Overview

This book develops alternative methods to estimate the unknown parameters in stochastic volatility models, offering a new approach to test model accuracy.

Traditional Methods and Challenges

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.

Advanced Inference Techniques

This text studies the second order rate of weak convergence to normality to obtain refined inference results like confidence intervals, as well as nontraditional continuous time stochastic volatility models driven by fractional Levy processes.

Incorporating Jumps and Memory

By incorporating jumps and long memory into the volatility process, these new methods will help better predict option pricing and stock market crash risk.

Additional Content

Some simulation algorithms for numerical experiments are provided.