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

Bjoern Schulte-Tillmann, Mawuli Segnon and Bernd Wilfling

No 9922, CQE Working Papers from Center for Quantitative Economics (CQE), University of Muenster

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 conï¬ dence sets (search for similar items in EconPapers)
JEL-codes: C51 C53 C58 E44 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2022-06
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-mac and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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