Multi-Period Forecasts of Volatility: Direct, Iterated, and Mixed-Data Approaches

22 Pages Posted: 17 Feb 2009

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

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

Multiple version iconThere are 2 versions of this paper

Date Written: February 16, 2009

Abstract

Multi-period forecasts of stock market return volatilities are often used in many applied areas of finance where long horizon measures of risk are necessary. Yet, very little is known about how to forecast variances several periods ahead, as most of the focus has been placed on one-period ahead forecasts. In this paper, we compare several approaches of producing multi-period ahead forecasts -iterated, direct, and mixed data sampling (MIDAS)- as alternatives to the often-used "scaling-up" method. The comparison is conducted (pseudo) out-of-sample using returns data of the US stock market portfolio and a cross section of size and book-to-market portfolios. The comparison results are surprisingly sharp. For the market, size, and book-to-market portfolios, we obtain the same precision ordering of the forecasting methods. The direct approach provides the worse (in MSFE sense) forecasts; it is dominated even by the naive "scaling-up" method. Iterated forecasts are suitable for shorter horizons (5 to 10 periods ahead), but their MSFEs deteriorate as the horizon increases. The MIDAS forecasts perform well at long horizons: they dominate all other approaches at horizons of 10-periods ahead and higher. The MIDAS forecasting advantage becomes most apparent at horizons of 30-periods ahead and longer. In sum, this study dispels the notion that volatility is not forecastable at long horizons and offers an approach that delivers accurate pseudo out-of-sample predictions.

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

JEL Classification: G17, C53, C52, C22

Suggested Citation

Ghysels, Eric and Valkanov, Rossen and Serrano, Antonio Rubia, Multi-Period Forecasts of Volatility: Direct, Iterated, and Mixed-Data Approaches (February 16, 2009). EFA 2009 Bergen Meetings Paper, Available at SSRN: https://ssrn.com/abstract=1344742 or http://dx.doi.org/10.2139/ssrn.1344742

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://https://eghysels.web.unc.edu/

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 (Contact Author)

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)

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