programma versione

DIPARTIMENTO di ECONOMIA - DIEC
Scuola di Scienze Sociali
SCUOLA DI
SCI ENZE SOCI ALI
Financial econometrics cod. 60528
Corso di studi: Economia degli Intermediari Finanziari
Docente
Malvina MARCHESE
A.A. 2014/15
e-mail: [email protected]
Anno di corso:II
Sem:II
Sede: Genova
SSD:SECS-P/05
cfu:9
Ore lezione:72
General objectives:
The course provides a survey of the theory and application of time series models in financial econometrics.
Students are introduced to time series analysis of linear univariate and multivariate covariance stationary
models with short and long memory parameterization. The course then employs linear time series knowledge
to introduce students to time series financial econometrics models, particularly discrete- time parametric
ARCH models. The main objective of this course is to develop the skills needed for modelling and
forecasting assets volatilities and their co-movements in financial markets. The course aims to provide
students with a strong theoretical understanding of volatility models and techniques for estimations,
assessment and forecasting in financial markets under a variety of degree of shock persistence. Computer
classes whose aim is to enable the students to develop computational skills in MATLAB for empirical
research complement theoretical lectures.
Syllabus:
Topic I: LINEAR TIME SERIES ANALYSIS .
*Stochastic processes, covariance stationarity, strict stationarity, unit root processes, fractionally integrated
processes, Wold decomposition theorem.
*Introduction to spectral analysis: Fourier transforms,Spectrum of a time series process, rate of decay of the
spectrum for short and long memory processes.
*ARMA,ARIMA,ARFIMA univariate models: estimation and principles of forecasting.
*Unit root tests,long memory tests, cointegration,model diagnostic.
TOPIC II: UNIVARIATE GARCH MODELS.
*Stylized facts of asset returns
*ARCH model: identification and covariance stationarity conditions ,order identification, estimation,
evaluation
*GARCH model: identification and covariance stationarity conditions ,order identification, estimation,
evaluation and forecasting.
*Asymmetric GARCH models and leverage effects:EGARCH,QGARCH,GJGARCH,TGARCH:
identification and covariance stationarity conditions ,order identification, estimation, evaluation and
forecasting.
*Long memory in univariate GARCH models: testing for long memory in the time series domain,
forecasting in presence of long memory.
TOPIC III: MULTIVARIATE GARCH MODELS.
*Co-movements of financial returns: empirical and theoretical examples. Introduction to MGARCH models
and specific issues.
*VEC and BEKK models: dimensionality issues, conditions for positive definiteness, iterative procedures for
DIPARTIMENTO di ECONOMIA - DIEC
Scuola di Scienze Sociali
SCUOLA DI
SCI ENZE SOCI ALI
estimation.
*FACTOR MODELS
*CCC models: dimensionality issues, conditions for positive definiteness, iterative procedures for estimation.
*NON PARAMETRIC models
*Testing in MGARCH models
TOPIC IV: APPLICATIONS
*Option pricing
*Asset allocation
*Value at risk
PREREQUISITES:
No basic knowledge of time series econometrics is assumed.
No basic knowledge of Matlab is required.
A good knowledge of introductory econometrics with matrix algebra is strictly required ( Econometria I
CLEC or equivalent ).
Good knowledge of probability theory and statistical inference is strictly required (Statistical models exam,
prof Lagazio)
Good background in calculus and basic linear algebra.
Specific Learning Objectives
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Theoretical skills: Students should develop a sounded theoretical understanding of time series
processes with different degrees of memory persistence both in the time and in the spectral domain.
They should develop awareness in modelling linear time series and then use this knowledge to
appreciate the different volatility models in the literature and their ability to reproduce stylized facts
of assets returns.
Applied skills: Students must learn to apply their theoretical knowledge of time series models to
actual time series data, implementing model detection and diagnostic on data sets. They should be
able to test for unit roots, cointegration, long and short memory. They should be able to write simple
Matlab codes for forecasting of volatilities.
Awareness: Students will develop awareness of financial econometrics models and by the end of the
course they should be able to employ such models also in the analysis
of commodity prices ,such as oil prices ,gas prices and energy commodity prices in general.
Communication skills:
Students will develop a good knowledge of econometric terminology
which will enable them to read most of the available literature on financial econometrics.
Learning skills: Students will develop econometrics and statistical skills that will enable them to
analyze financial markets data sets, and to understand autonomously most complex models available
in the literature.
Modalità didattiche, obblighi, testi e modalità di accertamento.
Modalità
didattiche
Presente
Aulaweb
Lezioni frontali, analisi di casi, testimonianze aziendali, lavori di gruppo….
su Si ☒ No ☐
Obblighi
Attendance is not compulsory but strongly recommended
Testi di studio
Hamilton, “Time Series Analysis”, Princeton University Press.
Francq, Zakoian “GARCH Models”, Wiley.
Modalità
accertamento
di Esame ☒ scritto ☐ orale ☐ altro: in the summer term only, 30% of the final mark
can be obtained with a group applied project.
DIPARTIMENTO di ECONOMIA - DIEC
Scuola di Scienze Sociali
SCUOLA DI
SCI ENZE SOCI ALI
Ripetizione
dell’esame
students can sit any exam during the exam
session, however sitting an exam implies loosing any previous mark grade
Informazioni aggiuntive per gli studenti non frequentanti
Modalità
didattiche
Students who cannot attend the course must contact Doctor Marchese at the beginning
of the term and set up an appointament.
Obblighi
Testi di studio
Modalità
accertamento
di Esame
☒ scritto ☐ orale ☐ altro:
Ripetizione
dell’esame
Italian Package students
How to do it with Gli studenti hanno diritto a fare l’esame in lingua italiana, previo accordo con il
docente.
no panic
Durante lo svolgimento delle lezioni gli studenti potranno beneficiare di orari di
ricevimento in lingua italiana da parte del docente e di tutoraggio in lingua italiana
fornito da un tutor didattico scelto dal docente.
Inoltre uno dei testi sopraindicati è disponibile anche nelle traduzioni italiane:
Hamilton : “Econometria delle serie storiche” Monduzzi editore
Si consiglia agli studenti che temano per il loro livello di inglese di seguire le lezioni,
di utilizzare il testo in italiano e confrontarlo con quello inglese e fare uso del servizio
di tutoraggio in italiano.
Gli studenti possono tentare tutti gli esami delle sessioni d'esame,
tuttavia se si ripresentano a un esame perdono automaticamente qualunque
voto ottenuto in precedenza
Note
Si invitano tutti gli studenti a consultare periodicamente la pagina di questo insegnamento sul portale dell’elearning AulaWeb (raggiungibile dal sito di Ateneo o all’indirizzo: economia.aulaweb.unige.it/). Tutte le
informazioni e i materiali relativi a questo insegnamento sono pubblicate esclusivamente in tale sito.