Finite-Time Impulsive Control of Financial Risk Dynamic System with Chaotic Characteristics

Abstract

The control of financial risk has always been one of the important topics in financial research. Based on the theory of finance, this paper proposes a kind of financial risk dynamic system. By analyzing some properties of the dynamic system, the system shows obvious coexisting chaotic oscillations. In order to control the financial risk dynamic system effectively, this paper proposes a finite-time impulse controller to control the financial risk dynamic system. Simulation results show that the finite-time impulse controller has faster convergence speed than the impulse controller.

Multistability Analysis and Adaptive Feedback Control on a New Financial Risk System

“…Zhang et al [11] presented the process of risk management for financial markets via chaotic dynamic behavior analysis and positive feedback gain matrix method. Ziyan et al [12] presented a finite-time impulse controller and risk model for the financial system with a three-stage cycle structure to regulate the dynamic system's financial risk. Using the fractional Lyapunov stability criterion, [13] investigated the FR dynamical system model in fractional-order and examined the adaptive fuzzy control of the FR chaotic system in fractional-order.…”

Realizing stochastic fixed-time synchronization between two nonlinear chaotic finance systems

“…Ahmad et al [21] have employed the finite-time control strategy to stabilize the CFS, which can avoid oscillation with the parameters varying and suffering from disturbance. And recently, the finite-time impulse control scheme has been proposed for suppressing the chaotic behavior in the financial risk system [22].…”

“…However, the upper-bound of time-estimation(UT) for realizing the finite-time stabilization or synchronization is tightly related with the initial states of CFSs [18]- [22]. That is, the UT cannot be known in advance due to the uncertainty of initial states.…”

“…(1) Distinguished from the works of [11]- [22] and [34,36], this investigation considers the influence of noise perturbations in the response CFS, and proposes the definition of SFxS between the drive-response CFSs for the first time;…”

A deep learning model-based approach to financial risk assessment and prediction

“…For example, the rapid bursting of the international asset price bubble in Japan in the early 1990s caused Japan's domestic economic growth to fall into a long-term slump, but the global Asian financial turmoil in the mid-to late-1990s curbed the rapid and healthy development of the Asian economy as a whole, and the U.S. subprime mortgage crisis in 2007 and the international financial crisis in 2008 brought economic and financial development around the world to a halt The subprime mortgage crisis in the United States in 2007 and the international financial crisis in 2008 brought economic and financial development around the world to a halt, recession and even political crisis [4][5]. This series of crisis events not only seriously threaten national financial security but also bring serious challenges to sustainable economic development, making the recognition and control of systemic financial risks a key concern of national regulators [6][7].…”