A structural VAR and VECM modeling method for open-high-low-close data contained in candlestick chart  

作　　者：Wenyang Huang Huiwen Wang Shanshan Wang 

机构地区：[1]College of Economics and Management,China Agricultural University,Beijing,China [2]School of Economics and Management,Beihang University,Beijing,China [3]Key Laboratory of Complex System Analysis,Management and Decision(Beihang University),Ministry of Education,Beijing,China [4]Beijing Advanced Innovation Center for Big Data and Brain Computing,Beijing,China

出　　处：《Financial Innovation》2024年第1期2017-2045,共29页金融创新(英文)

基　　金：the financial support from the Beijing Natural Science Foundation(Grant No.9244030);the National Natural Science Foundation of China(Grant Nos.72021001,11701023).

摘　　要：The structural modeling of open-high-low-close(OHLC)data contained within the candlestick chart is crucial to financial practice.However,the inherent constraints in OHLC data pose immense challenges to its structural modeling.Models that fail to process these constraints may yield results deviating from those of the original OHLC data structure.To address this issue,a novel unconstrained transformation method,along with its explicit inverse transformation,is proposed to properly handle the inherent constraints of OHLC data.A flexible and effective framework for structurally modeling OHLC data is designed,and the detailed procedure for modeling OHLC data through the vector autoregression and vector error correction model are provided as an example of multivariate time-series analysis.Extensive simulations and three authentic financial datasets from the Kweichow Moutai,CSI 100 index,and 50 ETF of the Chinese stock market demonstrate the effectiveness and stability of the proposed modeling approach.The modeling results of support vector regression provide further evidence that the proposed unconstrained transformation not only ensures structural forecasting of OHLC data but also is an effective feature-extraction method that can effectively improve the forecasting accuracy of machine-learning models for close prices.

关 键 词：OHLC data Structural modeling Unconstrained transformation Candlestick chart VAR VECM 

分 类 号：F83[经济管理—金融学]