Parameter Estimation in Stochastic Volatility Models

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Alternative methods for estimating unknown parameters in stochastic volatility models are explored, focusing on improving model accuracy. Traditional approaches often struggle with unobserved volatility processes, prompting a study of weak convergence to normality for refined inference results. The book introduces nontraditional continuous-time models driven by fractional Levy processes, incorporating jumps and long memory to enhance predictions of option pricing and stock market crash risk. Additionally, simulation algorithms for numerical experiments are included.