Research On Application Of Improved VaR-GARCH Volatility Model In Risk Management

Volatility is an important indicator for describing the risk of financial markets.It provides a modeling basis for calculating the risk value of financial positions in risk management.It is an important topic for studying financial market risks.For investors,accurately assessing market risks can provide good guidance for investment decisions and make full use of resources.This is also the significance of studying the volatility of the stock market.At the same time,the fluctuation of financial asset prices is also related to life.Various economic costs and risks,and accurately describing the volatility of financial assets have increasingly become the research focus of scholars and investors.The structure of this paper can be summarized into three aspects: First,through a detailed study of the literature on volatility and risk management,it provides a theoretical basis for improving the model;The second is to describe common volatility models and improved volatility structural change models;the third is to apply theory to practice,select CSI 300 index return data for empirical analysis and draw conclusions.The work in the empirical part mainly includes: one is to process the data and relevant tests;the other is to use GARCH to fit the volatility curve of the return rate;the third is to use the cluster analysis technology to cut the volatility curve to obtain a improved VaR-GARCH volatility model;The fourth is to calculate the corresponding VaR through the model.Estimation of volatility is not only a key point in financial market risk management,but also a difficult point.This paper intends to find a method that can take into account the characteristics of volatility agglomeration and relatively stable method of calculating estimated volatility.It is assumed that the volatility is a fixed constant throughout the time period,which is different from the method of real-time estimation.Therefore,the innovation of this paper is that the clustering algorithm is used to segment the volatility curve estimated by the GARCH model,which not only retains the continuity of time series data,but also divides it into several cells,and fluctuates within each cell.The rate is approximately a constant,which improves the accuracy of the volatility estimation,and can also make up for the computational difficulty of real-time estimation and reduce the impact of fluctuation noise.This model was introduced into the risk management in the empirical calculation and it was found that it improved the accuracy of prediction,so this model can be used as a volatility estimation model and applied in risk management.