4. Volatility Modeling Using Daily Data
This chapter develops models for forecasting daily volatility when only daily return data are available. We first briefly describe the simplest variance models available including moving averages and the RiskMetrics variance model. We then introduce the GARCH variance model and compare it with the RiskMetrics model. We also suggest extensions to the basic GARCH model, which improve the model's ability to capture variance persistence and leverage effects. The GARCH model parameters must be estimated using the quasi-maximum likelihood method which is described in detail. Finally, we discuss tools for evaluating the performance of volatility forecasting models.
Keywords: Volatility, moving average, RiskMetrics, ...

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