Financial-market volatility prediction with multiplicative Markov-switching MIDAS components

Schulte-Tillmann, Björn; Segnon, Mawuli; Wilfling, Bernd

Abstract

We propose four multiplicative-component volatility MIDAS models to disentangle short- and long-term volatility sources. Three of our models specify short-term volatility as Markov-switching processes. We establish statistical properties, covariance-stationarity conditions, and an estimation framework using regime-switching filter techniques. A simulation study shows the robustness of the estimates against several mis-specifications. An out-of-sample forecasting analysis with daily S&P500 returns and quarterly-sampled (macro)economic variables yields two major results. (i) Specific long-term variables in the MIDAS models significantly improve forecast accuracy (over the non-MIDAS benchmarks). (ii) We robustly find superior performance of one Markov-switching MIDAS specification (among a set of competitor models) when using the 'Term structure' as the long-term variable.

Keywords
MIDAS volatility modeling; Hierarchical hidden Markov models; Markov-switching; Forecasting; Model confidence sets