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

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Item Type:	Tesi di dottorato
Lingua:	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
Uncontrolled 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

Abstract

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.

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