Estimating stochastic volatility models using daily returns and realized volatility simultaneously

Takahashi, Makoto; Omori, Yasuhiro; Watanabe, Toshiaki

doi:10.1016/j.csda.2008.07.039

Cited by 118 publications

(118 citation statements)

References 35 publications

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“…However, even though we can obtain a consistent estimator of true volatility, there are non-negligible differences that are referred to as the 'realized volatility error' (see Barndorff-Nielsen and Shephard (2002)). For removing the estimation bias caused by the realized volatility error in estimating stochastic volatility (SV) models, BarndorffNielsen and Shephard (2002), Bollerslev and Zhou (2002), Takahashi, Omori andWatanabe (2009), andAsai, McAleer andMedeiros (2012a,b) showed that it is useful to use an ad hoc approach that accommodates an error term with constant variance. As in the realized GARCH model, Takahashi, Omori and Watanabe (2009) suggested a specification based on daily returns and a realized volatility measure simultaneously, which we will call the 'realized SV' (RSV) model.…”

Section: Introductionmentioning

confidence: 99%

Realized stochastic volatility with general asymmetry and long memory

Chang

McAleer

2017

Journal of Econometrics

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The paper develops a novel realized stochastic volatility model of asset returns and realized volatility that incorporates general asymmetry and long memory (hereafter the RSV-GALM model). The contribution of the paper ties in with Robert Basmann's seminal work in terms of the estimation of highly non-linear model specifications ("Causality tests and observationally equivalent representations of econometric models", Journal of Econometrics, 1988), especially for specifying causal effects from returns to future volatility. This paper discusses asymptotic results of a Whittle likelihood estimator for the RSV-GALM model and a test for general asymmetry, and analyses the finite sample properties. The paper also develops an approach to obtain volatility estimates and out-of-sample forecasts. Using high frequency data for three US financial assets, the new model is estimated and evaluated. The paper compares the forecasting performance of the new model with a realized conditional volatility model.

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Section: Introductionmentioning

confidence: 99%

Realized stochastic volatility with general asymmetry and long memory

Chang

McAleer

2017

Journal of Econometrics

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“…This has the advantage for the explicit modeling of leverage effect, and circumvents the need for additional latent volatility processes. The idea of using a measurement equation to tie the realized measure to the latent volatility goes back to Takahashi et al (2009), who used it in the context of stochastic volatility models. Additional MEM specifications have been explored and developed in Cipollini et al (2009) and Brownless and Gallo (2010).…”

Section: Introductionmentioning

confidence: 99%

Exponential GARCH Modeling with Realized Measures of Volatility

Hansen

Huang

2015

SSRN Journal

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We introduce the Realized Exponential GARCH model that can utilize multiple realized volatility measures for the modeling of a return series. The model specifies the dynamic properties of both returns and realized measures, and is characterized by a flexible modeling of the dependence between returns and volatility. We apply the model to DJIA stocks and an exchange traded fund that tracks the S&P 500 index and find that specifications with multiple realized measures dominate those that rely on a single realized measure. The empirical analysis suggests some convenient simplifications and highlights the advantages of the new specification.

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“…We separately estimate β and S where β is a conditional variance of Z t plus a bias correction term (e.g. Takahashi et al, 2009, for the univariate case).…”

Section: B Measurement Equation For Rv: Derivation and Approximationmentioning

confidence: 99%

“…First, our paper relates to work started by Barndorff-Nielsen and Shephard (2002) in incorporating realized volatility in models with time-varying volatility. Takahashi et al (2009) use daily stock return data in combination with high-frequency realized volatility to more accurately estimate the stochastic volatility. Maheu and McCurdy (2011) show that adding realized volatility directly into a model of stock returns can improve density forecasts over a model that only uses level data, such as the EGARCH.…”

Section: Introductionmentioning

confidence: 99%

Does Realized Volatility Help Bond Yield Density Prediction?

Shin

Zhong

2015

FEDS

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We suggest using "realized volatility" as a volatility proxy to aid in model-based multivariate bond yield density forecasting. To do so, we develop a general estimation approach to incorporate volatility proxy information into dynamic factor models with stochastic volatility. The resulting model parameter estimates are highly efficient, which one hopes would translate into superior predictive performance. We explore this conjecture in the context of density prediction of U.S. bond yields by incorporating realized volatility into a dynamic Nelson-Siegel (DNS) model with stochastic volatility. The results clearly indicate that using realized volatility improves density forecasts relative to popular specifications in the DNS literature that neglect realized volatility.

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Estimating stochastic volatility models using daily returns and realized volatility simultaneously

Cited by 118 publications

References 35 publications

Realized stochastic volatility with general asymmetry and long memory

Realized stochastic volatility with general asymmetry and long memory

Exponential GARCH Modeling with Realized Measures of Volatility

Does Realized Volatility Help Bond Yield Density Prediction?

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