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Mean volatility regressions

Author

Listed:
  • Lin, Lu
  • Li, Feng
  • Zhu, Lixing
  • Härdle, Wolfgang Karl

Abstract

Motivated by increment process modeling for two correlated random and non-random systems from a discrete-time asset pricing with both risk free asset and risky security, we propose a class of semiparametric regressions for a combination of a non-random and a random system. Unlike classical regressions, mean regression functions in the new model contain variance components and the model variables are related to latent variables, for which certain economic interpretation can be made. The motivating example explains why the GARCH-M of which the mean function contains a variance component cannot cover the newly proposed models. Further, we show that statistical inference for the increment process cannot be simply dealt with by a two-step procedure working separately on the two involved systems although the increment process is a weighted sum of the two systems. We further investigate the asymptotic behaviors of estimation by using sophisticated nonparametric smoothing. Monte Carlo simulations are conducted to examine finite-sample performance, and a real dataset published in Almanac of China's Finance and Banking (2004 and 2005) is analyzed for illustration about the increment process of wealth in financial market of China from 2003 to 2004.

Suggested Citation

  • Lin, Lu & Li, Feng & Zhu, Lixing & Härdle, Wolfgang Karl, 2010. "Mean volatility regressions," SFB 649 Discussion Papers 2011-003, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    • Handle: RePEc:zbw:sfb649:sfb649dp2011-003
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    References listed on IDEAS

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    1. Ole E. Barndorff‐Nielsen & Neil Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280, May.
    2. Bradley, Richard C. & Bryc, Wlodzimierz, 1985. "Multilinear forms and measures of dependence between random variables," Journal of Multivariate Analysis, Elsevier, vol. 16(3), pages 335-367, June.
    3. Ait-Sahalia, Yacine, 1996. "Testing Continuous-Time Models of the Spot Interest Rate," The Review of Financial Studies, Society for Financial Studies, vol. 9(2), pages 385-426.
    4. Kim, Tae Yoon & Cox, Dennis D., 1996. "Uniform strong consistency of kernel density estimators under dependence," Statistics & Probability Letters, Elsevier, vol. 26(2), pages 179-185, February.
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    More about this item

    Keywords

    non-random systems; random systems; semiparametric regression; variance built-in mean;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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