Multiplicative Error Models: 20 years on

TL;DR

This paper reviews the development and application of Multiplicative Error Models (MEMs) over 20 years, highlighting their effectiveness in modeling positive-valued financial time series and discussing advancements in model specification and inference.

Contribution

It provides a comprehensive overview of MEMs, including recent developments in multivariate modeling and the impact of low-frequency components on model performance.

Findings

MEMs produce accurate forecasts of positive-valued financial data.

Inclusion of low-frequency components improves model properties.

Multivariate MEMs extend univariate approaches effectively.

Abstract

Several phenomena are available representing market activity: volumes, number of trades, durations between trades or quotes, volatility - however measured - all share the feature to be represented as positive valued time series. When modeled, persistence in their behavior and reaction to new information suggested to adopt an autoregressive-type framework. The Multiplicative Error Model (MEM) is borne of an extension of the popular GARCH approach for modeling and forecasting conditional volatility of asset returns. It is obtained by multiplicatively combining the conditional expectation of a process (deterministically dependent upon an information set at a previous time period) with a random disturbance representing unpredictable news: MEMs have proved to parsimoniously achieve their task of producing good performing forecasts. In this paper we discuss various aspects of model specification and inference both for the univariate and the multivariate case. The applications are illustrative examples of how the presence of a slow moving low-frequency component can improve the properties of the estimated models.