The ARCH Model And The Application Of It In The Time Series

Since the 1970s in 20th century, with the macroeconomic environment changed, international and domestic financial markets had experienced profound transformation, financial market volatility and financial risks had increased clearly. It is important to understand and master the law and structure of fluctuations in the financial markets that how to measure the financial fluctuations analyze and depict the characteristics of financial volatility. And the measurement and analysis of financial volatility must be realized through scientific methods and tools.Measuring the volatility of financial risk is an important field in finance. Volatility in the article is the variance of asset return, which varies with time going and this is also called heteroscedasticity in Econometrics. Many high-frequency financial time series appear heteroscedastic. There are two methods of measuring volatility: One is ARCH models, including ARCH, GARCH and other extended models, the other one is SV model. These two models have been widely applied in modeling and research of economic field, especially of financial markets.In this paper, ARCH model, GARCH model and other extended models are introduced in detail and their properties and characteristics are analyzed. The major work done in my article: systematically elaborating the background, statistical properties of autoregressive conditional heteroscedasticity model communities; the estimate of parameter and the hypothesis test of the ARCH and the GARCH model are introduced in detailed; With the establish of the time series data of GDP of Jiang Su province, investigate the application of the ARCH model, with the analysis and calculation of kinds of models, validate the practicability and applicability of the ARCH model indirectly.