Korea Trade Review (무역학회지)

Korea Trade Research Association (한국무역학회)
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Volatility analysis and Prediction Based on ARMA-GARCH-typeModels: Evidence from the Chinese Gold Futures Market

ARMA-GARCH 모형에 의한 중국 금 선물 시장 가격 변동에 대한 분석 및 예측

  • Meng-Hua Li (Department of International Trade, Dongguk University) ;
  • Sok-Tae Kim (Department of International Trade, Dongguk University)
  • 이몽화 (동국대학교 국제통상학과) ;
  • 김석태 (동국대학교 국제통상학과)
  • Received : 2022.06.07
  • Accepted : 2022.06.28
  • Published : 2022.06.30
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Abstract

Due to the impact of the public health event COVID-19 epidemic, the Chinese futures market showed "Black Swan". This has brought the unpredictable into the economic environment with many commodities falling by the daily limit, while gold performed well and closed in the sunshine(Yan-Li and Rui Qian-Wang, 2020). Volatility is integral part of financial market. As an emerging market and a special precious metal, it is important to forecast return of gold futures price. This study selected data of the SHFE gold futures returns and conducted an empirical analysis based on the generalised autoregressive conditional heteroskedasticity (GARCH)-type model. Comparing the statistics of AIC, SC and H-QC, ARMA (12,9) model was selected as the best model. But serial correlation in the squared returns suggests conditional heteroskedasticity. Next part we established the autoregressive moving average ARMA-GARCH-type model to analysis whether Volatility Clustering and the leverage effect exist in the Chinese gold futures market. we consider three different distributions of innovation to explain fat-tailed features of financial returns. Additionally, the error degree and prediction results of different models were evaluated in terms of mean squared error (MSE), mean absolute error (MAE), Theil inequality coefficient(TIC) and root mean-squared error (RMSE). The results show that the ARMA(12,9)-TGARCH(2,2) model under Student's t-distribution outperforms other models when predicting the Chinese gold futures return series.

Keywords

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