Forecasting Chinese crude oil futures volatility using dynamic volatility spillover CARR-MIDAS model

Xinyu Wu; Yuanzheng Liu; Junlin Pu; Xiaona Wang; Xinyu Wu; Yuanzheng Liu; Junlin Pu; Xiaona Wang

doi:10.3934/math.20251063

AIMS Mathematics

2025, Volume 10, Issue 10: 23919-23942. doi: 10.3934/math.20251063

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Forecasting Chinese crude oil futures volatility using dynamic volatility spillover CARR-MIDAS model

1.
International Business School, Anhui University of Finance and Economics, Hefei 230071, China
2.
School of Finance, Anhui University of Finance and Economics, Bengbu 233030, China
3.
China School of Banking and Finance, University of International Business and Economics, Beijing 100029, China
4.
School of Finance, Tongling University, Tongling 244061, China

Received: 15 July 2025 Revised: 02 October 2025 Accepted: 13 October 2025 Published: 21 October 2025
MSC : 62M10, 91B84, 91G20

This paper proposes a dynamic volatility spillover conditional autoregressive range-mixed data sampling (DVS-CARR-MIDAS) model to forecast volatility in the Chinese crude oil futures market by incorporating dynamic volatility spillovers from the dominant US crude oil futures market to the emerging Chinese crude oil futures market. Empirical results based on West Texas Intermediate (WTI) crude oil and Shanghai International Energy Exchange (INE) data revealed significant and time-varying spillover effects from the US to the Chinese market. In addition, the DVS-CARR-MIDAS model consistently showed that the proposed model consistently outperforms benchmark models in both in-sample fitting and out-of-sample forecasting. These findings were robust to the Diebold-Mariano (DM) test, $ R_{oos}^2 $ test, alternative dominant market, and different out-of-sample forecast windows. Furthermore, the economic value analysis demonstrated that the proposed model provides meaningful benefits for portfolio management.
- volatility forecasting,
- dynamic volatility spillover,
- DVS-CARR-MIDAS,
- crude oil market
Citation: Xinyu Wu, Yuanzheng Liu, Junlin Pu, Xiaona Wang. Forecasting Chinese crude oil futures volatility using dynamic volatility spillover CARR-MIDAS model[J]. AIMS Mathematics, 2025, 10(10): 23919-23942. doi: 10.3934/math.20251063

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Abstract

This paper proposes a dynamic volatility spillover conditional autoregressive range-mixed data sampling (DVS-CARR-MIDAS) model to forecast volatility in the Chinese crude oil futures market by incorporating dynamic volatility spillovers from the dominant US crude oil futures market to the emerging Chinese crude oil futures market. Empirical results based on West Texas Intermediate (WTI) crude oil and Shanghai International Energy Exchange (INE) data revealed significant and time-varying spillover effects from the US to the Chinese market. In addition, the DVS-CARR-MIDAS model consistently showed that the proposed model consistently outperforms benchmark models in both in-sample fitting and out-of-sample forecasting. These findings were robust to the Diebold-Mariano (DM) test, $ R_{oos}^2 $ test, alternative dominant market, and different out-of-sample forecast windows. Furthermore, the economic value analysis demonstrated that the proposed model provides meaningful benefits for portfolio management.

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