 Application of the LPPL model in the identification and measurement of structural bubbles in the Chinese stock marketHongyun Ji and Han Zhang The North American Journal of Economics and Finance, 2024, vol. 70, issue C Abstract: This study proposes innovative methods for determining the starting point and estimating the parameters of the LPPL model for predicting stock market bubbles. These methods address the challenges in determining the starting point of irregularly staged bubbles and the issues of multiple local optima or no solutions in previous prediction algorithms. The proposed LPPL model, incorporating innovative algorithms, is applied to the Chinese A-share market from 2018 to 2021. The starting points of structural bubbles are determined using the difference and grid search methods, and the sequential quadratic programming (SQP) algorithm is used to estimate the parameters of the LPPL model. The study measures and warns about structural bubbles in the A-share market. The results demonstrate that the LPPL model with the innovative algorithm can accurately measure staged and structural bubbles in the A-share market and provide reasonably accurate warnings of bubble bursts, which have been confirmed by the market multiple times. Keywords: Chinese stock market; Structural bubble; LPPL model; SQP algorithm (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:70:y:2024:i:c:s1062940823001833 DOI: 10.1016/j.najef.2023.102060 Access Statistics for this article The North American Journal of Economics and Finance is currently edited by Hamid Beladi More articles in The North American Journal of Economics and Finance from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.