Realized GARCH : a joint modeling framework of return and realized measures of volatility
Abstract/Contents
- Abstract
- GARCH models have been successful in modeling financial returns. Still, much is to be gained by incorporating a realized measure of volatility in these models. In this thesis, we introduce a new framework for the joint modeling of returns and realized measures of volatility. A key feature of the Realized GARCH framework is a measurement equation that relates the observed realized measure to latent volatility. The new framework fills the gap between two lines of research on volatility modeling: GARCH and high frequency data. There are three major advantages of the Realized GARCH framework. First, the new framework nests most GARCH models as special cases and is, in many ways, a natural extension of standard GARCH models. The models with linear and log-linear specifications retain the simplicity and tractability of the classical GARCH framework; they imply an ARMA structure for the conditional variance and for the realized measures of volatility; and models in this class are parsimonious and simple to estimate. The measurement equation facilitates a simple modeling of the dependence between returns and future volatility that is commonly referred to as the leverage effect. Second, by incorporating the realized measures into the model, which are based on high frequency data and are much more accurate measurements for integrated volatility than daily squared returns, the Realized GARCH models provide a better performance in modeling and forecasting volatility. An empirical application with DJIA stocks and an exchange traded index fund shows that a simple Realized GARCH structure leads to substantial improvements in the empirical fit over to the standard GARCH model. This is true in-sample as well as out-of-sample. Moreover, the point estimates are remarkably similar across the different time series. Third, the measurement equation enables us to obtain additional insights on the bias and variance of different realized measures. Realized EGARCH model further extends the Realized GARCH model by reparameterizing the model and allowing different impacts of the leverage effect and residual measurement error on the conditional variance. The empirical results are in general consistent with the theoretical results in high frequency data literature and give practical guidance about how to use realized measures directly. The Realized EGARCH model is also applied to the exchange traded fund S& P500 index to study the volatility shocks during the financial crisis. We use this model to zoom into the events during the 2007-2009 period, and the model produces a daily series of volatility shocks. We link the announcements of events to large positive and negative volatility shocks.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Copyright date | 2011 |
| Publication date | 2010, c2011; 2010 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Huang, Zhuo |
|---|---|
| Associated with | Stanford University, Department of Economics |
| Primary advisor | Hansen, Peter |
| Thesis advisor | Hansen, Peter |
| Thesis advisor | Hong, Han |
| Thesis advisor | Wolak, Frank A |
| Advisor | Hong, Han |
| Advisor | Wolak, Frank A |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Zhuo Huang. |
|---|---|
| Note | Submitted to the Department of Economics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2011. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2011 by Zhuo Huang
Also listed in
Loading usage metrics...