Studying at the University of Verona

A.A. 2016/2017

Academic calendar

Il calendario accademico riporta le scadenze, gli adempimenti e i periodi rilevanti per la componente studentesca, personale docente e personale dell'Università. Sono inoltre indicate le festività e le chiusure ufficiali dell'Ateneo.
L’anno accademico inizia il 1° ottobre e termina il 30 settembre dell'anno successivo.

Academic calendar

Course calendar

The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..

Definition of lesson periods
Period From To
I sem. Oct 3, 2016 Jan 31, 2017
II sem. Mar 1, 2017 Jun 9, 2017
Exam sessions
Session From To
Sessione invernale Appelli d'esame Feb 1, 2017 Feb 28, 2017
Sessione estiva Appelli d'esame Jun 12, 2017 Jul 31, 2017
Sessione autunnale Appelli d'esame Sep 1, 2017 Sep 29, 2017
Degree sessions
Session From To
Sessione estiva Appelli di Laurea Jul 20, 2017 Jul 20, 2017
Sessione autunnale Appelli di laurea Oct 17, 2017 Oct 17, 2017
Sessione invernale Appelli di laurea Mar 22, 2018 Mar 22, 2018
Holidays
Period From To
Festa di Ognissanti Nov 1, 2016 Nov 1, 2016
Festa dell'Immacolata Concezione Dec 8, 2016 Dec 8, 2016
Vacanze di Natale Dec 23, 2016 Jan 8, 2017
Vacanze di Pasqua Apr 14, 2017 Apr 18, 2017
Anniversario della Liberazione Apr 25, 2017 Apr 25, 2017
Festa del Lavoro May 1, 2017 May 1, 2017
Festa della Repubblica Jun 2, 2017 Jun 2, 2017
Vacanze estive Aug 8, 2017 Aug 20, 2017

Exam calendar

The exam roll calls are centrally administered by the operational unit  Science and Engineering Teaching and Student Services Unit
Exam Session Calendar and Roll call enrolment sistema ESSE3. If you forget your password to the online services, please contact the technical office in your Faculty or to the service credential recovery.

Exam calendar

Per dubbi o domande Read the answers to the more serious and frequent questions - F.A.Q. Examination enrolment

Academic staff

A B C D G M O P R S

Angeleri Lidia

lidia.angeleri@univr.it 045 802 7911

Baldo Sisto

sisto.baldo@univr.it 045 802 7935

Barbu Viorel

Bos Leonard Peter

leonardpeter.bos@univr.it +39 045 802 7987

Caliari Marco

marco.caliari@univr.it +39 045 802 7904

Daldosso Nicola

nicola.daldosso@univr.it +39 045 8027076 - 7828 (laboratorio)

Di Persio Luca

luca.dipersio@univr.it +39 045 802 7968

Gregorio Enrico

Enrico.Gregorio@univr.it 045 802 7937

Marigonda Antonio

antonio.marigonda@univr.it +39 045 802 7809

Mazzuoccolo Giuseppe

giuseppe.mazzuoccolo@univr.it +39 0458027838

Monti Francesca

francesca.monti@univr.it 045 802 7910

Orlandi Giandomenico

giandomenico.orlandi at univr.it 045 802 7986
Foto,  October 21, 2016

Pauksztello David

foto,  March 6, 2017

Petrakis Iosif

Rizzi Romeo

romeo.rizzi@univr.it +39 045 8027088

Sansonetto Nicola

nicola.sansonetto@univr.it 049-8027932

Schuster Peter Michael

peter.schuster@univr.it +39 045 802 7029

Solitro Ugo

ugo.solitro@univr.it +39 045 802 7977

Study Plan

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University. Please select your Study Plan based on your enrolment year.

CURRICULUM TIPO:
TeachingsCreditsTAFSSD
6
B
(MAT/05)

1° Anno

TeachingsCreditsTAFSSD

2° Anno

TeachingsCreditsTAFSSD
6
B
(MAT/05)
Teachings Credits TAF SSD
Between the years: 1°- 2°One course to be chosen among the following
Between the years: 1°- 2°
Between the years: 1°- 2°
Other activitites
4
F
-

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.




SPlacements in companies, public or private institutions and professional associations

Teaching code

4S001109

Coordinatore

Luca Di Persio

Credits

6

Scientific Disciplinary Sector (SSD)

MAT/06 - PROBABILITY AND STATISTICS

Language of instruction

English en

Period

I sem. dal Oct 3, 2016 al Jan 31, 2017.

