{"title":"Arbitrage-Free Call Option Surface Construction Using Regression Splines (Preprint)","authors":"Greg Orosi","doi":"10.2139/ssrn.1956138","DOIUrl":null,"url":null,"abstract":"In this work, we suggest a novel quadratic programming-based algorithm to generate an arbitrage-free call option surface. Our approach relies on a regression spline-based implementation of the framework proposed by Orosi (2011) who presents a multi-parameter extension of the models of Figlewski (2002) and Henderson, Hobson, and Kluge (2007). Moreover, the empirical performance of the proposed method is evaluated using S&P 500 Index call options. Our results indicate that the proposed method provides a more precise fit to observed option prices than other alternative methodologies. NOTE: This is a preprint. The published version has been extensively revised.","PeriodicalId":431629,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics eJournal","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Econometrics: Applied Econometric Modeling in Financial Economics eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.1956138","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this work, we suggest a novel quadratic programming-based algorithm to generate an arbitrage-free call option surface. Our approach relies on a regression spline-based implementation of the framework proposed by Orosi (2011) who presents a multi-parameter extension of the models of Figlewski (2002) and Henderson, Hobson, and Kluge (2007). Moreover, the empirical performance of the proposed method is evaluated using S&P 500 Index call options. Our results indicate that the proposed method provides a more precise fit to observed option prices than other alternative methodologies. NOTE: This is a preprint. The published version has been extensively revised.
在这项工作中,我们提出了一种新的基于二次规划的算法来生成无套利看涨期权曲面。我们的方法依赖于Orosi(2011)提出的基于回归样条的框架实现,该框架提出了Figlewski(2002)和Henderson, Hobson, and Kluge(2007)模型的多参数扩展。此外,采用标准普尔500指数看涨期权对该方法的实证绩效进行了评估。我们的结果表明,所提出的方法提供了一个更精确的拟合观察期权价格比其他替代方法。注:这是预印本。已出版的版本作了大量修改。