How does theFinancials.com calculate Exponentially Weighted Moving Average volatility (VolEWMA)?
The Exponentially Weighted Moving Average (EWMA) approach to characterizing volatility is an example of exponential smoothing. Exponential Smoothing (ES) techniques employ one or more exponential smoothing parameters to give more weight to recent observations and less weight to older observations, in an attempt to respond “dynamically” to the changing value of the time series. The smoothing process is exponential because the weights employed are not arithmetic, but, instead lie along an exponential curve. EWMA is an example of the simplest form of the exponential smoothing method, or Single Exponential Smoothing (SES), which, logically enough, employs a single smoothing parameter. Several assumptions must be made about the nature of the data making up the underlying time series, in order for an SES technique like EWMA to be an appropriate analytic tool: • The first assumption is that the process generating the data is “stationary”, meaning that the data is in equilibrium, or moves rand
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