Parameter estimation in stochastic volatility models

By:

Bishwal, Jaya P. N

Material type: Text

TextPublication details: Springer Switzerland 2022Description: xxx, 613 pISBN:

9783031038600

Subject(s):

DDC classification:

519.22 BIS

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. (https://link.springer.com/book/10.1007/978-3-031-03861-7)

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Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	519.22 BIS (Browse shelf(Opens below))	1	Available		007586

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Previous	519.2 ROS Stochastic processes	519.2 ROS A first course in probability	519.2 RUD Probability theory: a primer	519.22 BIS Parameter estimation in stochastic volatility models	519.23 ABD Optional processes: theory and applications	519.23 OLO Probability, statistics, and stochastic processes	519.23 SPE Stochastic processes, estimation, and control	Next

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.

(https://link.springer.com/book/10.1007/978-3-031-03861-7)

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