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What Is a Volatility Model?

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  • Written By: Jean Marie Asta
  • Edited By: Jessica Seminara
  • Last Modified Date: 20 September 2016
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A volatility model is a form of modeling that is used to predict moments of uncertainty and potential disruption to normal business practices. These models are used by many data analysts to try to understand and predict moments in the future of their business where changes to the business model may be required in order to remain competitive. A good volatility model can provide a business with an edge on competitors who may not be prepared for future complications in the marketplace.

There are several volatility models in use by analysts today. The ARCH-GARCH model and the stochastic volatility model are two of the most common types. Both of these models determine volatility based on the concept of "white noise." This is a randomized representation of variables in a number field whose graphed sum equals out to zero over the time frame being analyzed.

An ARCH-GARCH volatility model is the simpler form of volatility model. The acronym "ARCH-GARCH" stands for "autoregressive conditional heteroskedasticity generalized - autoregressive conditional heteroskedasticity." These models only interpret one source of white noise as a part of the equation they use to produce results. The stochastic volatility model is more complex, factoring in multiple different calibrations of white noise. These calibrations are meant to represent unforeseen changes, innovations, and alterations to the data that may develop over a period of time.

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Understanding volatility is especially important to people who wish to make investments in stocks and businesses whose value may fluctuate over time. If investors are able to properly determine when their investments are about to enter into times of uncertain profitability, they may be able to withdraw their investments before the value decreases. Alternately, if the degree of volatility can be accurately predicted and investors keep their investments through a period of instability, they may also see their holdings increase considerably.

Although a volatility model is not always entirely accurate, especially over large time frames, it is an important part of the business environment. The fate of a business is dependent on its ability to accurately foresee changes, and so volatility models are in common use today. As technology advances and the study of how markets work is able to be interpreted by computers performing calculations many times more advanced than human economists are capable of, the accuracy and use of these models can only be expected to grow.

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