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Gaussian Semiparametric Estimation in Long Memory in Stochastic Volatility and Signal Plus Noise Models

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Author Info
Josu Arteche () (Departamento de Economía Aplicada III, Universidad del Pais Vasco, Spain)

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Abstract

This paper considers the persistence found in the volatility of many financial time series by means of a local Long Memory in Stochastic Volatility model and analyzes the performance of the Gaussian semiparametric or local Whittle estimator of the memory parameter in a long memory signal plus noise model which includes the Long Memory in Stochastic Volatility as a particular case. It is proved that this estimate preserves the consistency and asymptotic normality encountered in observable long memory series and under milder conditions it is more efficient than the estimator based on a log-periodogram regression. Although the asymptotic properties do not depend on the signal-to-noise ratio the finite sample performance rely upon this magnitude and an appropriate choice of the bandwidth is important to minimize the influence of the added noise. I analyze the effect of the bandwidth via Monte Carlo. An application to a Spanish stock index is finally included.

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Paper provided by Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística) in its series BILTOKI with number 200202.

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Date of creation: 08 Apr 2002
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Handle: RePEc:ehu:biltok:200202

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Postal: Dpto. de Econometría y Estadística, Facultad de CC. Económicas y Empresariales, Universidad del País Vasco, Avda. Lehendakari Aguirre 83, 48015 Bilbao, Spain
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Related research
Keywords: Long memory; Stochastic Volatility; Semiparametric estimation; Frequency domain.;

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Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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    Other versions:
  2. Melino, Angelo & Turnbull, Stuart M., 1990. "Pricing foreign currency options with stochastic volatility," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 239-265. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Kenedy Alva & Juan Romo & Esther Ruiz, 2009. "Modelling intra-daily volatility by functional data analysis: an empirical application to the spanish stock market," Statistics and Econometrics Working Papers ws092809, Universidad Carlos III, Departamento de Estadística y Econometría. [Downloadable!]
  2. Violetta Dalla & Liudas Giraitis & Javier Hidalgo, 2006. "Consistent estimation of the memory parameterfor nonlinear time series," STICERD - Econometrics Paper Series /2006/497, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE. [Downloadable!]
    Other versions:
  3. Per Frederiksen & Frank S. Nielsen & Morten Ørregaard Nielsen, 2008. "Local polynomial Whittle estimation of perturbed fractional processes," CREATES Research Papers 2008-29, School of Economics and Management, University of Aarhus. [Downloadable!]
    Other versions:
  4. Josu Arteche, 2005. "Semiparametric estimation in perturbed long memory series," BILTOKI 200502, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística). [Downloadable!]
    Other versions:
  5. Per Frederiksen & Morten Ørregaard Nielsen, 2008. "Bias-reduced estimation of long memory stochastic volatility," CREATES Research Papers 2008-35, School of Economics and Management, University of Aarhus. [Downloadable!]
    Other versions:
  6. Zhongjun Qu & Pierre Perron, 2008. "A Stochastic Volatility Model with Random Level Shifts: Theory and Applications to S&P 500 and NASDAQ Return Indices," Boston University - Department of Economics - Working Papers Series wp2008-007, Boston University - Department of Economics. [Downloadable!]
  7. Nikolaus Hautsch & Yangguoyi Ou, 2008. "Discrete-Time Stochastic Volatility Models and MCMC-Based Statistical Inference," SFB 649 Discussion Papers SFB649DP2008-063, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany. [Downloadable!]
  8. Rohit Deo & Meng-Chen Hsieh & Clifford M. Hurvich & Philippe Soulier, 2007. "Long Memory in Nonlinear Processes," Quantitative Finance Papers 0706.1836, arXiv.org. [Downloadable!]
  9. Per Frederiksen & Frank S. Nielsen, 2008. "Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood," CREATES Research Papers 2008-59, School of Economics and Management, University of Aarhus. [Downloadable!]
  10. Frank S. Nielsen, 2008. "Local polynomial Whittle estimation covering non-stationary fractional processes," CREATES Research Papers 2008-28, School of Economics and Management, University of Aarhus. [Downloadable!]
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