Volatility forecasting: combinations of realized volatility measures and forecasting models

Linlan Xiao, Vigdis Boasson, Sergey Shishlenin, Victoria Makushina

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


This article examines financial time series volatility forecasting performance. Different from other studies which either focus on combining individual realized measures or combining forecasting models, we consider both. Specifically, we construct nine important individual realized measures and consider combinations including the mean, the median and the geometric means as well as an optimal combination. We also apply a simple AR(1) model, an SV model with contemporaneous dependence, an HAR model and three linear combinations of these models. Using the robust forecasting evaluation measures including RMSE and QLIKE, our empirical evidence from both equity market indices and exchange rates suggests that combinations of both volatility measures and forecasting models improve the forecast performance significantly.

Original languageEnglish
Pages (from-to)1428-1441
Number of pages14
JournalApplied Economics
Issue number13
StatePublished - Mar 16 2018


  • Volatility forecasting
  • combination
  • forecasting models
  • robust evaluation
  • volatility measures


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