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Exponentially Weighted Moving Average

An Exponentially Weighted Moving Average shows how data averages over time as the weight of the data decreases.

What is the EWMA Model?

An exponentially Weighted Moving Average is a statistic for monitoring the process of averaging data in a way that lends less and less weight to data as time passes.

The only decision a user of the EWMA must make is the parameter alpha. The value of this parameter determines how important the current observation is in the EWMA calculation. The higher the alpha value, the more closely the EWMA tracks the original time series.

Example

EWMAt = α×rt + (1-α) EWMAt-1

Where:
α = The weight decided by the user
r = Value of the series in the current period

Why is an Exponentially Weighted Moving Average important?

The EWMA is widely used in computing the return volatility in risk management. There are various methods of computing the return volatility of a price series, like the historical standard deviation method, the EWMA model, and the GARCH model.

Owais Siddiqui
1 min read
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