A Coupled Component GARCH Model for Intraday and Overnight Volatility

A Coupled Component Garch Model for Intraday and Overnight Volatility

We propose a semi-parametric coupled component GARCH model for intraday and overnight volatility that allows the two periods to have different properties. To capture the very heavy tails of overnight returns, we adopt a dynamic conditional score model with t innovations. We propose a several step estimation procedure that captures the nonparametric slowly moving components by kernel estimation ...

A Semiparametric Intraday Garch Model

We propose a multiplicative component model for intraday volatility. The model consists of a seasonality factor, as well as a semiparametric and parametric component. The former captures the well-documented intraday seasonality of volatility, while the latter two account for the impact of the state of the limit order book, utilizing an additive structure, and fluctuations around this state by m...

Discipline of Finance Discussion Paper 2015 - 002 “ A Simple Overnight / Intraday Volatility Estimator ”

1 This paper provides a highly efficient overnight/intraday volatility estimator that is both numerically simple and relatively tractable to analyse. Because of these properties, it is envisaged that it will be very useful in providing volatility estimates in many contexts.

Business School Discipline of Finance Discussion Paper 2015 - 002 “ A Simple Overnight / Intraday Volatility Estimator ”

A GARCH(1,1) Model Approach for Control Limits on Volatility

The generalized autoregressive conditional heteroscedasticity (GARCH) approach is one of the common and simpler ways to use historical data to produce estimates of current and future levels of volatilities. This model recognizes that volatilities are not constant, for instance, a particular volatility may be high or low depending on the period of time. One of goals of a GARCH model is to track ...