Abstract
In order to improve the effect of bank interest rate volatility analysis, this article combines actual conditions and machine learning algorithms to construct a fluctuation analysis model of bank interest rate based on computer statistical model and machine learning. For the data with system transformation, the data contains stationary and nonstationary processes; so, the power of the standard unit root test is low. This paper therefore proposes a new unit root test method. From demand analysis, system design to system implementation, and testing, advanced software engineering-related ideas are adopted, and the bank’s interest rate management system is designed and implemented in strict accordance with software development-related processes. This paper adopts the modular design idea, classifies the functions to be realized according to their content, and conducts structural verification and performance analysis of the functional modules. Through experimental analysis, we can see that the system model constructed in this paper has certain effects in the analysis of interest rate fluctuations.
Highlights
With the continuous improvement of China’s financial market mechanism and the continuous enrichment of market levels, the interest rate sensitivity of microentities has been greatly improved
It is necessary to ensure the smooth operation of the benchmark interest rate of the money market and the ability to dilute liquidity shocks and ensure the smooth transmission of monetary policy guidance from the short-term money market to the long-end capital market and to the real economy
Studying the characteristics of currency market interest rate fluctuations and how effective influencing factors affect money market interest rate volatility is of great significance for the central bank to gradually improve its ability to guide money market interest rates and improve the predictability of money market liquidity shocks, thereby improving participants’ resilience
Summary
With the continuous improvement of China’s financial market mechanism and the continuous enrichment of market levels, the interest rate sensitivity of microentities has been greatly improved. It is necessary to ensure the smooth operation of the benchmark interest rate of the money market and the ability to dilute liquidity shocks and ensure the smooth transmission of monetary policy guidance from the short-term money market to the long-end capital market and to the real economy. On the one hand, the central bank has focused on building a currency market benchmark interest rate with global representative capabilities, ensuring that its fluctuations can represent the actual situation of currency market liquidity shocks and controlling its fluctuations within a certain range. The government has built a rich monetary policy tool system, stabilized the liquidity impact expectations of commercial banks, improved the predictability of monetary policy, strengthened the central bank’s ability to guide the money market benchmark interest rate, and reduced the friction of monetary policy in the money market-credit market-real economic transmission channel. Based on the above analysis, this article analyzes the fluctuation of bank interest rates based on computer statistical models and machine learning models
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