Time Varying Volatility and GARCH in Risk Management

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In Todays video let's learn about time varying volatility and GARCH in risk management

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What Is Time-Varying Volatility?

Time-varying volatility refers to the changes in market volatility over different time periods. Investors may choose to study or consider volatility of an underlying security during various time periods. The use of time-varied volatility measures can influence the expectations of investments.

How Time-Varying Volatility Works
Time-varying volatility can be studied in any time-frame. Generally, volatility analysis requires mathematical modeling to generate volatility levels as one measure of the risk of an underlying security. This type of modeling generates historical volatility statistics.

What is the GARCH Process?

The generalized autoregressive conditional heteroskedasticity (GARCH) process is an econometric term developed in 1982 by Robert F. Engle, an economist and 2003 winner of the Nobel Memorial Prize for Economics, to describe an approach to estimate volatility in financial markets. There are several forms of GARCH modeling. The GARCH process is often preferred by financial modeling professionals because it provides a more real-world context than other forms when trying to predict the prices and rates of financial instruments.

Heteroskedasticity describes the irregular pattern of variation of an error term, or variable, in a statistical model. Essentially, where there is heteroskedasticity, observations do not conform to a linear pattern. Instead, they tend to cluster. The result is that the conclusions and predictive value one can draw from the model will not be reliable. GARCH is a statistical model that can be used to analyze a number of different types of financial data, for instance, macroeconomic data. Financial institutions often use this model to estimate the volatility of returns for stocks, bonds and market indices. They use the resulting information to help determine pricing and judge which assets will potentially provide higher returns, as well as to forecast the returns of current investments to help in their asset allocation, hedging, risk management and portfolio optimization decisions.