Journal of Risk

Recursive estimation of the exponentially weighted moving average model

Radek Hendrych and Tomáš Cipra

  • The (robust) recursive estimation algorithms suitable for the EWMA model are suggested.
  • The prediction ability of the proposed (robust) recursive estimation schemes is investigated.
  • The priorities of the introduced recursive estimation procedures are demonstrated.

The exponentially weighted moving average (EWMA) model is a particular modeling scheme, supported by RiskMetrics, that is capable of forecasting the current level of volatility of financial time series. It is designed to track changes in the conditional variance of financial returns by assigning exponentially decreasing weights to observed past squared measurements. The aim of this paper is twofold. First, it introduces two recursive estimation algorithms that are appropriate for the EWMA model. Both are derived by employing the general recursive prediction error scheme. Moreover, they represent a computationally effective alternative to already established nonrecursive estimation strategies since they are effective in terms of memory storage, computational complexity and detecting structural changes. Second, this paper investigates the prediction ability of the proposed recursive estimation schemes when compared with other common (nonrecursive) estimation methods. The priorities of the suggested recursive estimators are demonstrated by means of a simulation study and an extensive empirical case study of eighteen key world stock indexes. Combinations of recursive predictions are also studied. Such a strategy can be recommended due to its advantageous properties when predicting volatility.

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to View our subscription options

You need to sign in to use this feature. If you don’t have a account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here