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The exponentially weighted moving average (EMWA) could be labeled as acompetitive volatility estimator, where its main strength relies on computationsimplicity, especially in a multi-asset scenario, due to dependency only on thedecay parameter, $\lambda$. But, what is the best election for $\lambda$ in theEMWA volatility model? Through a large time-series data set of historicalreturns of the top US large-cap companies; we test empirically the forecastingperformance of the EWMA approach, under different time horizons and varying thedecay parameter. Using a rolling window scheme, the out-of-sample performanceof the variance-covariance matrix is computed following two approaches. First,if we look for a fixed decay parameter for the full sample, the results are inagreement with the RiskMetrics suggestion for 1-month forecasting. In addition,we provide the full-sample optimal decay parameter for the weekly and bi-weeklyforecasting horizon cases, confirming two facts: i) the optimal value is as afunction of the forecasting horizon, and ii) for lower forecasting horizons theshort-term memory gains importance. In a second way, we also evaluate theforecasting performance of EWMA, but this time using the optimal time-varyingdecay parameter which minimizes the in-sample variance-covariance estimator,arriving at better accuracy than the use of a fixed-full-sample optimalparameter.

Asset volatility forecasting:The optimal decay parameter in the EWMA model