.png){kind=icon}
返回
Warrant pricing under GARCH diffusion model
.png){kind=icon}
PRE
AI
DOI：10.1016/j.econmod.2012.06.020.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The GARCH diffusion model has attracted a great deal of attention in recent years, as it is able to describe financial time series better, when comparing to many other models. This paper considers the problem of warrant pricing when the underlying asset follows the GARCH diffusion model. An analytical approximate solution for European option prices is derived by means of Fourier transform. The approximate solution can be quickly computed by the fast Fourier transform (FFT) algorithm. Monte Carlo simulations show that this approximate solution is correct and the FFT is accurate and efficient, and hence it enables us to investigate the volatility smile implied by the GARCH diffusion model. Then a method is developed to provide the maximum likelihood (ML) estimation of the GARCH diffusion model based on the efficient importance sampling (EIS) procedure. Furthermore, the empirical performance of the GARCH diffusion model applied to the valuation of Hang Seng Index (HSI) warrants traded on the Hong Kong Stock Exchange (HKEx) is investigated. Empirical results show that the GARCH diffusion model outperforms the Black-Scholes (B-S) model in terms of the pricing accuracy, indicating that the pricing model incorporated with stochastic volatility can improve the pricing of warrants. (C) 2012 Elsevier B.V. All rights reserved.
Keyword:
Warrant pricing
GARCH diffusion model
Fast Fourier transform
Maximum likelihood
Efficient importance sampling
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.7
论文数:
6.6K
被引数:
1.6W
机构
引用论文
分享收藏Protection against pneumococcal infection elicited by immunization with glutamyl tRNA synthetase, polyamine transport protein D and sortase A.png)
Vaccine
VaccineIF0
PREAI
分享收藏Associations between Bladder Cancer Risk Factors and Tumor Stage and Grade at DiagnosisEpidemiology
OAAI
EpidemiologyIF0
OAAI分享收藏
查看更多内容