Laboratory "numerical finance and option pricing"
A.A. 2018/2019
Obiettivi formativi
This course aims at giving the basic knowledge of computational finance and numerical option pricing.
Material span from basic R programming to financial advanced time series estimation.
Basic numerical differentiation and Monte Carlo analysis.
Basic stochastic models simulation and numerical option pricing.
Material span from basic R programming to financial advanced time series estimation.
Basic numerical differentiation and Monte Carlo analysis.
Basic stochastic models simulation and numerical option pricing.
Risultati apprendimento attesi
Non definiti
Periodo: Secondo trimestre
Modalità di valutazione: Giudizio di approvazione
Giudizio di valutazione: superato/non superato
Corso singolo
Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.
Programma e organizzazione didattica
Edizione unica
Periodo
Secondo trimestre
STUDENTI FREQUENTANTI
Programma
Syllabus
* Basic and advanced R programming
* Introduction to explorative data analysis for financial time series
* Basics of Monte Carlo simulation
* Basics of numerical differentiation
* European option pricing
* American option pricing
* Basic and advanced R programming
* Introduction to explorative data analysis for financial time series
* Basics of Monte Carlo simulation
* Basics of numerical differentiation
* European option pricing
* American option pricing
Informazioni sul programma
A preliminary knowledge of computer programming is welcome but not necessary.
Propedeuticità
Students have to attend the theoretical classes of Numerical Methods for Finance (MEF).
Students must have attended the courses of Mathematics and Time Series Analysis, MEF 1st year.
Students must have attended the courses of Mathematics and Time Series Analysis, MEF 1st year.
Prerequisiti
Mathematics, Time Series Analysis.
Final report Fail/pass.
Final report Fail/pass.
Metodi didattici
Computer Lab. Script and Slides.
Materiale di riferimento
STUDENTI NON FREQUENTANTI
Iacus and Yoshida (2018) Simulation and Inference for Stochastic Processes with YUIMA, Springer NY
Iacus (2011) Option Pricing and Estimation of Financial Models with R, Wiley UK
Iacus (2008) Simulation and Inference for Stochastic Differential Equations, Springer NY
Iacus (2011) Option Pricing and Estimation of Financial Models with R, Wiley UK
Iacus (2008) Simulation and Inference for Stochastic Differential Equations, Springer NY
Programma
attending the classes of Lab is mandatory
Prerequisiti
Mathematics, Time Series Analysis.
Final report Fail/pass.
Final report Fail/pass.
Materiale di riferimento
attending the classes of Lab is mandatory
SECS-S/01 - STATISTICA - CFU: 3
Attivita' di laboratorio: 20 ore
Docente:
Iacus Stefano Maria