Rescaled variance and related tests for long memory in volatility and levels

By Liudas Giraitis, Piotr Kokoszka, Remigijus Leipus, Gilles Teyssière. 2003

332 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/2ebbb9fb5d9b380b4b2936572a08f722f4e4d7df

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Citations in the corpus

Nonparametric cointegration analysis of fractional systems with unknown integration orders (2010, unknown): “Indeed, the original formulation by Granger (1981) accommodated this more general class of processes.”
Structural breaks in time series (2013, methodology): “: This rescaled variance statistic was considered by (Giraitis et al., 2001, 2003).”
Recent Advances in ARCH Modelling (2007, background): “(2005) discussed regime switching SV model rt = σtεt, where volatility σt = ζj , Sj−1 < t ≤ Sj (48)”
Testing stationarity of functional time series (2014, methodology): “For example, Lee and Schmidt (1996) and Giraitis et al. (2003) used it to detect long memory, with short memory as the null hypothesis.”
Microeconomic Models for Long Memory in the Volatility of Financial Time Series (2001, background): “where the in nite sequence of coeÆcients f jg(1)j=1 has the rate of decay j Cj (1); 0 < d < 1=2 as j !1 (31)”
Generalizations of the KPSS‐test for stationarity (2004, result): “These conditions VVS, 2004 have recently been generalized by GIRAITIS et al. (2003) who derive similar asymptotic results to the ones we rely on in this paper, but show they hold under more general conditions.”
Strong approximation for the sums of squares of augmented GARCH sequences (2006, background): “For further results concerning the estimation of the variance of sums of dependent random variables we refer to Giraitis et al. (2003) and Berkes et al. (2005).”
Long memory and regime switching: A simulation study on the Markov Regime-Switching ARFIMA model (2015, unknown): “In addition, both the widely used R/S (Lo, 1991) and V/S (Giraitis et al., 2003) long memory tests cannot distinguish between them.”
Modeling and predicting the CBOE market volatility index (2014, background): “We nevertheless find that our semiparametric HAR model performs very well across all forecasting horizons, mainly because it automatically selects which macro-finance effect is influential at any given day.”
On discriminating between long-range dependence and changes in mean (2006, background): “1(i) in [18] implies that if the observations Xi satisfy Xi = μ+ Yi with the Yi satisfying Assumption 2.”