Lasso-based forecast combinations for forecasting realized variances

Ines Wilms, Jeroen Rombouts and Christophe Croux

No 553087, Working Papers of Department of Decision Sciences and Information Management, Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven

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 (search for similar items in EconPapers)
Date: 2016-10
New Economics Papers: this item is included in nep-ecm, nep-for and nep-rmg
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Published in FEB Research Report KBI_1625

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Persistent link: https://EconPapers.repec.org/RePEc:ete:kbiper:553087

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