Di Somma, Vittorio (2019) Monte Carlo methods for barrier options. [Tesi di dottorato]

[thumbnail of di_somma_vittorio_32.pdf]
Preview
Text
di_somma_vittorio_32.pdf

Download (750kB) | Preview
Item Type: Tesi di dottorato
Resource language: English
Title: Monte Carlo methods for barrier options.
Creators:
Creators
Email
Di Somma, Vittorio
vittorio.disomma@unina.it
Date: 11 December 2019
Number of Pages: 72
Institution: Università degli Studi di Napoli Federico II
Department: Scienze Economiche e Statistiche
Dottorato: Economia
Ciclo di dottorato: 32
Coordinatore del Corso di dottorato:
nome
email
Pagano, Marco
marco.pagano@unina.it
Tutor:
nome
email
Di Lorenzo, Emilia
UNSPECIFIED
Toraldo, Gerardo
UNSPECIFIED
Cuomo, Salvatore
UNSPECIFIED
Date: 11 December 2019
Number of Pages: 72
Keywords: Barrier options; Monte Carlo methods; Bayesian statistics
Settori scientifico-disciplinari del MIUR: Area 13 - Scienze economiche e statistiche > SECS-S/01 - Statistica
Area 13 - Scienze economiche e statistiche > SECS-S/06 - Metodi matematici dell'economia e delle scienze attuariali e finanziarie
Date Deposited: 14 Jan 2020 16:40
Last Modified: 17 Nov 2021 12:04
URI: http://www.fedoa.unina.it/id/eprint/12982

Collection description

This thesis focuses the attention on a very common class of Monte Carlo methods to price a barrier option, named standard Monte Carlo methods, and on their issues: the bias and the high variance. In order to overcome these issues, in this thesis we describe a particular class of statistical procedures, named Bayesian Monte Carlo methods. The thesis is divided into two parts: in the first part we present the main Bayesian Monte Carlo methods under the Black-Scholes model, in the second part we generalize these schemes under the assumption of stochastic volatility. As supported by numerical experiments, a Bayesian Sequential Monte Carlo estimator is unbiased and has a lower variance than a standard Monte Carlo one.

Downloads

Downloads per month over past year

Actions (login required)

View Item View Item