File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Pricing S&P 500 Index Options under Stochastic Volatility with the Indirect Inference Method

TitlePricing S&P 500 Index Options under Stochastic Volatility with the Indirect Inference Method
Authors
KeywordsOption pricing
Stochastic volatility
Indirect inference method
Issue Date2004
PublisherWorld Scientific Publishing Co Pte Ltd. The Journal's web site is located at http://www.worldscinet.com/jda/jda.shtml
Citation
Journal of Derivatives Accounting, 2004, v. 1 n. 2, p. 171-186 How to Cite?
AbstractThis paper studies the price of S&P 500 index options by using Heston's (1993) stochastic volatility option pricing model. The Heston model is calibrated by a two-step estimation procedure to incorporate both the information from time-series asset returns and the information from cross-sectional option data. In the first step, the recently developed simulation-based 'indirect inference method' is used to estimate the structural parameters that govern the asset return distribution; in the second step, the risk premium, λ, the spot variance, vt, and the correlation coefficient between the asset return and its volatility, ρ, are estimated by a nonlinear least-squares method that minimizes the sum of the squares of the error between the cross-sectional option price and the corresponding model price. The model performance is assessed by directly comparing the computed option model price with the market price. We find that both the Black–Scholes model and the Heston model overprice the out-of-the-money options and underprice the in-the-money options, but the degree of the bias is different. The Heston model significantly outperforms the Black–Scholes model in almost all moneyness-maturity groups. On average, the Heston model can reduce pricing errors by about 25%. However, pricing bias still exists in the Heston model. In particular, the Heston model always overprices short-term options, indicating that some other factors, such as the random jump, may also be needed to explain the option price.
Persistent Identifierhttp://hdl.handle.net/10722/85515
ISSN

 

DC FieldValueLanguage
dc.contributor.authorShu, Jen_HK
dc.contributor.authorZhang, JEen_HK
dc.date.accessioned2010-09-06T09:06:02Z-
dc.date.available2010-09-06T09:06:02Z-
dc.date.issued2004en_HK
dc.identifier.citationJournal of Derivatives Accounting, 2004, v. 1 n. 2, p. 171-186en_HK
dc.identifier.issn0219-8681en_HK
dc.identifier.urihttp://hdl.handle.net/10722/85515-
dc.description.abstractThis paper studies the price of S&P 500 index options by using Heston's (1993) stochastic volatility option pricing model. The Heston model is calibrated by a two-step estimation procedure to incorporate both the information from time-series asset returns and the information from cross-sectional option data. In the first step, the recently developed simulation-based 'indirect inference method' is used to estimate the structural parameters that govern the asset return distribution; in the second step, the risk premium, λ, the spot variance, vt, and the correlation coefficient between the asset return and its volatility, ρ, are estimated by a nonlinear least-squares method that minimizes the sum of the squares of the error between the cross-sectional option price and the corresponding model price. The model performance is assessed by directly comparing the computed option model price with the market price. We find that both the Black–Scholes model and the Heston model overprice the out-of-the-money options and underprice the in-the-money options, but the degree of the bias is different. The Heston model significantly outperforms the Black–Scholes model in almost all moneyness-maturity groups. On average, the Heston model can reduce pricing errors by about 25%. However, pricing bias still exists in the Heston model. In particular, the Heston model always overprices short-term options, indicating that some other factors, such as the random jump, may also be needed to explain the option price.-
dc.languageengen_HK
dc.publisherWorld Scientific Publishing Co Pte Ltd. The Journal's web site is located at http://www.worldscinet.com/jda/jda.shtmlen_HK
dc.relation.ispartofJournal of Derivatives Accountingen_HK
dc.rightsJournal of Derivatives Accounting. Copyright © World Scientific Publishing Co Pte Ltd.-
dc.rightsFor preprints : Preprint of an article submitted for consideration in [Journal] © [Year] [copyright World Scientific Publishing Company] [Journal URL] For postprints : Electronic version of an article published as [Journal, Volume, Issue, Year, Pages] [Article DOI] © [copyright World Scientific Publishing Company] [Journal URL]-
dc.subjectOption pricing-
dc.subjectStochastic volatility-
dc.subjectIndirect inference method-
dc.titlePricing S&P 500 Index Options under Stochastic Volatility with the Indirect Inference Methoden_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0219-8681&volume=1&issue=2&spage=171&epage=186&date=2004&atitle=Pricing+SandP+500+Index+Options+under+Stochastic+Volatility+with+the+Indirect+Inference+Methoden_HK
dc.identifier.emailZhang, JE: jinzhang@hku.hken_HK
dc.identifier.authorityZhang, J=rp01125en_HK
dc.identifier.doi10.1142/S021986810400021X-
dc.identifier.hkuros117276en_HK
dc.identifier.volume1-
dc.identifier.issue2-
dc.identifier.spage171-
dc.identifier.epage186-
dc.publisher.placeSingapore-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats