On covariance estimation of non-synchronously observed diffusion processes

Hayashi, Teru; Yoshida, Nakahiro

doi:10.3150/bj/1116340299

Cited by 372 publications

(323 citation statements)

References 15 publications

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“…In practice, trading is non-synchronous, delivering fresh prices at irregularly spaced times, which differ across stocks. Research focusing on non-synchronous trading has been an active field of financial econometrics in past years; see, for example, Hayashi and Yoshida (2005) and Voev and Lunde (2007). This practical issue induces bias in the estimators and may be partially responsible for the Epps effect (Epps, 1979), a phenomenon of decreasing empirical correlation between the returns of two different stocks with increasing data-sampling frequency.…”

Section: Data Synchronization: Refresh Timementioning

confidence: 99%

Do Co-Jumps Impact Correlations in Currency Markets?

Baruník

Vácha

2016

SSRN Journal

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We quantify how co-jumps impact correlations in currency markets. To disentangle the continuous part of quadratic covariation from co-jumps, and study the influence of co-jumps on correlations, we propose a new wavelet-based estimator. The proposed estimation framework is able to localize the co-jumps very precisely through wavelet coefficients and identify statistically significant co-jumps. Empirical findings reveal the different behaviors of co-jumps during Asian, European and U.S. trading sessions. Importantly, we document that co-jumps significantly influence correlation in currency markets.

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Section: Data Synchronization: Refresh Timementioning

confidence: 99%

Do Co-Jumps Impact Correlations in Currency Markets?

Baruník

Vácha

2016

SSRN Journal

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“…For further details on the standard estimation procedures, refer to Nelsen (2007), Trivedi and Zimmer (2007), Jaworski et al (2013), Cherubini et al (2011), Joe (2014 and Durante and Sempi (2015). In contrast to the direct application of the ML approach to tick-by-tick data or high-frequency estimator of Kendall's τ, there is a considerable literature discussing how to estimate the correlation matrix of daily log-returns via a realized correlation matrix or similar methods, see , , Zhang et al (2005), Hayashi and Yoshida (2005), De Pooter et al (2008). The idea of using the information concentrated in the realized covariance matrix to estimate the parameters of a copula daily has been employed by Fengler and Okhrin (2016), who used a combination of the results from a lemma of Hoeffding (1940) and Sklar's theorem (1) to express the covariance σ ij between two random variables X i and X j in terms of the marginal distributions F i (·) and F j (·) and the copula C 2 (·, ·; θ)…”

Section: The Concept Of the Realized Copulamentioning

confidence: 99%

The Realized Hierarchical Archimedean Copula in Risk Modelling

Okhrin¹,

Tetereva

2017

Econometrics

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This paper introduces the concept of the realized hierarchical Archimedean copula (rHAC). The proposed approach inherits the ability of the copula to capture the dependencies among financial time series, and combines it with additional information contained in high-frequency data. The considered model does not suffer from the curse of dimensionality, and is able to accurately predict high-dimensional distributions. This flexibility is obtained by using a hierarchical structure in the copula. The time variability of the model is provided by daily forecasts of the realized correlation matrix, which is used to estimate the structure and the parameters of the rHAC. Extensive simulation studies show the validity of the estimator based on this realized correlation matrix, and its performance, in comparison to the benchmark models. The application of the estimator to one-day-ahead Value at Risk (VaR) prediction using high-frequency data exhibits good forecasting properties for a multivariate portfolio.

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“…In particular, for the dependence structure of equity indices (measured via daily returns) it has been shown that considering liquid, internationally available exchange-traded funds (ETFs) can lead to different results as when analyzing the indices themselves (cf. [43][44][45] and therein). However, one does not have this option for volatility indices as there are no suitable ETFs tracking these.…”

Section: Data Description and Filteringmentioning

confidence: 99%

Regime Switching Vine Copula Models for Global Equity and Volatility Indices

et al. 2017

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For nearly every major stock market there exist equity and implied volatility indices. These play important roles within finance: be it as a benchmark, a measure of general uncertainty or a way of investing or hedging. It is well known in the academic literature that correlations and higher moments between different indices tend to vary in time. However, to the best of our knowledge, no one has yet considered a global setup including both equity and implied volatility indices of various continents, and allowing for a changing dependence structure. We aim to close this gap by applying Markov-switching R-vine models to investigate the existence of different, global dependence regimes. In particular, we identify times of "normal" and "abnormal" states within a data set consisting of North-American, European and Asian indices. Our results confirm the existence of joint points in a time at which global regime switching between two different R-vine structures takes place.

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On covariance estimation of non-synchronously observed diffusion processes

Cited by 372 publications

References 15 publications

Do Co-Jumps Impact Correlations in Currency Markets?

Do Co-Jumps Impact Correlations in Currency Markets?

The Realized Hierarchical Archimedean Copula in Risk Modelling

Regime Switching Vine Copula Models for Global Equity and Volatility Indices

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