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Semiparametric dynamic portfolio choice with multiple conditioning variables

Author

Listed:
  • Jia Chen

    (Institute for Fiscal Studies)

  • Degui Li

    (Institute for Fiscal Studies)

  • Oliver Linton

    (Institute for Fiscal Studies and University of Cambridge)

  • Zudi Lu

    (Institute for Fiscal Studies)

Abstract

Dynamic portfolio choice has been a central and essential objective for institutional investors in active asset management. In this paper, we study the dynamic portfolio choice depending on multiple conditioning variables, where the number of the conditioning variables can be either fixed or diverging to infinity at certain polynomial rate in comparison with the sample size. We propose a novel data-driven method to estimate the nonparametric optimal portfolio choice, motivated by the model averaging marginal regression approach suggested by Li, Linton and Lu (2014). Specifically, in order to avoid curse of dimensionality associated with the problem and to make it practically implementable, we first estimate the optimal portfolio choice by maximising the conditional utility function for each individual conditioning variable, and then construct the dynamic optimal portfolio choice through the weighted average of the marginal optimal portfolio across all the conditioning variables. Under some mild regularity conditions, we have established the large sample properties for the developed portfolio choice procedure. Both simulation studies and empirical application well demonstrate the performance of the proposed methodology with finite sample and real data.

Suggested Citation

  • Jia Chen & Degui Li & Oliver Linton & Zudi Lu, 2015. "Semiparametric dynamic portfolio choice with multiple conditioning variables," CeMMAP working papers CWP07/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:07/15
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    References listed on IDEAS

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    Cited by:

    1. Jingwen Tu & Hu Yang & Chaohui Guo & Jing Lv, 2021. "Model averaging marginal regression for high dimensional conditional quantile prediction," Statistical Papers, Springer, vol. 62(6), pages 2661-2689, December.
    2. Chen, Jia & Li, Degui & Linton, Oliver, 2019. "A new semiparametric estimation approach for large dynamic covariance matrices with multiple conditioning variables," Journal of Econometrics, Elsevier, vol. 212(1), pages 155-176.
    3. Peng, Rong & Lu, Zudi, 2024. "Semiparametric Averaging of Nonlinear Marginal Logistic Regressions and Forecasting for Time Series Classification," Econometrics and Statistics, Elsevier, vol. 31(C), pages 19-37.
    4. Jia Chen & Degui Li & Oliver Linton & Zudi Lu, 2015. "Semiparametric model averaging of ultra-high dimensional time series," CeMMAP working papers CWP62/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. De Gooijer, Jan G. & Zerom, Dawit, 2019. "Semiparametric quantile averaging in the presence of high-dimensional predictors," International Journal of Forecasting, Elsevier, vol. 35(3), pages 891-909.
    6. Li, Degui, 2024. "Estimation of Large Dynamic Covariance Matrices: A Selective Review," Econometrics and Statistics, Elsevier, vol. 29(C), pages 16-30.

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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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

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