Direct Versus Iterated Multi-Period Volatility Forecasts: Why MIDAS Is King

34 Pages Posted: 31 Jan 2019

See all articles by Eric Ghysels

Eric Ghysels

University of North Carolina Kenan-Flagler Business School; University of North Carolina (UNC) at Chapel Hill - Department of Economics

Alberto Plazzi

Swiss Finance Institute; USI Lugano

Rossen I. Valkanov

University of California, San Diego (UCSD) - Rady School of Management

Antonio Rubia Serrano

University of Alicante, Department of Financial Economics; University of California, Los Angeles (UCLA) - Finance Area

Asad Dossani

Colorado State University, Fort Collins - College of Business

Date Written: January 11, 2019

Abstract

Multi-period-ahead forecasts of returns’ variance are used in most areas of applied finance where long horizon measures of risk are necessary. Yet, the major focus in the variance forecasting literature has been on one-period-ahead forecasts. In this paper, we compare several approaches of producing multi-period-ahead forecasts within the GARCH and RV families – iterated, direct, and scaled short-horizon forecasts. We also consider the newer class of mixed data sampling (MIDAS) methods. We carry the comparison on 30 assets, comprising of equity, Treasury, currency, and commodity indices. While the underlying data is available at high-frequency (5-minutes), we are interested at forecasting variances 5, 10, 22, 44, and 66 days ahead. The empirical analysis, which is carried in-sample and out-of-sample with data from 2005 to 2018, yields the following results. For GARCH, iterated GARCH dominates the direct GARCH approach. In the case of RV, the direct RV is preferred to the iterated RV. This dichotomy of results emphasizes the need for an approach that uses the richness of high-frequency data and, at the same time produces a direct forecast of the variance at the desired horizon, without iterating. The MIDAS is such an approach and, unsurprisingly, it yields the most precise forecasts of the variance, in and out-of-sample. More broadly, our study dispels the notion that volatility is not forecastable at long horizons and offers an approach that delivers accurate out-of-sample predictions.

Keywords: volatility forecasting, multi-period forecasts, mixed-data sampling

JEL Classification: G17, C53, C52, C22

Suggested Citation

Ghysels, Eric and Plazzi, Alberto and Valkanov, Rossen and Serrano, Antonio Rubia and Dossani, Asad, Direct Versus Iterated Multi-Period Volatility Forecasts: Why MIDAS Is King (January 11, 2019). Swiss Finance Institute Research Paper No. 19-02 (2019); Kenan Institute of Private Enterprise Research Paper No. 19-7. Available at SSRN: https://ssrn.com/abstract=3326606 or http://dx.doi.org/10.2139/ssrn.3326606

Eric Ghysels

University of North Carolina Kenan-Flagler Business School ( email )

Kenan-Flagler Business School
Chapel Hill, NC 27599-3490
United States

University of North Carolina (UNC) at Chapel Hill - Department of Economics ( email )

Gardner Hall, CB 3305
Chapel Hill, NC 27599
United States
919-966-5325 (Phone)
919-966-4986 (Fax)

HOME PAGE: http://www.unc.edu/~eghysels/

Alberto Plazzi (Contact Author)

Swiss Finance Institute ( email )

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

USI Lugano ( email )

Via Buffi 13
CH-6900 Lugano
Switzerland

HOME PAGE: http://www.people.usi.ch/plazzia/

Rossen Valkanov

University of California, San Diego (UCSD) - Rady School of Management ( email )

9500 Gilman Drive
Rady School of Management
La Jolla, CA 92093
United States
858-534-0898 (Phone)

Antonio Rubia Serrano

University of Alicante, Department of Financial Economics ( email )

Ctra. S. Vicente s/n
03690-S. Vicente del Raspeig
Alicante, San Vicente del Raspeig - Alicante 03690
Spain
(34) 965 903 621 (Phone)

University of California, Los Angeles (UCLA) - Finance Area ( email )

Los Angeles, CA 90095-1481
United States
(310) 825-7246 (Phone)

Asad Dossani

Colorado State University, Fort Collins - College of Business ( email )

Fort Collins, CO 80523
United States

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