GARCH Process

What is GARCH Process?

Coined by Robert F, as per the GARCH process meaning, GARCH stands for Generalized Autoregressive Conditional Heteroskedasticity. The concept is used to figure out the Volatility level in financial markets. Many experts and professional investors prefer the GARCH approach to find out the volatility in the stock Market.

They consider it a precise and authentic technique to predict future trends in the stock Industry. It can also be used to ascertain the price of all types of financial instruments that are open to investment and trading.

Overview of GARCH Process

The term Heteroskedasticity refers to the uneven pattern of variables. Basically, the variables do not form a linear pattern in Heteroskedasticity. They rather form a cluster. That is the reason why the estimated value that we get from the conclusion will not be accurate. This statistical model is mainly used to identify a Range of financial instruments and help users predict the trends and price changes in these commodities or financial instruments over time.

Even the established financial institutions use the GARCH approach to conduct proper market research on the stocks and instruments and figure out the volatility level. They use the results they obtain from the conclusion to predict the stock pricing and find out which asset has the potential to perform better in the long run. This process can also be used to estimate the returns on your investment, giving you an opportunity to allocate your assets and make investments accordingly.

Many investors and local traders consider the GARCH approach for improving their investment Portfolio, while others use it as a way to make informed investment decisions.

How the GARCH Model is Different from Homoskedastic Approach?

Now, it is important to note that the GARCH model is completely different from the standard homoskedastic approach, in which, the investors tend to assume the constant volatility. The latter is commonly used in OLS (Ordinary Least Squares) analysis. While this model might prove accurate, we can’t neglect the fact that volatility level changes from time to time. When it comes to asset returns, volatility is never the same. Some part of volatility depends on the past variance. Keeping all these factors in mind, it is safe to say that Ordinary Least Squares Analysis can be suboptimal.

Now that GARCH is an autoregressive approach, it relies on past variances in the stock industry to determine the current variance. The model is extensively used in the financial markets and stock industry because of its reliability. It has proven to be an effective model in figuring out Inflation as well as asset returns. The main objective is to minimize all sorts of possible forecasting errors and enhance the accuracy of price prediction in the financial markets. It takes the past variances into consideration to help investors make accurate future predictions.

The concept highlights the markets that have a varying volatility rate. In other words, it indicates the financial markets, where the volatility is expected to change. Basically, volatility in these markets tends to be higher during an economic crisis.