Liu, T., Zhang, Y., Zhang, W., & Hamori, S. (2024). Quantile Connectedness of Uncertainty Indices, Carbon Emissions, Energy, and Green Assets: Insights from Extreme Market Conditions. Energies, 17(22), 5806.
Mou, S., Xue, Q., Zhang, W., Kinkyo, T., Hamori, S., Chen, J., Takiguchi, T., & Ariki, Y. (2024, July). Integrating Textual and Financial Time Series Data for Enhanced Forecasting. 2024 16th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) (pp. 541-544). IEEE.
Zhang, W., Liu, T., Zhang, Y., & Shigeyuki Hamori. (2024). Can NFTs hedge the risk of traditional assets after the COVID-19 pandemic? The North American Journal of Economics and Finance, 72, 102149.
Zhang, W., He, X., & Shigeyuki Hamori. (2023). The impact of the COVID-19 pandemic and Russia-Ukraine war on multiscale spillovers in green finance markets: Evidence from lower and higher order moments. International Review of Financial Analysis, 89, 102735–102735.
Zhang, W., He, X., & Hamori, S. (2022). Volatility spillover and investment strategies among sustainability-related financial indexes: Evidence from the DCC-GARCH-based dynamic connectedness and DCC-GARCH t-copula approach. International Review of Financial Analysis, 83, 102223.
Zhang, W., & Hamori, S. (2021). Crude oil market and stock markets during the COVID-19 pandemic: Evidence from the US, Japan, and Germany. International Review of Financial Analysis, 74, 101702. (Highly cited)
Zhang, W., & Hamori, S. (2021b). THE CONNECTEDNESS BETWEEN THE SENTIMENT INDEX AND STOCK RETURN VOLATILITY UNDER COVID-19: A TIME-VARYING PARAMETER VECTOR AUTOREGRESSION APPROACH. The Singapore Economic Review, 1–32.
Zhang, W., & Hamori, S. (2020). Do Machine Learning Techniques and Dynamic Methods Help Forecast US Natural Gas Crises? Energies, 13(9), 2371.
Zhang, W., He, X., Nakajima, T., & Hamori, S. (2020). How Does the Spillover among Natural Gas, Crude Oil, and Electricity Utility Stocks Change over Time? Evidence from North America and Europe. Energies, 13(3), 727.
Nakajima, T., Hamori, S., He, X., Liu, G., Zhang, W., Zhang, Y., & Liu, T. (2021). ESG Investment in the Global Economy. Springer.
Cong, Y., Zhang, W., Cai, X., & Tian, S. (2024, November 14). Hybrid deep learning approaches for high-frequency carbon prices forecasting. ssrn.com.
Zhang, W., Hamori, S., & Cai, X. (2024, November 11). How Geopolitical Crises Influence BRICS Financial Markets and Macroeconomic Growth: Insights from the Russia-Ukraine War Using GARCH-MIDAS and Quantile Regression. ssrn.com.
Mou, S., Zhang, W., Kinkyo, T., Hamori, S., Chen, J., Takiguchi, T., & Ariki, Y. (2024). Enhancing Economic Time Series Prediction with News Text Data and Numerical Data: A Transformer-Based Approach. 言語処理学会第30回年次大会発表論文集 2024
Nippon Finance Association (NFA) 6th Fall Conference, Can NFTs hedge the risk of traditional assets after the COVID-19 pandemic? Kyushu University, Ito Campus, 9 November, 2024.
The 6th International Conference on Econometrics and Statistics (EcoSta 2023), Can NFTs hedge the risk of traditional assets after the COVID-19 pandemic? Waseda University, Nishi-Waseda Campus, 1-3, August 2023.
International Review of Financial Analysis
The Singapore Economic Review
Frontiers in Environmental Science
Applied Economics Letters
North American Journal of Economics and Finance
Financial Innovation
Cogent Economics & Finance
Finance Research Letters
JSPS (Grant-in-Aid for Research Activity Start-up) 24K22651: "地政学リスクが金融市場とマクロ経済へのリスク波及効果:GARCH-MIDAS-QRとQC-FTにより": July 2024-March 2026
JSPS (Grant-in-Aid for Early-Career Scientists) 25K16691: "リスク波及効果を考慮した革新的深層学習モデルの開発と高頻度金融時系列予測への応用": April 2025-March 2030