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Trending time-varying coefficient market models

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  • Chongshan Zhang
  • Xiangrong Yin

Abstract

In this paper we study time-varying coefficient (beta coefficient) models with a time trend function to characterize the nonlinear, non-stationary and trending phenomenon in time series and to explain the behavior of asset returns. The general local polynomial method is developed to estimate the time trend and coefficient functions. More importantly, a graphical tool, the plot of the k th-order derivative of the parameter versus time, is proposed to select the proper order of the local polynomial so that the best estimate can be obtained. Finally, we conduct Monte Carlo experiments and a real data analysis to examine the finite sample performance of the proposed modeling procedure and compare it with the Nadaraya--Watson method as well as the local linear method.

Suggested Citation

  • Chongshan Zhang & Xiangrong Yin, 2012. "Trending time-varying coefficient market models," Quantitative Finance, Taylor & Francis Journals, vol. 12(10), pages 1533-1546, October.
    • Handle: RePEc:taf:quantf:v:12:y:2012:i:10:p:1533-1546
    DOI: 10.1080/14697688.2011.552918
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    References listed on IDEAS

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    1. Franses, Philip Hans, 1996. "Periodicity and Stochastic Trends in Economic Time Series," OUP Catalogue, Oxford University Press, number 9780198774549.
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    Cited by:

    1. Yue, Mu & Li, Jialiang & Cheng, Ming-Yen, 2019. "Two-step sparse boosting for high-dimensional longitudinal data with varying coefficients," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 222-234.

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