Parameter Estimation in Stochastic Volatility Models
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...
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| Format: | eBook |
| Language: | English |
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Cham
Springer International Publishing
2022, 2022
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| Edition: | 1st ed. 2022 |
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| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 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