Realized GARCH: a joint model for returns and realized measures of volatility

By P. R. Hansen, Zhuo Huang, Howard Shek. 2012

663 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/a5dd302e94b2180b91ed0f85bf5e6446a3d9c2f3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Finance

Citations in the corpus

Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models (2020, neutral): “We base our analysis on the MCS approach introduced by Hansen et al. (2011).”
Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly (2019, background): “The literature on Markowitz portfolio selection in large dimensions—that is, when the number of assets is of the same magnitude as the number of observations—has experienced a large expansion over the last decade or so and is still growing; to list only a few examples, see Ledoit and Wolf (2003); Ja”
The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures (2011, methodology): “Comparing these results with the coefficients we obtain for close-to-close returns, we find that the period outside the trading hours accounts for between 20% and 30% of the total daily variance of the stocks; the results in Hansen, Huang, and Shek (2011b) lead to similar conclusions.”
Asymptotic Theory for the QMLE in GARCH-X Models With Stationary and Nonstationary Covariates (2013, methodology): “…have been adopted covariates in the GARCH-type models with the rapid development seen in the field of realized volatility; see Barndorff-Nielsen and Shephard (2007), Engle (2002), Engle and Gallo (2006), Hansen, Huang, and Shek (2012), Hwang and Satchell (2005), and Shephard and Sheppard (2010).”
Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models (2016, neutral): “The model confidence sets are proposed by Hansen et al. (2011), and their construction in our study is detailed in the online appendix.”
Combining density forecasts using focused scoring rules (2017, neutral): “A second model that relates conditional volatility to realized measures is the realized GARCH model (RGARCH) of Hansen et al. (2012).”
Volatility and Quantile Forecasts by Realized Stochastic Volatility Models with Generalized Hyperbolic Distribution (2016, methodology): “Hansen and Lunde (2006) and Ubukata and Oya (2008) study the MMN effects on the RV estimator. In general, the MMN effect becomes larger at the higher sampling frequency while the information loss becomes larger at the lower frequency. There are several methods available for mitigating the MMN effect”
Multivariate High-Frequency-Based Volatility (HEAVY) Models (2012, result): “For the BEKK model, Comte and Lieberman (2003) show strong consistency of QMLE by verifying the conditions given in Jeantheau (1998). Hafner and Preminger (2009) show similar results for the VEC model which nests the BEKK model, and their results also apply to integrated processes.”
Option Valuation with Observable Volatility and Jump Dynamics (2015, background): “Section 4 estimates the physical process on returns, realized (2009), Shephard and Sheppard (2010), and Hansen, Huang, and Shek (2012).”
New HEAVY Models for Fat-Tailed Realized Covariances and Returns (2017, methodology): “For example, Creal, Koopman, and Lucas (2011) used the GAS framework to model volatilities and correlations in stock returns; Lucas, Schwaab, and Zhang (2014) developed new dynamic copula models under skewness and fat tails and applied this to systemic risk measurement; Harvey and Luati (2014) descr”