Large Dynamic Covariance Matrix Estimation with an Application to Portfolio Allocation: A Semiparametric Reproducing Kernel Hilbert Space Approach

PENG Siyang, GUO Shaojun, LONG Yonghong

系统科学与复杂性(英文) ›› 2022, Vol. 35 ›› Issue (4) : 1429-1457.

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系统科学与复杂性(英文) ›› 2022, Vol. 35 ›› Issue (4) : 1429-1457. DOI: 10.1007/s11424-021-0168-3

Large Dynamic Covariance Matrix Estimation with an Application to Portfolio Allocation: A Semiparametric Reproducing Kernel Hilbert Space Approach

    PENG Siyang1, GUO Shaojun2, LONG Yonghong1
作者信息 +

Large Dynamic Covariance Matrix Estimation with an Application to Portfolio Allocation: A Semiparametric Reproducing Kernel Hilbert Space Approach

    PENG Siyang1, GUO Shaojun2, LONG Yonghong1
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文章历史 +

摘要

The estimation of high dimensional covariance matrices is an interesting and important research topic for many empirical time series problems such as asset allocation. To solve this dimension dilemma, a factor structure has often been taken into account. This paper proposes a dynamic factor structure whose factor loadings are generated in reproducing kernel Hilbert space (RKHS), to capture the dynamic feature of the covariance matrix. A simulation study is carried out to demonstrate its performance. Four different conditional variance models are considered for checking the robustness of our method and solving the conditional heteroscedasticity in the empirical study. By exploring the performance among eight introduced model candidates and the market baseline, the empirical study from 2001 to 2017 shows that portfolio allocation based on this dynamic factor structure can significantly reduce the variance, i.e., the risk, of portfolio and thus outperform the market baseline and the ones based on the traditional factor model.

Abstract

The estimation of high dimensional covariance matrices is an interesting and important research topic for many empirical time series problems such as asset allocation. To solve this dimension dilemma, a factor structure has often been taken into account. This paper proposes a dynamic factor structure whose factor loadings are generated in reproducing kernel Hilbert space (RKHS), to capture the dynamic feature of the covariance matrix. A simulation study is carried out to demonstrate its performance. Four different conditional variance models are considered for checking the robustness of our method and solving the conditional heteroscedasticity in the empirical study. By exploring the performance among eight introduced model candidates and the market baseline, the empirical study from 2001 to 2017 shows that portfolio allocation based on this dynamic factor structure can significantly reduce the variance, i.e., the risk, of portfolio and thus outperform the market baseline and the ones based on the traditional factor model.

关键词

Dynamic structure / factor models / high dimensional covariance matrices / portfolio allocation / reproducing kernel Hilbert space

Key words

Dynamic structure / factor models / high dimensional covariance matrices / portfolio allocation / reproducing kernel Hilbert space

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PENG Siyang , GUO Shaojun , LONG Yonghong. Large Dynamic Covariance Matrix Estimation with an Application to Portfolio Allocation: A Semiparametric Reproducing Kernel Hilbert Space Approach. 系统科学与复杂性(英文), 2022, 35(4): 1429-1457 https://doi.org/10.1007/s11424-021-0168-3
PENG Siyang , GUO Shaojun , LONG Yonghong. Large Dynamic Covariance Matrix Estimation with an Application to Portfolio Allocation: A Semiparametric Reproducing Kernel Hilbert Space Approach. Journal of Systems Science and Complexity, 2022, 35(4): 1429-1457 https://doi.org/10.1007/s11424-021-0168-3

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基金

This paper was supported by National Natural Science Foundation of China under Grant No. 11771447.
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