Pragmatic Comparison Analysis of Alternative Option Pricing Models

Natasha Latif, Shafqat Ali Shad, Muhammad Usman, Chandan Kumar, Bahman B Motii, MD Mahfuzer Rahman, Khuram Shafi, Zahra Idrees
{"title":"Pragmatic Comparison Analysis of Alternative Option Pricing Models","authors":"Natasha Latif, Shafqat Ali Shad, Muhammad Usman, Chandan Kumar, Bahman B Motii, MD Mahfuzer Rahman, Khuram Shafi, Zahra Idrees","doi":"arxiv-2309.09890","DOIUrl":null,"url":null,"abstract":"In this paper, we price European Call three different option pricing models,\nwhere the volatility is dynamically changing i.e. non constant. In stochastic\nvolatility (SV) models for option pricing a closed form approximation technique\nis used, indicating that these models are computationally efficient and have\nthe same level of performance as existing ones. We show that the calibration of\nSV models, such as Heston model and the High Order Moment based Stochastic\nVolatility (MSV) is often faster and easier. On 15 different datasets of index\noptions, we show that models which incorporates stochastic volatility achieves\naccuracy comparable with the existing models. Further, we compare the In Sample\nand Out Sample pricing errors of each model on each date. Lastly, the pricing\nof models is compared among three different market to check model performance\nin different markets. Keywords: Option Pricing Model, Simulations, Index\nOptions, Stochastic Volatility Models, Loss Function\nhttp://www.sci-int.com/pdf/638279543859822650.pdf","PeriodicalId":501355,"journal":{"name":"arXiv - QuantFin - Pricing of Securities","volume":"21 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Pricing of Securities","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2309.09890","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 paper, we price European Call three different option pricing models, where the volatility is dynamically changing i.e. non constant. In stochastic volatility (SV) models for option pricing a closed form approximation technique is used, indicating that these models are computationally efficient and have the same level of performance as existing ones. We show that the calibration of SV models, such as Heston model and the High Order Moment based Stochastic Volatility (MSV) is often faster and easier. On 15 different datasets of index options, we show that models which incorporates stochastic volatility achieves accuracy comparable with the existing models. Further, we compare the In Sample and Out Sample pricing errors of each model on each date. Lastly, the pricing of models is compared among three different market to check model performance in different markets. Keywords: Option Pricing Model, Simulations, Index Options, Stochastic Volatility Models, Loss Function http://www.sci-int.com/pdf/638279543859822650.pdf
备选期权定价模型的实用比较分析
本文对欧洲看涨期权的三种不同的期权定价模型进行了定价,其中波动率是动态变化的,即是非恒定的。在期权定价的随机波动率(SV)模型中使用了封闭形式的近似技术,表明这些模型具有计算效率,并且与现有模型具有相同的性能水平。我们证明了sv模型(如Heston模型和基于高阶矩的随机波动率(MSV))的校准通常更快,更容易。在15个不同的指数期权数据集上,我们证明了纳入随机波动率的模型达到了与现有模型相当的准确性。此外,我们比较了每个模型在每个日期的In Sample和Out Sample定价误差。最后,对三种不同市场的模型定价进行比较,检验模型在不同市场的表现。关键词:期权定价模型,模拟,IndexOptions,随机波动率模型,损失函数http://:/www.sci-int.com/pdf/638279543859822650.pdf
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信