Data-driven modeling of capital liquidity changes during financial market crises

• Published in: Applied mathematics and nonlinear sciences Vol. 9; no. 1
• Main Author: Lu, Man
• Format: Journal Article
• Language: English
• Published: Sciendo 01.01.2024
• Subjects: 68P30; Capital liquidity; Degrees of freedom; Financial market crisis; MCMC method; Vector autoregressive model
• ISSN: 2444-8656; 2444-8656
• DOI: 10.2478/amns-2024-2881

Against the backdrop of the ongoing global financial crisis, clarifying the mechanism of the role of the drivers of capital liquidity changes is the basis for maintaining financial stability during financial market crises. This paper proposes the basic hypothesis of capital liquidity changes during a financial market crisis, with reference to related studies. Subsequently, a data-driven model of capital liquidity changes during the financial market crisis is constructed based on the vector autoregressive model, and the main nodes of capital liquidity changes during the financial market crisis are obtained according to the changes in the degrees of freedom of the model. The MCMC method is utilized to assess the model’s smoothness and ensure the stability of the data-driven model of capital liquidity changes. The empirical analysis of the model finds that the positive shock of capital liquidity change on stock growth rate exhibits a positive response, and the stock growth rate explains 0.527% and 1.292% of the capital liquidity change on average during one year of financial market crisis. It has also been found that the arbitrage motive drives capital liquidity more strongly during a financial market crisis compared to the hedging motives. The model proposed in this paper helps to prompt policymakers to monitor the changes in capital mobility and make useful decisions in a timely manner, which in turn maintains the stable operation of the financial market.