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Double-conditional smoothing of high-frequency volatility surface in a spatial multiplicative component GARCH with random effects

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  • Yuanhua Feng

    ()
    (University of Paderborn)

Abstract

This paper introduces a spatial framework for high-frequency returns and a faster double-conditional smoothing algorithm to carry out bivariate kernel estimation of the volatility surface. A spatial multiplicative component GARCH with random effects is proposed to deal with multiplicative random effects found from the data. It is shown that the probabilistic properties of the stochastic part and the asymptotic properties of the kernel volatility surface estimator are all strongly affected by the multiplicative random effects. Data example shows that the volatility surface before, during and after the 2008 financial crisis forms a volatility saddle.

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File URL: http://groups.uni-paderborn.de/fiwi/RePEc/pdf/wpaper/WP65.pdf
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Bibliographic Info

Paper provided by University of Paderborn, CIE Center for International Economics in its series Working Papers with number 65.

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Length: 28 pages
Date of creation: Aug 2013
Date of revision:
Handle: RePEc:pdn:wpaper:65

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Web page: http://www.uni-paderborn.de/fakultaeten/wiwi/department4/cie/
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Related research

Keywords: Spatial multiplicative component GARCH; high-frequency returns; double-conditional smoothing; multiplicative random effect; volatility arch; volatility saddle.;

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  1. Feng, Yuanhua & McNeil, Alexander J., 2008. "Modelling of scale change, periodicity and conditional heteroskedasticity in return volatility," Economic Modelling, Elsevier, vol. 25(5), pages 850-867, September.
  2. Feng, Yuanhua, 2004. "Simultaneously Modeling Conditional Heteroskedasticity And Scale Change," Econometric Theory, Cambridge University Press, vol. 20(03), pages 563-596, June.
  3. Torben G. Andersen & Tim Bollerslev, 1998. "Deutsche Mark-Dollar Volatility: Intraday Activity Patterns, Macroeconomic Announcements, and Longer Run Dependencies," Journal of Finance, American Finance Association, vol. 53(1), pages 219-265, 02.
  4. Wolfgang HÄRDLE & A. TSYBAKOV & L. YANG, 1996. "Nonparametric Vector Autoregression," SFB 373 Discussion Papers 1996,61, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  6. Andersen, Torben G. & Bollerslev, Tim & Cai, Jun, 2000. "Intraday and interday volatility in the Japanese stock market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 10(2), pages 107-130, June.
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