Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76570
PIRA download icon_1.1View/Download Full Text
Title: On empirical likelihood option pricing
Authors: Zhong, X
Cao, J
Jin, Y 
Zheng, W
Issue Date: Jun-2017
Source: The journal of risk, June 2017, v. 19, no. 5, p. 41-53
Abstract: The Black-Scholes model is the golden standard for pricing derivatives and options in the modern financial industry. However, this method imposes some parametric assumptions on the stochastic process, and its performance becomes doubtful when these assumptions are violated. This paper investigates the application of a nonparametric method, namely the empirical likelihood (EL) method, in the study of option pricing. A blockwise EL procedure is proposed to deal with dependence in the data. Simulation and real data studies show that this new method performs reasonably well and, more importantly, outperforms classical models developed to account for jumps and stochastic volatility, thanks to the fact that nonparametric methods capture information about higher-order moments.
Keywords: Nonparametric
Option pricing
Empirical likelihood
Robust
Blocking time series
Publisher: Incisive Media Ltd.
Journal: The journal of risk 
ISSN: 1465-1211
EISSN: 1755-2842
DOI: 10.21314/JOR.2017.357
Rights: Copyright © 2017 Incisive Risk Information (IP) Limited
The following publication Zhong, X., Cao, J., Jin, Y., & Zheng, W. (2017). On empirical likelihood option pricing. Journal of Risk, 19(5), 41-53 is available at https://doi.org/10.21314/JOR.2017.357
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Jin_Empirical_Likelihood_Option.pdfPre-Published version305.04 kBAdobe PDFView/Open
Open Access Information
Status open access
File Version Final Accepted Manuscript
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

121
Last Week
0
Last month
Citations as of Mar 24, 2024

Downloads

27
Citations as of Mar 24, 2024

SCOPUSTM   
Citations

2
Citations as of Mar 29, 2024

WEB OF SCIENCETM
Citations

2
Last Week
0
Last month
Citations as of Mar 28, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.