Lasso-Based Forecast Combinations for Forecasting Realized Variances

24 Pages Posted: 22 Nov 2016

See all articles by Ines Wilms

Ines Wilms

KU Leuven - Department of Applied Economics

Jeroen Rombouts

ESSEC Business School

Christophe Croux

KU Leuven - Faculty of Business and Economics (FEB)

Date Written: 2016

Abstract

Volatility forecasts are key inputs in financial analysis. While lasso based forecasts have shown to perform well in many applications, their use to obtain volatility forecasts has not yet received much attention in the literature. Lasso estimators produce parsimonious forecast models. Our forecast combination approach hedges against the risk of selecting a wrong degree of model parsimony. Apart from the standard lasso, we consider several lasso extensions that account for the dynamic nature of the forecast model. We apply forecast combined lasso estimators in a comprehensive forecasting exercise using realized variance time series of ten major international stock market indices. We find the lasso extended “ordered lasso” to give the most accurate realized variance forecasts. Multivariate forecast models, accounting for volatility spillovers between different stock markets, outperform univariate forecast models for longer forecast horizons.

Keywords: Forecast combination, Hierarchical lasso, Lasso, Ordered Lasso, Realized variance, Volatility forecasting

Suggested Citation

Wilms, Ines and Rombouts, Jeroen and Croux, Christophe, Lasso-Based Forecast Combinations for Forecasting Realized Variances (2016). Available at SSRN: https://ssrn.com/abstract=2873354 or http://dx.doi.org/10.2139/ssrn.2873354

Ines Wilms (Contact Author)

KU Leuven - Department of Applied Economics ( email )

Leuven, B-3000
BELGIUM

Jeroen Rombouts

ESSEC Business School ( email )

3 Avenue Bernard Hirsch
CS 50105 CERGY
CERGY, CERGY PONTOISE CEDEX 95021
France

Christophe Croux

KU Leuven - Faculty of Business and Economics (FEB) ( email )

Naamsestraat 69
Leuven, B-3000
Belgium

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