Semiparametric estimation of long-memory volatility dependencies: The role of high-frequency data

By Tim Bollerslev, Jonathan H. Wright. 2000

135 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/467b195317324f93a483f900449710a119765266

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Citations in the corpus

Modelling structural breaks, long memory and stock market volatility: an overview (2005, methodology): “…(1996), who propose an average periodogram estimation for long memory; Lobato (1999) analyses a two-step estimator of a long memory vector process; Bollerslev and Wright (2000) perform a semiparametric estimation of long memory volatility dependencies; Wright (2000) develops log-periodogram…”
Stochastic behavioral asset pricing models and the stylized facts (2008, background): “To see this, plug (20),(21) and (22) into (19):”
Intraday and interday volatility in the Japanese stock market (2000, background): “Nonetheless, as shown by Andersen and Boller- slev (1997a) and Bollerslev and Wright (2000), the degree of fractional integration should remain the same across the temporally aggregated absolute returns under quite general distributional assumptions.”
MODELING AND FORECASTING REALIZED VOLATILITY (2003, unknown): “…three estimates of d are all significantly greater than zero and less than one half when judged by the 21 Consistent with the simulation evidence in Bollerslev and Wright (2000), the corresponding estimates of d for vt, not reported here, are about 0.15 less than those for yt reported in the…”
ESTIMATING THE PERSISTENCE AND THE AUTOCORRELATION FUNCTION OF A TIME SERIES THAT IS MEASURED WITH ERROR (2013, unknown): “In the context of volatility measures, Bollerslev and Wright (2000) showed that high-frequency-based volatility measures lead to more accurate estimates of the long-memory parameter.”
Decoupling the Short- and Long-Term Behavior of Stochastic Volatility (2016, background): “icrostructure-based theory on the connection between trading and rough volatility). 2.4 Strong persistence of volatility That volatility is very persistent has long been a well-established fact (e.g.,Bollerslev and Wright, 2000;Andersen et al.,2003). A large body of literature has therefore focused ”
The memory of stochastic volatility models (2001, methodology): “The e!ect of temporal aggregation on model (1.3) in case of long memory has been considered by Andersen and Bollerslev (1997) and Bollerslev and Wright (2000) .”
Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market: Rise of the Machines (2009, unknown): “[12] Bollerslev T.”
Estimation methods for stochastic volatility models: a survey (2004, background): “Bollerslev and Wright (2000) show that the properties of the GPH estimator of d are closely related to the sampling frequency of the data and propose new estimators that aggregate the information in very high frequency returns.”
Simple (but effective) tests of long memory versus structural breaks (2006, background): “For part (b), it suffices to show that (11) holds when Xt is generated by (4) and d̂(a) is the FELW estimator from the ath subsample.”
Leverage and Volatility Feedback Effects in High-Frequency Data (2006, methodology): “4 This mirrors the use of high-frequency data in more effectively measuring volatility dependencies over longer-run interdaily horizons in Bollerslev and Wright (2000), and the aforementioned use of highfrequency-based realized volatility-type measures in Ghysels, Santa-Clara, and Valkanov (2005) in”
Multivariate Skewed Student's t Copula in the Analysis of Nonlinear and Asymmetric Dependence in the German Equity Market (2008, unknown): “( H + 12) Z 0 1 [(t s) H 1 2 ( s) H 1 2]dB(s) + Z t 0 (t s) H 1 2dB(s) , ( 6 )”
Gaussian semiparametric estimation of multivariate fractionally integrated processes (2007, background): “Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes.”