| 書目名稱 | Parameter Estimation in Stochastic Volatility Models |
| 編輯 | Jaya P. N. Bishwal |
| 視頻video | http://file.papertrans.cn/742/741137/741137.mp4 |
| 概述 | Presents step-by-step tutorials to help the reader to learn quickly.Prepares readers for future developments via a chapter on next generation Flash.Includes ten tips on how to protect flash sites from |
| 圖書封面 |  |
| 描述 | This book develops alternative methods to estimate the unknown parameters in stochastic volatility models, offering a new approach to test model accuracy. While there is ample research to document stochastic differential equation models driven by Brownian motion based on discrete observations of the underlying diffusion process, these traditional methods often fail to estimate the unknown parameters in the unobserved volatility processes. This text studies the second order rate of weak convergence to normality to obtain refined inference results like confidence interval, as well as nontraditional continuous time stochastic volatility models driven by fractional Levy processes. By incorporating jumps and long memory into the volatility process, these new methods will help better predict option pricing and stock market crash risk. Some simulation algorithms for numerical experiments are provided. |
| 出版日期 | Book 2022 |
| 關鍵詞 | Ito stochastic differential equation; stochastic volatility model; jumps; long memory; fractional Browni |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-03861-7 |
| isbn_softcover | 978-3-031-03863-1 |
| isbn_ebook | 978-3-031-03861-7 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
|Archiver|手機版|小黑屋|
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