Financial Time series including ARCH-Garch models Research Paper

Financial Time series including ARCH-Garch models - Research Paper Example 'Financial Time series including ARCH-Garch models' forms the basis of financial and macroeconomics where model builders use stochastic processes to test and construct equations of economic variables. Time varying volatility and non-stationarity has largely contributed to the understanding and applicability of financial time series. Economic variables are referred as non-stationary when there is no tendency of being linear or constant and assume stochastic trends. Empirical research is often conducted in macroeconomics to estimate variable relationships and test hypothesis of the theories of macroeconomics. Empirical financial models are formulated based on cointergration concept that forms the basis of the major breakthrough in macro and financial economics. The aspect of volatility is important to financial economics such as the stock exchange and capital markets. Indeed, financial managers and analyst have repeatedly used the time series volatility model to forecast volatility and make relevant decision concerning future and current investments in the financial markets. Financial time series plays a big role in modeling and forecasting the financial operations of such corporations for countermeasures and avoidance of future financial crisis. The dynamics of corporate returns and stock prices can be effectively managed through application of financial time series and forecasting. Fluctuations of returns and the speculative prices in the stock markets are presented and modeled by time series volatility, which helps in decision making in investment. ... The dynamics of corporate returns and stock prices can be effectively managed through application of financial time series and forecasting. Fluctuations of returns and the speculative prices in the stock markets are presented and modeled by time series volatility, which helps in decision making in investment. Time series models such as the autoregressive conditional heteroscedasticity are used by financial analyst to determine the relationship between returns and risks levels in investments. Volatility of the sequence of the returns in money markets, foreign exchange markets, and stock markets are best described by the autoregressive conditional heteroscedasticity model in financial time series. Advance usage of the autoregressive conditional heteroscedasticity model is also used in the futures markets and by profession and executives in the stock markets to enforce counteractive measures to stabilize the markets. China stock market is a good example of a market that has successfully used the autoregressive conditional heteroscedasticity model to describe the market2 Arch Methodology This financial time series model is used to forecast random variables from information of past variable trends and linearity through specific universal assumptions on the conditional macroeconomic and financial economic variables. Forecast of variables depends on past information with regards to conditional and random variables assumption although conditional variance in conventional models does not depend on the random variables past information but on estimations and test. Econometric use of the Autoregressive Conditional Heteroskedasticity Model focuses on the limitations of forecasting in resultant prediction of the future that varies from one period to