On covariance estimation of non-synchronously observed diffusion processes

By Takaki Hayashi, Nakahiro Yoshida. 2005

449 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/395a9a420b8c579c6f44cdaad8c51ad638baa1e5

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Citations in the corpus

Designing Realised Kernels to Measure the Ex-Post Variation of Equity Prices in the Presence of Noise (2008, background): “multivariate versions of these results which deal with the scrambling effects discussed by, for example, Hayashi and Yoshida (2005), Bandi”
Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading (2011, methodology): “To provide useful benchmarks for these estimators we also compute: CovHYs , the Hayashi & Yoshida (2005) covariance estimator.”
Estimating Covariation: Epps Effect, Microstructure Noise (2011, background): “There are alternatives available, in particular the elegant estimator proposed in the breakthrough paper by Hayashi and Yoshida (2005, 2006) ).”
Integrated Covariance Estimation using High-frequency Data in the Presence of Noise (2006, unknown): “Hayashi and Yoshida (2005) propose a simple estimator that resolves the problem of nonsynchronicity and is unbiased and consistent for the integrated covariance in the absence of noise.”
VOLATILITY AND COVARIATION ESTIMATION WHEN MICROSTRUCTURE NOISE AND TRADING TIMES ARE ENDOGENOUS (2012, methodology): “Both lead to a highly biased behavior of na ve high frequency covariation measures, see [ 21 ], [33], [36].”
Pre-Averaging Estimators of the Ex-Post Covariance Matrix in Noisy Diffusion Models with Non-Synchronous Data (2010, unknown): “Hayashi and Yoshida (2005) introduced an estimator, which is capable of dealing with non-synchronous data, but not with market microstructure noise.”
Robust Markowitz mean‐variance portfolio selection under ambiguous covariance matrix (2016, unknown): “…inaccurate due to asynchronous data and lead-lag eﬀect, especially when the number of assets is large, and the correlation estimate converges to its true value less rapidly than the estimates of volatilities that are based on the full sets of marginal observations, see e.g. [18], [16] and [1].”
HAR Modeling for Realized Volatility Forecasting (2012, unknown): “…proposed in the literature: incorporate lead and lag cross returns in the estimator ([70], [32],[22], [9]), avoid any synchronization by directly using tick-by-tick data ([42],[54],[53],[67],[71],[72],[35]), multivariate realized kernel ([11]), and the mul-15 tivariate Fourier method ([68, 64]).”
Variation, jumps, market frictions and high frequency data in financial econometrics (2005, unknown): “We discussed the Fourier method in section 3.8.3, but there is also the work of Hayashi and Yoshida (2005) which characterises the bias caused by random sampling and suggests methods for trying to overcome it.”
Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use? (2008, methodology): “It would be interesting to see empirical work on the scale of this article with many stocks that considers transaction time sampling rather than calendar time sampling, for example using the covariance estimators of Harris et al. (1995), De Jong and Nijman (1997), and Hayashi and Yoshida (2005).”
Econometric Analysis of Vast Covariance Matrices Using Composite Realized Kernels and Their Application to Portfolio Choice (2016, background): “…recent studies, it has been confirmed that realized covariances computed over shorter fixed time periods underestimate the degree of dependence between assets (see, e.g., Renò 2003; Hayashi and Yoshida 2005; Voev and Lunde 2007; Griffin and Oomen 2011; Zhang 2011b; Barndorff-Nielsen et al. 2011).”
Estimation of Volatility Functionals in the Simultaneous Presence of Microstructure Noise and Jumps (2007, background): “Finally, an interesting and very important modification of this problem is the estimation of the quadratic covariation for non-synchronously observed data in the presence of noise (see Hayashi & Yoshida [16] for more details in a pure diffusion case).”
FAST CONVERGENCE RATES IN ESTIMATING LARGE VOLATILITY MATRICES USING HIGH-FREQUENCY FINANCIAL DATA (2013, background): “See, e.g., Ait-Sahalia et al. (2010), Barndorff-Nielsen, Hansen, Lunde, and Shephard (2011), Christensen et al. (2010), Griffin and Oomen (2011), Hayashi and Yoshida (2005), Zhang (2011), and the references therein.”
Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-Frequency Data (2017, methodology): “Various methods are available, including Hayashi and Yoshida (2005), Aı̈t-Sahalia et al. (2010), Christensen et al. (2010), Barndorff-Nielsen et al. (2011), Zhang (2011), Shephard and Xiu (2012) and Bibinger et al. (2014).”