The exponentially weighted moving average (EWMA) approach captures short-term movements in volatility for a conditional volatility forecasting model. By virtue of its alpha, or decay factor, this provides a statistical streaming data model that is exponentially biased towards newer entries. http://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average An EWMA only needs the most recent forecast value to be kept, as opposed to a standard moving average model. INTERNAL API @param alpha decay factor, sets how quickly the exponential weighting decays for past data compared to new data, see http://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average @param value the current exponentially weighted moving average, e.g. Y(n - 1), or, the sampled value resulting from the previous smoothing iteration. This value is always used as the previous EWMA to calculate the new EWMA.

public sealed class EWMA

[<SealedAttribute>]
type EWMA =  class end