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Parameter Estimation in Stochastic Volatility Models

Title
Parameter Estimation in Stochastic Volatility Models [electronic resource] / by Jaya P. N. Bishwal.
ISBN
9783031038617
Edition
1st ed. 2022.
Publication
Cham : Springer International Publishing : Imprint: Springer, 2022.
Physical Description
1 online resource (XXX, 613 p.)
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
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.
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
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Format
Books / Online
Language
English
Added to Catalog
August 09, 2022
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.
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