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Leveraging asymmetric price limits for financial stability in industrial applications: An agent-based model
Computers in Industry ( IF 8.2 ) Pub Date : 2024-10-30 , DOI: 10.1016/j.compind.2024.104197 Xinhui Yang, Jie Zhang, Qing Ye, Victor Chang
Computers in Industry ( IF 8.2 ) Pub Date : 2024-10-30 , DOI: 10.1016/j.compind.2024.104197 Xinhui Yang, Jie Zhang, Qing Ye, Victor Chang
How to upgrade business processes to improve production efficiency is an ongoing concern in industrial research. While previous studies have extensively examined various prioritization schemes at each stage of the business process, there has been a lack of investigation into the financial resources required for their implementation. The attainment of sufficient and stable financial support necessitates stability in stock prices, making the control of significant volatility in stock markets a critical issue. This study examines the effectiveness of three design schemes of price limit policy, a prevalent policy that intends to control significant volatility in financial markets and stabilize the market. Utilizing a heterogeneous agent-based model that simulates trading agents' processes of updating strategies through genetic programming algorithms and incorporates specialized designs for price limit policies, this study demonstrates that an asymmetric limit policy—consisting solely of a lower price limit (without an upper price limit)—can significantly enhance market quality by achieving lower volatility, higher market liquidity and better price effectiveness. Furthermore, we investigate the applicable conditions of asymmetric price limits. The findings suggest that an extremely restrictive limit range could lead to volatility spillover, while a 10 % range is deemed appropriate for achieving better efficiency. Additionally, the asymmetric price limit mechanism has the potential to significantly reduce market volatility by up to 12.5 % in volatile, low liquidity, and low price efficiency markets, which aligns with the declining range from bubble-crash periods to stable periods in the Chinese stock market. These results are further supported by sensitivity analysis.
更新日期：2024-10-30
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