In this thesis, we propose two continuous time stochastic volatility models with long memory that generalize two existing models. More importantly, we provide a

It is now known that long memory stochastic volatility models can capture the well-documented evidence of volatility persistence. However, due to the complex st

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