{"title":"双因素Heston-Kou随机波动率模型下的近似期权定价","authors":"Youssef El-Khatib, Zororo S. Makumbe, Josep Vives","doi":"10.1007/s10287-023-00486-8","DOIUrl":null,"url":null,"abstract":"Abstract Under a two-factor stochastic volatility jump (2FSVJ) model we obtain an exact decomposition formula for a plain vanilla option price and a second-order approximation of this formula, using Itô calculus techniques. The 2FSVJ model is a generalization of several models described in the literature such as Heston (Rev Financ Stud 6(2):327–343, 1993); Bates (Rev Financ Stud 9(1):69–107, 1996); Kou (Manag Sci 48(8):1086–1101, 2002); Christoffersen et al. (Manag Sci 55(12):1914–1932, 2009) models. Thus, the aim of this study is to extend some approximate pricing formulas described in the literature, like formulas in Alòs (Finance Stoch 16(3):403–422, 2012); Merino et al. (Int J Theor Appl Finance 21(08):1850052, 2018); Gulisashvili et al. (J Comput Finance 24(1), 2020), to pricing under the more general 2FSVJ model. Moreover, we provide numerical illustrations of our pricing method and its accuracy and computational advantage under double exponential and log-normal jumps. Numerically, our pricing method performs very well compared to the Fourier integral method. The performance is ideal for out-of-the-money options as well as for short maturities.","PeriodicalId":46743,"journal":{"name":"Computational Management Science","volume":"44 4","pages":"0"},"PeriodicalIF":1.3000,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Approximate option pricing under a two-factor Heston–Kou stochastic volatility model\",\"authors\":\"Youssef El-Khatib, Zororo S. Makumbe, Josep Vives\",\"doi\":\"10.1007/s10287-023-00486-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Under a two-factor stochastic volatility jump (2FSVJ) model we obtain an exact decomposition formula for a plain vanilla option price and a second-order approximation of this formula, using Itô calculus techniques. The 2FSVJ model is a generalization of several models described in the literature such as Heston (Rev Financ Stud 6(2):327–343, 1993); Bates (Rev Financ Stud 9(1):69–107, 1996); Kou (Manag Sci 48(8):1086–1101, 2002); Christoffersen et al. (Manag Sci 55(12):1914–1932, 2009) models. Thus, the aim of this study is to extend some approximate pricing formulas described in the literature, like formulas in Alòs (Finance Stoch 16(3):403–422, 2012); Merino et al. (Int J Theor Appl Finance 21(08):1850052, 2018); Gulisashvili et al. (J Comput Finance 24(1), 2020), to pricing under the more general 2FSVJ model. Moreover, we provide numerical illustrations of our pricing method and its accuracy and computational advantage under double exponential and log-normal jumps. Numerically, our pricing method performs very well compared to the Fourier integral method. The performance is ideal for out-of-the-money options as well as for short maturities.\",\"PeriodicalId\":46743,\"journal\":{\"name\":\"Computational Management Science\",\"volume\":\"44 4\",\"pages\":\"0\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2023-11-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Management Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s10287-023-00486-8\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"SOCIAL SCIENCES, MATHEMATICAL METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Management Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s10287-023-00486-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"SOCIAL SCIENCES, MATHEMATICAL METHODS","Score":null,"Total":0}
引用次数: 0
摘要
摘要在双因素随机波动跳变(2FSVJ)模型下,利用Itô微积分技术,得到了普通期权价格的精确分解公式和该公式的二阶近似。2FSVJ模型是文献中描述的几个模型的概括,如Heston (Rev finance Stud 6(2): 327-343, 1993);贝茨(Rev finance Stud 9(1): 69-107, 1996);管理科学48(8):1086-1101,2002);Christoffersen et al.(管理科学55(12):1914-1932,2009)模型。因此,本研究的目的是扩展文献中描述的一些近似定价公式,如Alòs中的公式(Finance Stoch 16(3): 403-422, 2012);Merino et al.(国际理论与应用金融杂志21(08):1850052,2018);Gulisashvili et al. (J computer Finance 24(1), 2020),在更通用的2FSVJ模型下定价。此外,我们还提供了数值实例说明我们的定价方法及其在双指数和对数正态跳跃下的准确性和计算优势。在数值上,与傅里叶积分法相比,我们的定价方法表现得非常好。这种表现对于现金外期权和短期期权都是理想的。
Approximate option pricing under a two-factor Heston–Kou stochastic volatility model
Abstract Under a two-factor stochastic volatility jump (2FSVJ) model we obtain an exact decomposition formula for a plain vanilla option price and a second-order approximation of this formula, using Itô calculus techniques. The 2FSVJ model is a generalization of several models described in the literature such as Heston (Rev Financ Stud 6(2):327–343, 1993); Bates (Rev Financ Stud 9(1):69–107, 1996); Kou (Manag Sci 48(8):1086–1101, 2002); Christoffersen et al. (Manag Sci 55(12):1914–1932, 2009) models. Thus, the aim of this study is to extend some approximate pricing formulas described in the literature, like formulas in Alòs (Finance Stoch 16(3):403–422, 2012); Merino et al. (Int J Theor Appl Finance 21(08):1850052, 2018); Gulisashvili et al. (J Comput Finance 24(1), 2020), to pricing under the more general 2FSVJ model. Moreover, we provide numerical illustrations of our pricing method and its accuracy and computational advantage under double exponential and log-normal jumps. Numerically, our pricing method performs very well compared to the Fourier integral method. The performance is ideal for out-of-the-money options as well as for short maturities.
期刊介绍:
Computational Management Science (CMS) is an international journal focusing on all computational aspects of management science. These include theoretical and empirical analysis of computational models; computational statistics; analysis and applications of constrained, unconstrained, robust, stochastic and combinatorial optimisation algorithms; dynamic models, such as dynamic programming and decision trees; new search tools and algorithms for global optimisation, modelling, learning and forecasting; models and tools of knowledge acquisition.
The emphasis on computational paradigms is an intended feature of CMS, distinguishing it from more classical operations research journals.
Officially cited as: Comput Manag Sci