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Liquidity and realized covariance forecasting: a hybrid method with model uncertainty

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Abstract

This paper investigates the realized covariance forecasting and liquidity effects on the covariance. The realized covariance is calculated based on the high frequency data of CSI 300 stock index and futures, and nonlinear support vector regression (SVR) approach is employed to evaluate the out-of-sample forecasting ability of HAR-type models. Then, we propose the hybrid method, named the weighted average windows (WAveW) method based on both OLS and SVR forecasts, to accommodate model uncertainty. The empirical results find that the performance of the WAveW method based on SVR forecasts obtains more accurate forecasting than the OLS and SVR methods, and the incorporation of liquidity helps to improve the forecasting ability. From the portfolio selection perspective, we show that our new method achieves higher economic value, which further confirms the effectiveness of our proposed hybrid method. The results are robust under alternative rolling windows, liquidity, covariance and cojumps.

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Data availability statement

The data that support this study are available from the corresponding author upon reasonable request.

Notes

  1. Zhang et al. (2020) claim that the out-of-sample performance of models estimated using AveW is robust to the choice of estimation windows. Here different values of \(w_{\min }\) and n are tested for each prediction, it is found that the prediction effect is optimal when \(w_{\min } = 800\) and n = 11.

  2. The detailed jump testing procedure can be found in Lee and Mykland (2008).

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Acknowledgements

The work is supported by the National Natural Science Foundation of China grant (72001180), Humanities and Social Science Fund of Ministry of Education of China (17YJC790119), the Fundamental Research Funds for the Central Universities (2682021ZTPY077).

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Authors and Affiliations

  1. School of Mathematics, Southwest Jiaotong University, Chengdu, Sichuan, 611756, PR China

    Gaoxiu Qiao & Yangli Cao

  2. School of Economics and Management, Southwest Jiaotong University, Chengdu, Sichuan, 611756, PR China

    Feng Ma

  3. Oklahoma State University, Stillwater, USA

    Weiping Li

Authors
  1. Gaoxiu Qiao
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  2. Yangli Cao
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  3. Feng Ma
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Contributions

Gaoxiu Qiao: Conceptualization, Methodology, Writing-Reviewing and Editing, Funding acquisition. Yangli Cao: Data curation, Methodology, Software, Visualization, Writing. Feng Ma: Writing-Reviewing and Editing Weiping Li: Supervision.

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Correspondence to Feng Ma.

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Qiao, G., Cao, Y., Ma, F. et al. Liquidity and realized covariance forecasting: a hybrid method with model uncertainty. Empir Econ 64, 437–463 (2023). https://doi.org/10.1007/s00181-022-02248-y

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