Learning outcomes

Mathematical Finance
Academic Year 2016/2017

The Mathematical Finance course for the internationalized Master's Degree ( completely taught in English) aims to introduce the main concepts of discrete as well as continuous time, stochastic approach to the theory of modern financial markets.

In particular, the fundamental purpose of the course is to provide the mathematical tools characterizing the setting of Itȏ stochastic calculus for the determination, the study and the analysis of models for options, interest rates models, financial derivatives, etc., determined by stochastic differential equations driven by Brownian motion and/or impulsive random noises.

Basic ingredients are the foundation of the theory of continuous-time martingale, Girsanov theorems and the Feynman–Kac theorem and their applications to the theory of option pricing with specific examples in equities, also considering path-dependent options, and within the framework of interest rates models.

Great attention will be also given to the practical study and realisation of concrete models characterising the modern approach to both the risk managment and option pricing frameworks, also by mean of numerical computations and computer oriented lessons.

It is important to emphasize how the Stochastic Systems course is organized in such a way that students can concretely complete and further develop their own:
°ability to establish profound connections with non-mathematical disciplines, both in terms of motivation of mathematical research and of the application of the results of such surveys;
° capacity of analysis, synthesis and abstraction;
° specific computational and computer skills;
° ability to understand texts, even advanced, of Mathematics in general and Applied Mathematics in particular;
• ability to develop mathematical models for physical and natural sciences, while being able to analyze its limits and actual applicability, even from a computational point of view;
° skills concerning how to develop mathematical and statistical models for the economy and financial markets;
° capacity to extract qualitative information from quantitative data;
° knowledge of programming languages or specific software.

Program

Mathematical Finance
2016/2017

The MathFin course will be enriched by the contributions of Michele Bonollo e Luca Spadafora, for the details of their respective parts, please see below.

[ Luca Di Persio ]

Discrete time models
• Contingent claims, value process, hedging strategies, completeness, arbitrage
• Fundamental theorems of Asset Pricing (in discrete time)

The Binomial model for Assset Pricing
• One period / multiperiod Binomial model
• A Random Walk (RW) interlude (scaled RW, symmetric RW, martingale property and quadratic variation of the symmetric RW, limiting distribution)
• Derivation of the Black-Scholes formula (continuous-time limit)

Brownian Motion (BM)
• review of the main properties of the BM: filtration generated by BM, martingale property, quadratic variation, volatility, reflection properties, etc.

Stochastic Calculus
• Itȏ integral
• Itȏ-Döblin formula
• Black-Scholes-Merton Equation
• Evolution of Portfolio/Option Values
• Solution to the Black-Scholes-Merton Equation
• Sensitivity analysis

Risk-Neutral Pricing
• Risk-Neutral Measure and Girsanov's Theorem
• Pricing under the Risk-Neutral Measure
• Fundamental Theorems of Asset Pricing
• Existence/uniqueness of the Risk-Neutral Measure
• Dividend/continuously-Paying
• Forwards and Futures

[ Luca Spadfora ]

***Statistics
*Theory Review: distributions, the moments of a distribution, statistical estimators, Central Limit Theorem (CLT), mean, variance and empirical distributions.
*Elements of Extreme Value Theory: what is the distribution of the maximum?
Numerical studies: statistical error of the sample mean, CLT at work, distributions of extreme values.

***Risk Modelling
*How can we measure risk? Main risk measures: VaR and Expected Shorfall
*How to model risk: historical, parametric and Montecarlo methods
*We have a risk model: does it works? The backtesting methodology
*Empirical studies a) empirical behavior and stylized facts of historical series
*Empirical studies b) Implementation of risk models
*Empirical studies c) Implementation of risk models backtesting

[ Miche Bonollo ]

*** Tools for derivatives pricing
* Functionals of brownian motions: fist hitting time, occupation time, local time, min-MAX distribution review
* Application 1: range accrual payoff
* Application 2: worst of and Rainbow payoff

*** Credit portfolio models
* The general framework. The credit portfolio data
* Gaussian Creidit Metrics - Merton model
* The quantile estimation problem with Montecarl approach. L-Estimators, Harrel-Davis

Bibliography:

