Application analysis of ARCH model. Since 1982, economists and econometricians have been trying to continuously enhance our ability to explain and predict markets by constantly exploring the potential of this model. Judging from the research situation abroad, there are roughly two research directions:

Research on the expansion of ARCH model

The first is to study the expansion of ARCH model and improve the ARCH model. Since the inception of the ARCH model, it has experienced two breakthroughs. Once, Bollerslev T. proposed Generalized ARCH (Generalized ARCH), that is, the GARCH model. Since then, almost all new results of the ARCH model have been obtained based on the GARCH model. The second time was due to the breakthrough in the research of long memory in economics. The research on fractal integration was proved to be more effective in describing certain long memory economic phenomena, and a series of long memory ARCH models were born by combining it with the ARCH model. Research has been in the ascendant since 1996.

Using ARCH as an effective tool to measure the volatility of data

The second application is to use the ARCH model as an effective tool to measure the volatility of financial time series data, and apply in a wide range of research areas related to volatility. Including policy research, theoretical proposition testing, seasonal analysis, etc.

The ARCH model can accurately simulate the changes in volatility of time series variables. It is widely used in empirical research in financial engineering, allowing people to grasp risks (volatility) more accurately, especially in applications The Value at Risk theory is a well-known tool on Wall Street.

It is foreseeable that future research will be further developed in the two directions of methodology and instrumental theory, especially its applied research is still expanding, especially with the maturity of market microstructure theory, the use of ARCH model To simulate volatility, it will provide a broader research space for futures trading system design, risk control system design and investment portfolio risk management strategy research.