Semicovariances and Machine Learning Methods in Oil and Gold Futures Markets

28 Pages Posted: 24 Mar 2021

See all articles by Taeyoon Kim

Taeyoon Kim

Pohang University of Science and Technology (POSTECH)

Byung-June Kim

Pohang University of Science and Technology (POSTECH)

Bong-Gyu Jang

Pohang University of Science and Technology (POSTECH)

Kyeong Tae Kim

POSTECH

Date Written: February 18, 2021

Abstract

We adopt the semicovariance decomposition method and machine-learning models to forecast the realized correlation and realized volatility of oil and gold futures markets. The general framework consists of three steps: data preprocessing, accumulating window cross-validation, and performance evaluation. Data preprocessing generates a time-series of realized covariance matrices and semicovariance matrices as model input. Given input data at each time step, forecasting models are fitted with cross-validation. Forecasting models are evaluated using out-of-sample accuracy and robustness tests. In predicting the risk and correlation of oil and gold, the semicovariance decomposition method can achieve higher accuracy and stable results toward the various periods of training data compared to the benchmark DCC-GARCH model. A combination of random forest and the semicovariance decomposition method can increase the out-of-sample accuracy. These results show a possibility that the semicovariance decomposition combined with machine-learning techniques can be efficient even in the commodity futures market.

Keywords: Realized Volatility, Forecasting, Machine Learning, Semicovariance decomposition

JEL Classification: C32, C53, Q02

Suggested Citation

Kim, Taeyoon and Kim, Byung-June and Jang, Bong-Gyu and Kim, Kyeong Tae, Semicovariances and Machine Learning Methods in Oil and Gold Futures Markets (February 18, 2021). Available at SSRN: https://ssrn.com/abstract=3787938 or http://dx.doi.org/10.2139/ssrn.3787938

Taeyoon Kim

Pohang University of Science and Technology (POSTECH) ( email )

77 Cheongam-ro
Pohang
Korea, Republic of (South Korea)

Byung-June Kim (Contact Author)

Pohang University of Science and Technology (POSTECH) ( email )

77 Cheongam-ro
Pohang
Korea, Republic of (South Korea)

Bong-Gyu Jang

Pohang University of Science and Technology (POSTECH) ( email )

77 Cheongam-ro
Pohang
Korea, Republic of (South Korea)

Kyeong Tae Kim

POSTECH ( email )

Nam-gu, Hyoja-dong
Pohang, Kyung-Buk
Korea

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