Generalized spectral tests for the martingale difference hypothesis

By Juan-Carlos Escanciano, Carlos Velasco. 2006

201 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/878b7899b0aff7ab3ba84f7f985bf6a613070ac4

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Citations in the corpus

Specification Tests of Parametric Dynamic Conditional Quantiles (2010, unknown): “Following Escanciano and Velasco (2006) and references therein, we choose (cid:8)( ) equal to the d variate standard normal random vector 4 .”
Goodness-of-Fit Tests for Linear and Nonlinear Time Series Models (2006, background): “, Eubank and Hart 1992). To compute all of the test statistics we consider the usual least squares residuals from model 1, that is, êt(θn) = Yt − â − b̂Yt−1 for t = 1, . . . ,n. Table 1 reports the empirical rejection probabilities (RPs) associated with the models 1–4 to examine the empirical level ”
The Evolution of Stock Market Efficiency Over Time: A Survey of the Empirical Literature (2011, background): “…or equivalently, whether stock prices follow a martingale Journal of Economic Surveys (2011) Vol. 25, No. 1, pp. 69–108 C© 2010 Blackwell Publishing Ltd process (see Hinich and Patterson, 1992; Domı́nguez and Lobato, 2003; Kuan and Lee, 2004; Hong and Lee, 2005; Escanciano and Velasco, 2006a, b).”
Stock return predictability and the adaptive markets hypothesis: Evidence from century-long U.S. data (2011, unknown): “Based on this, Escanciano and Velasco (2006) propose a generalized spectral (GS) test, which can capture both linear and non-linear dependence.”
An updated review of Goodness-of-Fit tests for regression models (2013, methodology): “Weak convergence of the process α∗n is obtained by martingale difference theory, which is also useful for tests in the spectral domain (see Escanciano and Velasco 2006a), playing a key role for testing about the dependence structure.”
Small Sample Properties of Alternative Tests for Martingale Difference Hypothesis (2011, background): “For nonlinear models, we consider four models used by Escanciano and Velasco (2006), which include • Bilinear model: Yt = ²t + 0.25²t−1Yt−1 + 0.15²t−1Yt−2; • TAR(1) model: Yt = −0.5Yt−1 + ²t if Yt ≥ 1 and Yt = 0.4Yt−1 + ²t if Yt < 1; • Non-linear moving average model (NLMA): Yt = ²t−1²t−2(²t−2 + ²t…”
Testing for Granger-causality in quantiles (2018, methodology): “Following Escanciano and Velasco (2006), we take Fω(·) as the CDF of a d-variate standard normal random vector.”
Cryptocurrency trading: a comprehensive survey (2020, methodology): “The consistent test of Dominguez and Lobato [78], generalized spectral (GS) of Escanciano and Velasco [86] are applied in capturing time-varying linear and nonlinear dependence in bitcoin returns.”
White Noise Testing and Model Diagnostic Checking for Functional Time Series (2016, methodology): “As the functional objects and the associated operators both lie on some Hilbert spaces, it seems natural to adopt the Hilbert space approach (see e.g., Politis and Romano, 1994; Escanciano and Velasco, 2006; Shao, 2011a) for the asymptotic analysis.”
A WARP-SPEED METHOD FOR CONDUCTING MONTE CARLO EXPERIMENTS INVOLVING BOOTSTRAP ESTIMATORS (2013, unknown): “More recent studies with Monte Carlo simulations that feature small B and K are, e.g., Li and Tkacz (2006) (B = 100, K = 500), Escanciano and Velasco (2006) (B = 300, K = 1000), and Canepa (2006) (B = 400, K = 1000).”
ON THE LACK OF POWER OF OMNIBUS SPECIFICATION TESTS (2009, unknown): “In this section we examine the martingale properties of some exchange rates returns studied previously by Escanciano and Velasco (2006).”
Change Points and Temporal Dependence in Reconstructions of Annual Temperature: Did Europe Experience a Little Ice Age? (2014, background): “test of Escanciano and Velasco (2006), which corresponds to an exponential weighting function ω.”