Asymptotic normality of a covariance estimator for nonsynchronously observed diffusion processes

Hayashi, Teru; Yoshida, Nakahiro

doi:10.1007/s10463-007-0138-0

Cited by 79 publications

(95 citation statements)

References 5 publications

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“…Concerning the variance models, recent advances include the two-scales realized variance by Zhang et al (2005), the realized kernels of Barndorff-Nielsen et al (2006), and the realized range-based variance which has newly been revived by Christensen & Podolskij (2007). With respect to covariance estimation Hayashi & Yoshida (2005) and Corsi & Audrino (2007) propose an estimator which does not require synchronization of observations and thus accounts for the Epps effect. Griffin & Oomen (2006) study the properties of this estimator under i.i.d.…”

Section: Estimation Proceduresmentioning

confidence: 99%

“…This leads to a bias towards zero in the estimated realized covariance as the sampling frequency increases. A solution to this problem is proposed by Hayashi & Yoshida (2005). Considering two assets k and l, the Hayashi-Yoshida (HY) estimator based on all observations is defined as…”

Section: Covariance Estimationmentioning

confidence: 99%

“…The progress in estimating the covariance between two randomly observed diffusion processes has been somewhat more cumbersome, due to the additional complication of non-synchronicity. Nevertheless, recent contributions such as Hayashi & Yoshida (2005) and Corsi & Audrino (2007) have introduced consistent and bias-free estimators for non-synchronously observed processes, when there are no market frictions. Based on these approaches, Griffin & Oomen (2006), Voev & Lunde (2007) and Zhang (2006), among others, consider the properties of such estimators in the presence of measurement noise and propose certain extensions in order to correct for the impact of the noise.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimating High-Frequency Based (Co-) Variances: A Unified Approach

Nolte

Voev

2007

SSRN Journal

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We propose a unified framework for estimating integrated variances and covariances based on simple OLS regressions, allowing for a general market microstructure noise specification. We show that our estimators can outperform in terms of the root mean squared error criterion the most recent and commonly applied estimators, such as the realized kernels of Barndorff-Nielsen, Hansen, Lunde & Shephard (2006), the two-scales realized variance of Zhang, Mykland & Aït-Sahalia (2005), the Hayashi & Yoshida (2005) covariance estimator, and the realized variance and covariance with the optimal sampling frequency chosen after Bandi & Russell (2005a) and Bandi & Russell (2005b).The power of our methodology stems from the fact that instead of trying to correct the realized quantities for the noise, we identify both the true underlying integrated moments and the moments of the noise, which are also estimated within our framework. Apart from being simple to implement, an important property of our estimators is that they are quite robust to misspecifications of the noise process.

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Section: Estimation Proceduresmentioning

confidence: 99%

Section: Covariance Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimating High-Frequency Based (Co-) Variances: A Unified Approach

Nolte

Voev

2007

SSRN Journal

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“…With respect to the first point, several authors, including Hayashi and Yoshida (2005), Voev and Lunde (2007), Zhang (2008), Malliavin and Mancino (2009), Wang and Zhou (2010) and Barndorff-Nielsen et al (2011), have developed volatility matrix estimators in the context of realized volatility and co-volatility. Regarding diffusion limits in the univariate class, Nelson (1990) derived SV models as the diffusion limit of the GARCH and exponential GARCH (EGARCH) models.…”

Section: Introductionmentioning

confidence: 99%

Leverage and feedback effects on multifactor Wishart stochastic volatility for option pricing

McAleer

2015

Journal of Econometrics

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in 1 AbstractThe paper proposes a general asymmetric multifactor Wishart stochastic volatility (AMWSV) diffusion process which accommodates leverage, feedback effects and multifactor for the covariance process. The paper gives the closed-form solution for the conditional and unconditional Laplace transform of the AMWSV models. The paper also suggests estimating the AMWSV model by the generalized method of moments using information not only of stock prices but also of realized volatilities and covolatilities. The empirical results for the bivariate data of the NASDAQ 100 and S&P 500 indices show that the general AMWSV model is preferred among several nested models.

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“…Another approach to dealing with asynchronicity is the Hayashi and Yoshida estimator (Hayashi and Yoshida, 2005) which accumulates cross-products of all fully and partially overlapping event-time returns to obtain unbiased covariance estimators.…”

Section: Introductionmentioning

confidence: 99%

Obtaining and Predicting the Bounds of Realized Correlations

Grossmass

2014

Swiss J Economics Statistics

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Summary This paper argues that the inherent data problems make precise point identification of realized correlation difficult but identification bounds in the spirit of Manski (1995) can be derived. These identification bounds allow for a more robust approach to inference especially when the realized correlation is used for estimating other risk measures. We forecast the identification bounds using the HAR model of Corsi (2003) using data during the year of onset of the credit crisis and find that the bounds provide good predictive coverage of the realized correlation for both 1- and 10-step forecasts even in volatile periods.

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Asymptotic normality of a covariance estimator for nonsynchronously observed diffusion processes

Abstract: Diffusions, Discrete-time observations, High-frequency data, Nonsynchronicity, Quadratic variation, Realized volatility,

Cited by 79 publications

References 5 publications

Estimating High-Frequency Based (Co-) Variances: A Unified Approach

Estimating High-Frequency Based (Co-) Variances: A Unified Approach

Leverage and feedback effects on multifactor Wishart stochastic volatility for option pricing

Obtaining and Predicting the Bounds of Realized Correlations

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