Recursive Estimation of the Exponentially Weighted Moving Average Model

24 Pages Posted: 26 Jul 2019

See all articles by Radek Hendrych

Radek Hendrych

Charles University in Prague

Tomas Cipra

Charles University of Prague

Date Written: July 26, 2019

Abstract

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.

Keywords: exponentially weighted moving average (EWMA) model, predictions, realized volatility, recursive estimation, RiskMetrics

Suggested Citation

Hendrych, Radek and Cipra, Tomas, Recursive Estimation of the Exponentially Weighted Moving Average Model (July 26, 2019). Journal of Risk, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3427108

Radek Hendrych (Contact Author)

Charles University in Prague ( email )

Celetná 13
Praha 1, 116 36
Czech Republic

Tomas Cipra

Charles University of Prague ( email )

Sokolovska 83
Prague, 186 75
Czech Republic

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