Gaussian semiparametric estimation of multivariate fractionally integrated processes

By Katsumi Shimotsu. 2007

86 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/11e1c363ae643a2a93ed22b7df18a7dbf6b61040

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Citations in the corpus

Mixed-correlated ARFIMA processes for power-law cross-correlations (2013, background): “The most frequently discussed and applied ones are the multivariate generalizations of the wellestablished fractionally integrated ARMA processes (usually labeled as FARIMA and ARFIMA) – VARFIMA or MVARFIMA processes [36, 38, 23] – and fractional Gaussian noise processes or fractional Brownian motio”
Wavelet estimation for operator fractional Brownian motion (2015, unknown): “rd et al. (2012), Coeurjolly et al. (2013)). Several estimators have been developed by building upon the univariate, entry-wise scaling laws, e.g., the Fourier-based multivariate local Whittle (e.g., Shimotsu (2007), Nielsen (2011)) and the multivariate wavelet regression (Wendt et al. (2009), Ambla”
Fully modified narrow‐band least squares estimation of weak fractional cointegration (2011, unknown): “The proof of Lemma 1(b) in Shimotsu (2007) applies also to our terms H1j and H2j which shows that (a) holds and that maxa,b | ∑r j=1H2j | = OP (r1/2(log r)).”
Local Whittle Analysis of Stationary Fractional Cointegration and the Implied–Realized Volatility Relation (2007, methodology): “Lobato (1997, 1999), Christensen and Nielsen (2004), and (for an alternative model) Shimotsu (2005) .”
Exact local Whittle estimation of fractionally cointegrated systems (2012, methodology): “(30) SinceDnj(ν0) = e iπν0/2+O(λj)+O(j −ν0−1) from (27) of SP06, we can write the first term in (30) as eiπν0/2m−1 ∑m j=1(j/m) −ν0Iu2j + op(m −1/2), by approximating Iuj by A(λj)IεjĀ(λj) first (see Lemma 1(b1) of Shimotsu (2007)) and then using the order of the covariance between Iεj and Iεk.”