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Kaijian He
Researcher at Hunan University of Science and Technology
Publications - 77
Citations - 2126
Kaijian He is an academic researcher from Hunan University of Science and Technology. The author has contributed to research in topics: Value at risk & Ensemble forecasting. The author has an hindex of 21, co-authored 68 publications receiving 1351 citations. Previous affiliations of Kaijian He include Hunan University & City University of Hong Kong.
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Exploring the effect of industrial structure adjustment on interprovincial green development efficiency in China: A novel integrated approach
Bangzhu Zhu,Bangzhu Zhu,Mengfan Zhang,Yanhua Zhou,Ping Wang,Jichuan Sheng,Kaijian He,Yi-Ming Wei,Rui Xie +8 more
TL;DR: Wang et al. as mentioned in this paper proposed a novel integrated approach incorporating industrial structure adjustment measurement, super-efficiency slacks-based measure with undesirable outputs and panel regression models to explore the effect of industrial structure adjusting on green development efficiency.
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Extreme risk spillover network: application to financial institutions
TL;DR: In this paper, the authors proposed an extreme risk spillover network for analysing the intimate value at risk (VaR) and the Granger causality risk test (GRLT) to quantify the risk of spillovers.
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A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting
TL;DR: The empirical analysis demonstrates that the proposed model can achieve higher level and directional predictions and higher robustness and seems an advanced approach for predicting the high nonstationary, nonlinear and irregular carbon price.
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Measuring the maturity of carbon market in China: An entropy-based TOPSIS approach
TL;DR: Li et al. as mentioned in this paper proposed an entropy-based TOPSIS model to measure the maturity of carbon market, which used the entropy method to objectively weight the indicators, and the TOPsIS method to measure maturity of the carbon market.
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Crude oil price analysis and forecasting using wavelet decomposed ensemble model
TL;DR: In this paper, a wavelet decomposed ensemble model is proposed to improve the forecasting accuracy of crude oil price with deeper understanding of the market microstructure, which is based on the Heterogeneous Market Hypothesis.