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

Gaoxiu Qiao, Yangli Cao, Feng Ma () and Weiping Li
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Gaoxiu Qiao: Southwest Jiaotong University
Yangli Cao: Southwest Jiaotong University
Feng Ma: Southwest Jiaotong University
Weiping Li: Oklahoma State University

Empirical Economics, 2023, vol. 64, issue 1, No 17, 437-463

Abstract: 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.

Keywords: Liquidity; Realized covariance; CSI 300 index and futures markets; Model uncertainty; Weighted average windows method; G14; G13 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00181-022-02248-y

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