Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/76570
DC Field | Value | Language |
---|---|---|
dc.contributor | School of Accounting and Finance | en_US |
dc.creator | Zhong, X | en_US |
dc.creator | Cao, J | en_US |
dc.creator | Jin, Y | en_US |
dc.creator | Zheng, W | en_US |
dc.date.accessioned | 2018-05-10T02:56:13Z | - |
dc.date.available | 2018-05-10T02:56:13Z | - |
dc.identifier.issn | 1465-1211 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/76570 | - |
dc.language.iso | en | en_US |
dc.publisher | Incisive Media Ltd. | en_US |
dc.rights | Copyright © 2017 Incisive Risk Information (IP) Limited | en_US |
dc.rights | 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 | en_US |
dc.subject | Nonparametric | en_US |
dc.subject | Option pricing | en_US |
dc.subject | Empirical likelihood | en_US |
dc.subject | Robust | en_US |
dc.subject | Blocking time series | en_US |
dc.title | On empirical likelihood option pricing | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 41 | en_US |
dc.identifier.epage | 53 | en_US |
dc.identifier.volume | 19 | en_US |
dc.identifier.issue | 5 | en_US |
dc.identifier.doi | 10.21314/JOR.2017.357 | en_US |
dcterms.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. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | The journal of risk, June 2017, v. 19, no. 5, p. 41-53 | en_US |
dcterms.isPartOf | The journal of risk | en_US |
dcterms.issued | 2017-06 | - |
dc.identifier.isi | WOS:000402547200003 | - |
dc.identifier.eissn | 1755-2842 | en_US |
dc.description.validate | 201805 bcrc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | AF-0153 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | The work described in this paper was partially supported by a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project No. CUHK 458212), the PolyU AF Departmental Research Grant and NSF funding (Project No. DMS- 1612978). | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 6759863 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Jin_Empirical_Likelihood_Option.pdf | Pre-Published version | 305.04 kB | Adobe PDF | View/Open |
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