Parameter Estimation in Stochastic Volatility Models

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The book introduces innovative methods for estimating unknown parameters in stochastic volatility models, addressing limitations in traditional approaches that rely on Brownian motion. It explores weak convergence to normality for improved inference, including confidence intervals, and examines continuous-time models driven by fractional Levy processes. By integrating jumps and long memory into the volatility framework, these methods enhance predictions for option pricing and stock market crash risk. Additionally, it includes simulation algorithms for practical application.