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Parameter Estimation in Stochastic Volatility Models / by Jaya P. N. Bishwal
版 | 1st ed. 2022. |
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出版者 | (Cham : Springer International Publishing : Imprint: Springer) |
出版年 | 2022 |
本文言語 | 英語 |
大きさ | XXX, 613 p : online resource |
著者標目 | *Bishwal, Jaya P. N author SpringerLink (Online service) |
件 名 | LCSH:Mathematical statistics LCSH:Stochastic models FREE:Mathematical Statistics FREE:Stochastic Modelling |
一般注記 | Stochastic Volatility Models: Methods of Pricing, Hedging and Estimation -- Sequential Monte Carlo Methods -- Parameter Estimation in the Heston Model -- Fractional Ornstein-Uhlenbeck Processes, Levy-Ornstein-Uhlenbeck Processes and Fractional Levy-Ornstein-Uhlenbeck Processes -- Inference for General Semimartingales and Selfsimilar Processes -- Estimation in Gamma-Ornstein-Uhlenbeck Stochastic Volatility Model -- Berry-Esseen Inequalities for the Functional Ornstein-Uhlenbeck-Inverse-Gaussian Process -- Maximum Quasi-likelihood Estimation in Fractional Levy Stochastic Volatility Model -- Estimation in Barndorff-Neilsen-Shephard Ornstein-Uhlenbeck Stochastic Volatility Model -- Parameter Estimation in Student Ornstein-Uhlenbeck Model -- Berry-Esseen Asymptotics for Pearson Diffusions -- Bayesian Maximum Likelihood Estimation in Fractional Stochastic Volatility Models -- Berry-Esseen-Stein-Malliavin Theory for Fractional Ornstein-Uhlenbeck Process -- Approximate Maximum Likelihood Estimation for Sub-fractional Hybrid Stochastic Volatility Model -- Appendix 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 HTTP:URL=https://doi.org/10.1007/978-3-031-03861-7 |
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電子ブック | 配架場所 | 資料種別 | 巻 次 | 請求記号 | 状 態 | 予約 | コメント | ISBN | 刷 年 | 利用注記 | 指定図書 | 登録番号 |
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電子ブック | オンライン | 電子ブック |
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Springer eBooks | 9783031038617 |
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電子リソース |
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EB00229145 |