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About this book
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.
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Table of contents (14 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
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Bibliographic Information
Book Title: Parameter Estimation in Stochastic Volatility Models
Authors: Jaya P. N. Bishwal
DOI: https://doi.org/10.1007/978-3-031-03861-7
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-031-03860-0Published: 07 August 2022
Softcover ISBN: 978-3-031-03863-1Published: 07 August 2023
eBook ISBN: 978-3-031-03861-7Published: 06 August 2022
Edition Number: 1
Number of Pages: XXX, 613
Topics: Probability Theory and Stochastic Processes, Applications of Mathematics, Statistical Theory and Methods
Keywords
- Ito stochastic differential equation
- stochastic volatility model
- jumps
- long memory
- fractional Brownian motion
- fractional Levy poses
- partially observed models
- parameter estimation
- discrete observations
- high frequency data
- asymptotic theory
- approximate maximum likelihood method
- minimum contrast method
- Berry-Esseen bounds