First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
STATISTICAL SCIENCES
Course unit
COMPUTATIONAL FINANCE
SCP4063078, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
STATISTICAL SCIENCES
SS1736, Degree course structure A.Y. 2014/15, A.Y. 2018/19
N0
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Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination COMPUTATIONAL FINANCE
Website of the academic structure http://www.stat.unipd.it/studiare/ammissione-laurea-magistrale
Department of reference Department of Statistical Sciences
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge MASSIMILIANO CAPORIN SECS-S/03

Mutuated
Course unit code Course unit name Teacher in charge Degree course code
ECM0013159 COMPUTATIONAL FINANCE MASSIMILIANO CAPORIN EP2422

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines SECS-P/05 Econometrics 9.0

Course unit organization
Period First semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Laboratory 6.5 46 116.5 No turn
Lecture 2.5 18 44.5 No turn

Calendar
Start of activities 01/10/2018
End of activities 18/01/2019

Examination board
Board From To Members of the board
3 Commissione a.a.2018/19 01/10/2018 30/09/2019 CAPORIN MASSIMILIANO (Presidente)
BERNARDI MAURO (Membro Effettivo)
MORETTO MICHELE (Membro Effettivo)

Syllabus
Prerequisites: Not strinctly necessary but kindly suggested.
1) Basic elements of statistics for financial applications.
2) Basic elements of mathematical finance.
3) Basic knowledge of microeconomics and macroeconomics, knowledge of the Markowitz model, knowledge of the Capital Asset Pricing Model (CAPM).

The prerequisites at point 3) correspond to the content of the course of Economics of Financial Market taugth in the three-year degree in Statistica per l'Economia e l'Impresa.
Target skills and knowledge: The course aims at providing tools enabling the student to address computational problems and issues in the broad area of finance. Emphasis will be given to the asset allocation framework. A the end of the course students will become advanced users of a statistical software enabling them to formalize and solve the computational problem related to an empirical finance question.
Examination methods: The exam will be given in the form of a group homework. Each group (a team), will receive, at a beginning of the course (groups will be formed within the first two weeks of lectures), a list of tasks pointing at computational finance questions. Each team will have to coordinate activities, inducing team members to interact. During the exam session, each team will show results in the form of a presentation. Each team member must have full knowledge of the presentation and of the analyses performed by the team and of the main findings.
Assessment criteria: The evaluation of the group homework will be based on the following criterias:
- presence of appropriate answers to the various tasks assigned to the team;
- appropriateness of the quantitative tools adopted by the team;
- interpretation/economic intuition of the results obtained;
- interaction across team members.
Course unit contents: 1. The Matlab suite: introduction and coding.
2. Basic Asset Allocation: Markowitz with and without the risk free; Markowitz under standard constraints.
3. Advanced Asset Allocation: Risk Budgeting; non-linear and cardinality constraints; penalization methods in the asset allocation framework;the Michaud approach for resampling; Black-Litterman model; Chow-Kritzman model.
4. Backtesting and performance evaluation.
Planned learning activities and teaching methods: Lectures in computer laboratory with theory and practice even in groups.
Additional notes about suggested reading: Lecture notes will be distributed to students by moodle, including example codes and data.
Textbooks (and optional supplementary readings)
  • Hull, J.C., Options, Futures and other derivatives. --: Prentice Hall, --. E' disponibile anche una versione in Italiano Cerca nel catalogo
  • Roncalli, T., Introduction to risk parity and budgeting. --: Chapman & Hall, --. Cerca nel catalogo
  • Bodie, Z., Kane, A. and Marcus, A.J., Investments. --: McGraw Hill, --. Cerca nel catalogo
  • Hull, J.C., Risk management and financial institutions. --: Wiley Finance, --. E' disponibile anche una versione in Italiano Cerca nel catalogo
  • Barucci, E., Marsala, C., Nencini, M., and Sgarra, C., Ingegneria finanziaria. --: Egea, --. Cerca nel catalogo
  • Elton, E.J., Gruber, M.J., Brown, S.J., and Goetzmann, W.N., Modern Portfolio Theory and Investment Analysis. --: Wiley, --. E' disponibile anche una versione in Italiano Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Case study
  • Working in group
  • Problem solving
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Matlab