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International Journal of Innovation and Applied Studies
ISSN: 2028-9324     CODEN: IJIABO     OCLC Number: 828807274     ZDB-ID: 2703985-7
 
 
Friday 29 March 2024

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Evaluating the Predictive Accuracy of Heston Stochastic Volatility Model for Currency Options using Local and Global Calibration


Volume 34, Issue 1, October 2021, Pages 51–61

 Evaluating the Predictive Accuracy of Heston Stochastic Volatility Model for Currency Options using Local and Global Calibration

Mohammed Bouasabah1

1 National school of business and management, Ibn Tofail University, Kenitra, Morocco

Original language: English

Copyright © 2021 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


This paper analyses the implementation and calibration of the Heston Stochastic Volatility Model. We first introduce the model, provides theoretical motivation for its robustness and hence popularity and explain how characteristic functions can be used to estimate option prices. Then we consider the implementation of the Heston model, showing that relatively simple solutions can lead to fast and accurate currency option prices. In this work, we perform several tests, using both local and global calibration to evaluate the Predictive Accuracy of the Heston Stochastic Volatility Model for currency options.Our analyses show that straightforward setups deliver excellent calibration results. All calculations are carried out in MATLAB and included in the paper. All the MATLAB’s codes required to implement the model are provided in the appendix A.

Author Keywords: Heston Model, Currency option, MATLAB Calibration, Local calibration, Global calibration.


How to Cite this Article


Mohammed Bouasabah, “Evaluating the Predictive Accuracy of Heston Stochastic Volatility Model for Currency Options using Local and Global Calibration,” International Journal of Innovation and Applied Studies, vol. 34, no. 1, pp. 51–61, October 2021.