Systemic Risk And Liquidity Rescue Under Liquidity Shocks

During the latest global financial crisis, the problem of systemic risk and rescue strategies for financial institutions have come into focus. How to lay a solid theoretical foundation for systemic risk measurement and rescue decision is an important academic problem at present. In response to the financial crisis, one key point of international regulation policy is to strengthen "macro-prudential" approaches to financial regulation. In contrast to micro-prudential regulation, macro-prudential regulation cares about the whole financial system and views it as a complex financial network system, examining the robustness of system based on the complex network structure instead of the robustness of single notes, as the latter is neither a necessary condition nor a sufficient condition for system robustness. The critical of macro-prudential regulation is employing quantitative methods to examine and evaluate the potential risk factors in financial system, predicting and evaluating the systemic risk and its contagion, identifying the important financial institutions, and then adopting niche-targeting regulatory measure and rescue measure to make sure the financial system can defend the exogenous shocks so as to effectively guard against and dissolve systemic risk to maintain economy and finance stability. Many countries have set about building macro-prudential regulation regime, for example, the US regulatory institution has proposed to reform the current regulatory regime and regulate according to the importance and connectivity of financial institutions in the networks. Obviously, an important technical element of macro-prudential regulation is to correctly measure systemic risk and its contagious mechanism in financial network.As an important infrastructure of the financial system of a country, large value payment system (LVPS) is responsible for funds transfer between banks and being important channel and basic platform for national treasury management and centre bank to implement monetary policy. So the LVPS is also the blood circulation system of finance. The efficient and safe operation of the payment and settlement system is related with the economic development, financial and social stability, and if the systemic risk breaks out, the destruction will be unbelievable and immeasurable, so LVPS is naturally an important object of macro-prudential regulation. In LVPS, participants are connected through payment network, and any shocks to network or notes will affect the efficiency and robustness of system by the contagious effect of network. These shocks may come from the external economic system, such as terroristic attacks, common health events, or natural disasters, and it also may come from the internal economic system, such as economic crisis or financial crisis. Shocks may be of full-scale or part-scale. Full-scale shocks will affect the liquidity level of the whole system, and part-scale shocks will affect the liquidity level by causing participant-level operational risk.Because of the complexity of the network structure and settlement mechanism, it is hard to apply traditional economic models to study, simulation becomes a widely used method in this field. Different from the majority papers using simulating software BOF-PSS2developed by Finland centre bank for simulation study, this paper adopts Matlab programs to simulate in order to improve efficiency and preciseness of the study, which is also an innovation point of this paper.Based on the structure of Chinese LVPS, status of participants, real trade circumstances, and some open data released by the PBC, this paper describes the payment network topology structure which involves three policy banks, five state-owned commercial banks, twelve joint-equity commercial banks, one city commercial bank, one rural commercial bank, one urban credit cooperative, one rural credit cooperative, one foreign bank and one China Postal Savings Bank,26banks in total.Based on the network topology structure, simulated data and different operational risk scenarios, this paper studies the system’s robustness and participants’importance of China’s large value payment system. The results show that large state-owned banks are the most important resource of potential operational risk and the greatest threat to the robustness of system. The policy banks are the most frangible notes and amplifier of liquidity risk. In contrast, city commercial banks and rural credit cooperatives are the stabilizers of liquidity risk.For the supervision of systemic risk, what’s more important is safeguard before the risk took shape, and rescuing the participants contrapuntally when it is predicted that systemic risk is likely to happen. Therefore, this paper constructs a dynamic model of systemic risk to predict and measure the potential systemic risk. To prove the model we constructed is effective and usable, we test it via simulating. The results show that the systemic risk index model constructed can effectively estimate the systemic risk and represent the dynamics of systemic risk.Chapter four is the core part of this paper. In this chapter, we propose the systemic full rescue strategy, four individual non-full rescue strategies and three individual full rescue strategies. As for choice of different liquidity rescue strategies under different risky scenarios in financial complex network, at the angle of large value payment system, this paper studies the performance of the above different rescue strategies under different liquidity rescue levels and different scenarios based on the former constructed model. The results show that the equilibrium strategy out of four selected individual non-full rescue strategies is dominant under most situations, which demonstrates the existence of scope-externality of rescue. But two individual full rescue strategies, which rescue the small shocked members or high efficiently rescued members at first, are better than the equilibrium strategy.The contribution of this paper is that it analyses the effect that extreme cases have on China’s LVPS systematically for the first time, and identifies the key notes of the system using kinds of indicators. Especially, on the premise of subdividing different shock effect, via mathematic modeling to predict systemic risk and discuss the contagious mechanism and path of liquidity shock in the system, this paper studies the optimal liquidity rescue strategies to participants from the viewpoint of centre bank, which can provide theoretical support to systemic risk management and emergency management in the LVPS and similar financial complex networks.