Estimating Long Memory in Volatility

By Clifford M. Hurvich, Eric Moulines, Philippe Soulier. 2005

132 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/3599a5fa1523fba342dbaab6cf06b3aecb6eed83

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Franccois Roueff
2. Jie Hou
3. Murad S. Taqqu
4. Gilles Faÿ
5. Eric Moulines
6. Pierre Perron
7. Liudas Giraitis
8. Violetta Dalla

In Finance

1. Franccois Roueff
2. Gilles Faÿ
3. Murad S. Taqqu
4. Eric Moulines
5. Jie Hou
6. Pierre Perron
7. P. Das
8. Liudas Giraitis

Citations in the corpus

• Consistent estimation of the memory parameter for nonlinear time series (2006, methodology): “In case of a linear process (nt), consistency and asymptotic distribution of the local Whittle estimator âlog were analysed in Hurvich et al. (2005) and Arteche (2004).”
• ESTIMATING THE PERSISTENCE AND THE AUTOCORRELATION FUNCTION OF A TIME SERIES THAT IS MEASURED WITH ERROR (2013, unknown): “A few papers, such as Chong and Lui (1999), Sun and Phillips (2003), Hurvich, Moulines, and Soulier (2005), and Haldrup and Nielsen (2007), consider estimation of the memory parameter in an ARFIMA setting, where the time series of interest is perturbed by an additive noise term.”
• Simple (but effective) tests of long memory versus structural breaks (2006, background): “(29) follows from (31), and we establish (25).”
• Rough Volatility: Fact or Artefact? (2022, background): “This long-range dependence in volatility is modelled by choosing 1 > H > 1/2 [7, 5, 6, 18, 20].”
• Estimators of Long-Memory: Fourier versus Wavelets (2008, methodology): “The stochastic volatility model (a special instance of the signal plus noise model) has been considered in Hurvich et al. (2005b); Deo et al. (2006b), which establish consistency, rate of convergence and asymptotic normality of an appropriately modified LWF estimator.”
• Long-Memory and Level Shifts in the Volatility of Stock Market Return Indices (2010, unknown): “…likely scenario given that the proxies used for volatility are noisy measures of the true volatility process, the bias is negative but it is negative for all values of m (see, Breidt, Crato, and de Lima 1998; Deo and Hurvich 2001; Sun and Phillips 2003, and Hurvich, Moulines, and Soulier 2005).”
• Local Polynomial Whittle Estimation of Perturbed Fractional Processes (2008, unknown): “Furthermore, Hurvich et al. (2005) show that the polynomial log-periodogram regression estimator of Andrews & Guggenberger (2003) suffers from severe bias in the case of perturbed fractional processes and the LPW estimator is expected to perform similarly.”
• Modified local Whittle estimator for long memory processes in the presence of low frequency (and other) contaminations (2014, background): “Hurvich and Ray (2003), Hurvich et al. (2005) and Arteche (2006), among others, have proposed estimators that can reduce the effect of noise by introducing an 310 J. Hou, P. Perron / Journal of Econometrics 182 (2014) 309–328 additive constant or a polynomial term in the spectral density function.”