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Dynamic Functional Regression with Application to the Cross-section of Returns

Author

Listed:
  • Piotr Kokoszka
  • Hong Miao
  • Matthew Reimherr
  • Bahaeddine Taoufik

Abstract

Motivated by testing the significance of risk factors for a cross-section of returns, we develop an inferential framework which involves function-on-scalar regression. Asymptotic theory is developed assuming the factors form a weakly dependent vector-valued time series, and the regression errors are weakly dependent functions. To accommodate the empirical behavior of the cross-section of returns and of the factors, we allow both the factors and the error functions can exhibit mild departures from stationarity. This requires new asymptotic theory which leads to several tests for the significance of function-valued regression coefficients. The new approach to the study of the significance of risk factors for a cross-section of returns complements the established Fama–French approach based on portfolio construction. It is more suitable if the statistical significance of the risk factors is to be rigorously controlled.

Suggested Citation

  • Piotr Kokoszka & Hong Miao & Matthew Reimherr & Bahaeddine Taoufik, 2018. "Dynamic Functional Regression with Application to the Cross-section of Returns," Journal of Financial Econometrics, Oxford University Press, vol. 16(3), pages 461-485.
    • Handle: RePEc:oup:jfinec:v:16:y:2018:i:3:p:461-485.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbx027
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    Citations

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    Cited by:

    1. Philip Nadler & Alessio Sancetta, 2023. "Empirical Asset Pricing with Functional Factors," Journal of Financial Econometrics, Oxford University Press, vol. 21(4), pages 1258-1281.
    2. Chenlei Leng & Degui Li & Hanlin Shang & Yingcun Xia, 2024. "Covariance Function Estimation for High-Dimensional Functional Time Series with Dual Factor Structures," Papers 2401.05784, arXiv.org, revised Jan 2024.
    3. Ruanmin Cao & Lajos Horváth & Zhenya Liu & Yuqian Zhao, 2020. "A study of data-driven momentum and disposition effects in the Chinese stock market by functional data analysis," Review of Quantitative Finance and Accounting, Springer, vol. 54(1), pages 335-358, January.
    4. Bo Li & Sabri Boubaker & Zhenya Liu & Waël Louhichi & Yao Yao, 2023. "Exploring the Nonlinear Idiosyncratic Volatility Puzzle: Evidence from China," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 527-559, August.
    5. Wang, Deqing & Tian, Sihua & Fang, Lei & Xu, Yan, 2020. "A functional index model for dynamically evaluating China's energy security," Energy Policy, Elsevier, vol. 147(C).
    6. Zhao, Yuqian, 2021. "Validating intra-day risk premium in cross-sectional return curves," Finance Research Letters, Elsevier, vol. 43(C).
    7. Horváth, Lajos & Li, Bo & Li, Hemei & Liu, Zhenya, 2020. "Time-varying beta in functional factor models: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).

    More about this item

    Keywords

    Cross-section of returns; functional regression; Hilbert space; weak dependence; time series;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

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