Nonstationarities in Financial Time Series, the Long-Range Dependence, and the IGARCH Effects

By Thomas Mikosch, Ctlin Stric. Review of Economics and Statistics, 2004

543 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/eab323f3318ad390cd6b8b553e228b9f9582de9c

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Citations in the corpus

Persistence of precious metal prices: A fractional integration approach with structural breaks (2014, background): “Ignoring structural breaks in economic time series can produce persistence or long memory effects and may have implications concerning the existence of higher order unconditional moments such as kurtosis, tail index, or forecasting (Mikosch and Stărică, 2004; Pesaran and Timmerman, 2004).”
Detecting gradual changes in locally stationary processes (2013, background): “Model (7.8) has been studied in a variety of papers [see Drees and Stărică (2003) and Fryzlewicz et al. (2006) among others].”
Long memory and structural breaks in modeling the return and volatility dynamics of precious metals (2012, methodology): “In this section, we briefly present the tests of long memory and structural changes as well as the GARCH-type specifications we use to account for these stylized facts in the conditional return and volatility of precious metals.”
Adaptive Estimation of Autoregressive Models with Time-Varying Variances (2008, background): “For instance, artificial IGARCH effects may be observed due to nonstationary changes in the unconditional volatility (Diebold, 1986; Mikosch and Stărică, 2004).”
Extreme Value Theory for GARCH Processes (2009, methodology): “An alternative explanation for this IGARCH effect are non-stationarities in the observed data as explained in Mikosch and Stărică [33].”
Modified local Whittle estimator for long memory processes in the presence of low frequency (and other) contaminations (2014, background): “Mikosch and Stărică (2004) considered the asymptotic properties of the periodogram for a deterministic level shift component when B = 2 (one level shift), with the addition of a short-memory component, and showed that E(Iu,k) = O(T/k2).”
A Component GARCH Model with Time Varying Weights (2009, background): “Furthermore, it has been documented how structural breaks in the volatility process can give rise to spurious volatility persistence if a GARCH model is fitted to the data without accounting for the breaks (Lamoureux and Lastrapes, 1990; Mikosch and Starica, 2004).”
Locally Stationary Processes (2011, background): “…researchers are convinced that the observed slow decay of the sample autocorrelation function of absolute stock returns is not a long memory effect but due to nonstationary changes in the unconditional variance (c.f. Mikosch and Stărică (2004), Stărică and Granger (2005), Fryzlewicz et. al.”
MEMORY PARAMETER ESTIMATION IN THE PRESENCE OF LEVEL SHIFTS AND DETERMINISTIC TRENDS (2013, unknown): “Econometric Theory, 29, 2013, 1196–1237. doi:10.1017/S0266466613000042”
GARCH Models (2010, unknown): “8 The slow decrease of the empirical autocorrelations can also be explained by nonstationarity phenomena, such as structural changes or breaks (see, for instance, Mikosch and Stărică, 2004).”
A simple test of changes in mean in the possible presence of long‐range dependence (2011, background): “Related references on the issue of spurious long memory include: Granger and Hyung (2004), Lobato and Savin (1998), Mikosch and Stărică (2004), Perron and Qu (2007) and Teverovsky and Taqqu (1997), among others.”
LOCALLY STATIONARY FACTOR MODELS: IDENTIFICATION AND NONPARAMETRIC ESTIMATION (2011, methodology): “As shown by Mikosch and Stărică (2004), the empirically often observed long-range dependence in volatility can be explained by a time-varying unconditional variance rather than autocorrelation.”
Practical Issues in the Analysis of Univariate GARCH Models (2009, background): “For example, a two components structure or ignored structural breaks in the unconditional variance ([58] and [70]) can result in IGARCH behavior.”