ON THE LOG PERIODOGRAM REGRESSION ESTIMATOR OF THE MEMORY PARAMETER IN LONG MEMORY STOCHASTIC VOLATILITY MODELS

By Rohit S. Deo, Clifford M. Hurvich. Econometric Theory, 2001

155 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/adc91f8a48d6f0c4e0109b48f2bd7c6ebd416ff2

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Citations in the corpus

Long-range Dependence in Daily Stock Volatilities (2000, methodology): “A recent technical report by Deo and Hurvich (1998) shows thatˆd GP H for the LMSV model defined in (1)-(2) has finite sample bias that depends on the ratio of the variance of v t to that of ǫ t , i.e. the signal to noise ratio (SNR).”
Nonlinear Log-Periodogram Regression for Perturbed Fractional Processes (2003, methodology): “A spectral density satisfying Assumptions 1 and 2 also satisfies Assumptions 1 and 2 of Robinson (1995a). In consequence, the lemma follows from Theorem 2 of Robinson (1995a). Since we normalize the discrete Fourier transform by the spectral density f z (λ) instead of the power function C −1/2 g λ −”
Local Polynomial Whittle Estimation of Perturbed Fractional Processes (2008, methodology): “…of Künsch (1987) and Robinson (1995a) both preserve consistency and asymptotic normality when applied to perturbed fractional processes, as shown recently by Deo & Hurvich (2001) and Arteche (2004), these estimators can be severely biased since they do not take the perturbation into account.”
Level Shifts and the Illusion of Long Memory in Economic Time Series (2004, methodology): “Granger and Hyung (1999) and Diebold and Inoue (2001) studied the random level-shift process of Chen and Tiao (1990).”
True or Spurious Long Memory? A New Test (2008, methodology): “To facilitate the use of GPH estimators for volatility, Deo and Hurvich (2001) extended the distributional analysis for the GPH approach to a stochastic volatility model.”
Consistent estimation of the memory parameter for nonlinear time series (2006, methodology): “noise which corresponds to the model examined by Deo and Hurvich (2001). This rather simple example illustrates that adding a Gaussian i.”
Long-Memory and Level Shifts in the Volatility of Stock Market Return Indices (2010, unknown): “Under the null hypothesis of a stationary fractionally integrated process, we have given some conditions on d and m (Hurvich, Deo, and Brodsky 1998; Deo and Hurvich 2001),√ c[Ta](d̂a,c − d0) →d N(0,π2/24).”
Semiparametric Bayesian Inference of Long‐Memory Stochastic Volatility Models (2004, methodology): “However, Deo and Hurvich (2001) find the GPH estimator of the LMSV model to be downwardly biased with an empirical standard deviation closer to the standard errors associated with regression theory than to its theoretical asymptotic standard errors.”
The Distribution of Realized Exchange Rate Volatility (2001, unknown): “Although the earlier proofs for consistency and asymptotic normality of the log-periodogram regression estimator rely on normality, Deo and Hurvich (1998) and Robinson and Henry (1999) showed that these properties extend to non-Gaussian, possibly heteroscedastic, time series as well.”
Modified local Whittle estimator for long memory processes in the presence of low frequency (and other) contaminations (2014, methodology): “For the log-periodogram estimator, Geweke and Porter-Hudak (1983) suggested using the scaling factor S(l,m)1/2, where S(l,m) = m j=1(log j − (m − l + 1)−1 m τ=l log τ) 2 for some lower trimming l, and its use was also discussed by Deo and Hurvich (2001).”