2014
DOI: 10.1002/jae.2389
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Realized Beta Garch: A Multivariate Garch Model With Realized Measures of Volatility

Abstract: We introduce a multivariate generalized autoregressive conditional heteroskedasticity (GARCH) model that incorporates realized measures of variances and covariances. Realized measures extract information about the current levels of volatilities and correlations from high-frequency data, which is particularly useful for modeling financial returns during periods of rapid changes in the underlying covariance structure. When applied to market returns in conjunction with returns on an individual asset, the model yi… Show more

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Cited by 134 publications
(97 citation statements)
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“…Using the sequence of simulated residuals, a path for the volatility for the next H steps can be constructed by using equation (8) i+k,t ). An equivalent procedure is applied to the Realized LGARCH following insights from Hansen et al (2014) and we use the usual recursions to produce forecasts for the GARCH(1,1). In our procedure, we will use N = 1000 simulations for each horizon.…”
Section: Conditional Volatility Forecastingmentioning
confidence: 99%
See 1 more Smart Citation
“…Using the sequence of simulated residuals, a path for the volatility for the next H steps can be constructed by using equation (8) i+k,t ). An equivalent procedure is applied to the Realized LGARCH following insights from Hansen et al (2014) and we use the usual recursions to produce forecasts for the GARCH(1,1). In our procedure, we will use N = 1000 simulations for each horizon.…”
Section: Conditional Volatility Forecastingmentioning
confidence: 99%
“…Later, Hansen et al (2012) proposed the class of Realized GARCH models that generalizes the GARCH-X by including a measurement equation for the realized measure of volatility. Hansen and Huang (2015), Hansen et al (2014), andVander Elst (2015) completed the class of Realized GARCH models with the Realized EGARCH, the Realized Beta GARCH, and the FloGARCH, respectively. Shephard and Sheppard (2010) proposed the HEAVY model that also focuses on modeling the conditional volatility of returns.…”
Section: Introductionmentioning
confidence: 99%
“…The formal definition of the realized measures is given in Appendix B. Despite the constantly growing research on incorporating the realized measures into multivariate Gaussian models, discussed in Chiriac and Voev (2011) and Bauer and Vorkink (2011), and into GARCH type models, for example, Hansen et al (2014) and Bollerslev et al (2016), there is still a gap in the literature on how the parameters of non-Gaussian copula can be estimated daily based on high-frequency observations. It is important to note here that such standard copula estimation techniques as the Maximum Likelihood (ML) method or the inversion of Kendall's τ can not be directly applied to tick-by-tick observations.…”
Section: The Concept Of the Realized Copulamentioning
confidence: 99%
“…Many researchers have implemented the obtained realized measures to model financial time series. Most of those studies, however, employ models where the realized correlation matrix directly characterizes the multivariate distribution, see, for example, Bauer and Vorkink (2011), Chiriac and Voev (2011), Jin and Maheu (2012), or address GARCH type models, for example, Hansen et al (2014), Bauwens et al (2012), Noureldin et al (2012), Bollerslev et al (2016). There are only a limited number of studies which discuss the implementation of high-frequency data in copula models.…”
Section: Introductionmentioning
confidence: 99%
“…We build on Patton (2006a), Jondeau and Rockinger (2006) and Creal, et al (2012), who consider models of time-varying copulas where a parametric functional form is assumed, and the parameter is allowed to vary through time as a function of lagged information, similar to the ARCH model for volatility, see Engle (1982). 3 We attempt to bridge the gap between the existing timevarying copula models, which have almost exclusively used lower frequency data, and models from the volatility and correlation forecasting literature, which have successfully used high frequency data, see Shephard and Sheppard (2010), Noureldin et al (2012), Hansen et al (2011Hansen et al ( , 2013 for example. In a recent related paper, Fengler and Okhrin (2012) use a method-of-moments approach to match the covariance structure implied by a copula-based multivariate model with that estimated using high frequency data.…”
Section: Introductionmentioning
confidence: 99%