply Chain Management, Springer, 2005 5. A. Agnetis, C. Arbib, M. Lucertini, S. Nicoloso, Il Processo Decisionale, La Nuova Italia Scientifica, 1992 6. A. Sassano, Modelli e algoritmi della ricerca operativa, Franco Angeli, 1999

Prerequisites None

Teaching Methods

Lectures, home works, group works.

Assessment Methods

Mid-term and final exams.

More Information

Lecturer’s webpage: http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/paletta/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/servizi/paletta/

23


Course Name Computing Systems

ISCED Code

CFU (ECTS) 10

Course Year 2nd

Year Master Degree in Statistics and Informatics for Business and Finance

Semester Winter

Lecturer Prof. MASTROIANNI Carlo



60 / 6

Apprenticeship NO


Italian

Course Contents: The objective of the course is to teach methodologies and languages for the definition and/or redefinition (process re-engineering) of business processes, and statistical techniques (based on Markov chains), mathematical tools (based on queue networks) and computer-aided methodologies (based on simulation) for the performance analysis of business processes (workflow analysis) and for the reconstruction of process schemas starting from the produced data (workflow mining). The acquired methodologies and techniques will be actualized in economic, statistical and business use cases through the use of advanced informatics tools, e.g., YAWL e ProM.


- Wil van der Aalst and Kees van Hee. Workflow Management: Models, Methods, and Systems. The MIT Press, 2004. - A.H.M. ter Hofstede, W.M.P. van der Aalst, M. Adams, N. Russell. Modern Business Process Automation: YAWL and its Support Environment. Springer 2010.

Prerequisites None

Teaching Methods

Front teaching and individual projects

Assessment Methods

Writing and oral examination

More Information

Lecturer’s webpage: http://www.unical.it/portale/strutture/dipartimenti_240/disesf/esterni/mastroianni/

http://www.unical.it/portale/strutture/dipartimenti_240/disesf/esterni/mastroianni/

master degree course in statistics and informatics for business … · · 2014-08-26statistics...

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