In this paper we propose a numerical scheme to estimate the price of a barrier option in a general framework. More precisely, we extend a classical Sequential Monte Carlo approach, developed under the hypothesis of deterministic volatility, to Stochastic Volatility models, in order to improve the efficiency of Standard Monte Carlo techniques in the case of barrier options whose underlying approaches the barriers. The paper concludes with the application of our procedure to two case studies under a SABR model.

A Sequential Monte Carlo Approach for the pricing of barrier option in a Stochastic Volatility Model / Cuomo, Salvatore; DI SOMMA, Vittorio; DI LORENZO, Emilia; Toraldo, Gerardo. - In: ELECTRONIC JOURNAL OF APPLIED STATISTICAL ANALYSIS: DECISION SUPPORT SYSTEMS AND SERVICES EVALUATION. - ISSN 2037-3627. - 13:1(2020), pp. 128-145. [10.1285/i20705948v13n1p128]

A Sequential Monte Carlo Approach for the pricing of barrier option in a Stochastic Volatility Model

Salvatore Cuomo;Vittorio Di Somma;Emilia di Lorenzo;Gerardo Toraldo
2020

Abstract

In this paper we propose a numerical scheme to estimate the price of a barrier option in a general framework. More precisely, we extend a classical Sequential Monte Carlo approach, developed under the hypothesis of deterministic volatility, to Stochastic Volatility models, in order to improve the efficiency of Standard Monte Carlo techniques in the case of barrier options whose underlying approaches the barriers. The paper concludes with the application of our procedure to two case studies under a SABR model.
2020
A Sequential Monte Carlo Approach for the pricing of barrier option in a Stochastic Volatility Model / Cuomo, Salvatore; DI SOMMA, Vittorio; DI LORENZO, Emilia; Toraldo, Gerardo. - In: ELECTRONIC JOURNAL OF APPLIED STATISTICAL ANALYSIS: DECISION SUPPORT SYSTEMS AND SERVICES EVALUATION. - ISSN 2037-3627. - 13:1(2020), pp. 128-145. [10.1285/i20705948v13n1p128]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/828921
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