Exponential GARCH Modeling with Realized Measures of Volatility

Hansen, Peter Reinhard; Huang, Zhuo

doi:10.2139/ssrn.3178886

Cited by 37 publications

(64 citation statements)

References 40 publications

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“…The Realized EGARCH model of Hansen and Huang (2016) (with a single realized measure of volatility) is given by the following three equations:…”

Section: Realized Garch Frameworkmentioning

confidence: 99%

“…The MEM framework was subsequently refined and used by Shephard and Sheppard (2010), who refers to their model as the HEAVY model. More recently, Hansen et al (2012), see also Hansen and Huang (2016) and Hansen et al (2014), introduced the Realized GARCH model that takes a different approach to the joint modeling of returns and realized volatility measures. The key difference is the presence of a measurement equation that ties the realized measure to the underlying conditional variance.…”

Section: Introductionmentioning

confidence: 99%

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Volatility During the Financial Crisis Through the Lens of High Frequency Data: A Realized GARCH Approach

et al. 2018

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We study financial volatility during the global financial crisis and use the largest volatility shocks to identify major events during the crisis. Our analysis makes extensive use of high frequency (HF) financial data to model volatility and, importantly, to determine the timing within the day when the largest volatility shocks occurred. The latter helps us identify the events that may be associated with each of these shocks, and serves to illustrate the benefits of using high-frequency data. Some of the largest volatility shocks coincide, not surprisingly, with the bankruptcy of Lehman Brothers on September 15, 2008 and Congress's failure to pass the Emergency Economic Stabilization Act on September 29, 2008. The day with the largest volatility shock was February 27, 2007, the date when Freddie Mac announced a stricter policy for underwriting subprime loans and a date that was marked by a crash on the Chinese stock market. However, the intraday HF data shows that the main culprit was a computer glitch in the trading system. The days with the largest drops in volatility can in most cases be related to interventions by governments and central banks.

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“…The Realized EGARCH model of Hansen and Huang (2016) (with a single realized measure of volatility) is given by the following three equations:…”

Section: Realized Garch Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Volatility During the Financial Crisis Through the Lens of High Frequency Data: A Realized GARCH Approach

et al. 2018

Self Cite

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“…In contrast, the Realized GARCH (henceforth RGARCH) framework by Hansen et al (2012) relies on a so-called measurement equation to relate the realized measure to the underlying conditional variance. Hansen and Huang (2016) and Lunde and Olesen (2013) find that RGARCH models significantly improve volatility forecasts. Extensions to a multivariate setup including financial assets or exchange rates are also found to provide good empirical fit for the data under analysis (see Hansen et al, 2014;Dumitrescu and Hansen, 2018).…”

Section: Introductionmentioning

confidence: 97%

Do high-frequency-based measures improve conditional covariance forecasts?

Banulescu-Radu¹,

Dumitrescu²

2019

Financial Mathematics, Volatility and Covariance Modelling

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In this paper we investigate the possible benefits from using ex-post highfrequency based (realized) measures of volatility and correlation in conditional covariance forecasting. For this, we combine the (Robust) Realized GARCH framework with time varying conditional copulas and compare their forecasting abilities with those of multivariate Realized GARCH models and wellestablished competing models from the literature, i.e. the GJR-GARCH copula and the corrected DCC. The one-step-ahead forecasting abilities of the models are assessed in an empirical illustration on three pairs of financial assets by relying on the Model Confidence Set test. Our findings indicate that the proposed specifications relying on realized measures significantly improve the quality of covariance matrix forecasts.

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“…A more robust version of the Realized GARCH model was introduced by Banulescu-Radu et al (2019), suggesting a variant that is less sensitive to outliers and minimizes the impact on volatility of days with extreme negative volatility shocks. A realized exponential GARCH model that can use multiple realized volatility measures for the modeling of a return series, using a similar framework, has also been proposed (Hansen and Huang 2016). Finding that the Realized GARCH model was insufficient for capturing the long memory of underlying volatility, Huang et al (2016) developed a parsimonious variant of the Realized GARCH model by introducing Corsi's (2009) heterogeneous autoregressive (HAR) specification in the volatility dynamics.…”

Section: Introductionmentioning

confidence: 99%

Bivariate Volatility Modeling with High-Frequency Data

2019

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We propose a methodology to include night volatility estimates in the day volatility modeling problem with high-frequency data in a realized generalized autoregressive conditional heteroskedasticity (GARCH) framework, which takes advantage of the natural relationship between the realized measure and the conditional variance. This improves volatility modeling by adding, in a two-factor structure, information on latent processes that occur while markets are closed but captures the leverage effect and maintains a mathematical structure that facilitates volatility estimation. A class of bivariate models that includes intraday, day, and night volatility estimates is proposed and was empirically tested to confirm whether using night volatility information improves the day volatility estimation. The results indicate a forecasting improvement using bivariate models over those that do not include night volatility estimates.

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Exponential GARCH Modeling with Realized Measures of Volatility

Cited by 37 publications

References 40 publications

Volatility During the Financial Crisis Through the Lens of High Frequency Data: A Realized GARCH Approach

Volatility During the Financial Crisis Through the Lens of High Frequency Data: A Realized GARCH Approach

Do high-frequency-based measures improve conditional covariance forecasts?

Bivariate Volatility Modeling with High-Frequency Data

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