A. F. McNeil, R. Frey, P. Embrechts, Quantitative Risk Management:Concepts, Techniques and Tools, Princeton University Press, 2015.
J. -P. Bouchaud, M. Potter, Theory of Financial Risk - From Statistical Physics to Risk Management, University Press, Cambridge, 2000.
R. Cont, P. Tankov, Financial Modelling With Jump Processes, Chapman and Hall, CRC Press, 2003.
E. J. Gumbel, Statistics of Extremes, Dover Publications, Mineola (NY), 2004.
M.Yor et al, "Exponential Functionals of Brownian Motion and related Processes", Springer.
Shreve, Steven , Stochastic Calculus for Finance II: Continuous-Time Models
Shreve, Steven , Stochastic Calculus for Finance I: The Binomial Asset Pricing Model

Bibliografia

Reference texts
Author Title Publishing house Year ISBN Notes
M.Yor et al Exponential Functionals of Brownian Motion and related Processes Springer 2010
R. Cont, P. Tankov Financial Modelling With Jump Processes Chapman and Hall, CRC Press 2003
A. F. McNeil, R. Frey, P. Embrechts Quantitative Risk Management:Concepts, Techniques and Tools Princeton University Press 2015
E. J. Gumbel Statistics of Extremes Dover Publications, Mineola (NY) 2004
S. E. Shreve Stochastic Calculus for Finance II: Continuous-Time Models Springer, New York 2004
S. E. Shreve Stochastic Calculus for Finance I: The Binomial Asset Pricing Model Springer, New York 2004
J. -P. Bouchaud, M. Potter Theory of Financial Risk - From Statistical Physics to Risk Management University Press, Cambridge 2000

Examination Methods

Mathematical Finance
Academic Year 2016/2017

Final Exam : the exam will consists in an oral session, to be given with prof. L. Di Persio, which will be targeted on the theory behind all the arguments treated in the whole course, hence including the parts developed by M. Bonollo and L. Spadafora.

Moreover each student will be called to develop a case study within a list of projects proposed by both M. Bonollo and L. Spadafora, according with the notions that will have been addressed during their respective parts [ see the Course Program section ].


The final vote is expressed out of 30: in particular:
° The doctors Bonollo and Spadafora will communicate to prof. Of Persio a report on the goodness of the project presented by the single student;
° professor. Di Persio will use the previous report, along with the outcome of the oral examination he conducted, to decide on a final grade expressed out of 30.

It is important to emphasize how the skills acquired by students at the end of the course will enable them to:
- carry out high-profile technical and / or professional tasks, both mathematically oriented and of
computational type, both in laboratories and / or research organizations, in the fields of finance, insurance, services, and public administration, both individually and in groups;
° read and understand advanced texts of math and applied sciences, even at the level of advanced research;
• to use high-tech computing and computing tools with the utmost ease of implementation algorithms and models illustrated in the course, as well as to acquire further information;
- to know in depth the demonstration techniques used during the course in order to be able to exploit them to solve problems in different mathematical fields, also by taking the necessary tools and methods, from seemingly distant contexts, thus mathematically formalizing problems expressed in languages ​​of other scientific disciplines as well as economical ones, using, adapting and developing advanced models.

Tipologia di Attività formativa D e F

Academic year

Course not yet included

Career prospects


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Graduation

List of theses and work experience proposals

theses proposals Research area
Controllo di sistemi multiagente Calculus of variations and optimal control; optimization - Hamilton-Jacobi theories, including dynamic programming
Controllo di sistemi multiagente Calculus of variations and optimal control; optimization - Manifolds
Controllo di sistemi multiagente Calculus of variations and optimal control; optimization - Optimality conditions
Formule di rappresentazione per gradienti generalizzati Mathematics - Analysis
Formule di rappresentazione per gradienti generalizzati Mathematics - Mathematics
Mathematics Bachelor and Master thesis titles Various topics
Stage Research area
Internship proposals for students in mathematics Various topics

Double degree

The University of Verona, through a network of agreements with foreign universities, offers international courses that enable students to gain a Double/Joint degree at the time of graduation. Indeed, students enrolled in a Double/Joint degree programme will be able to obtain both the degree of the University of Verona and the degree issued by the Partner University abroad - where they are expected to attend part of the programme -, in the time it normally takes to gain a common Master’s degree. The institutions concerned shall ensure that both degrees are recognised in the two countries.

Places on these programmes are limited, and admissions and any applicable grants are subject to applicants being selected in a specific Call for applications.

The latest Call for applications for Double/Joint Degrees at the University of Verona is available now!


University Language Centre - CLA


Further services

I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.