Robust optimization of forecast combinations

Thierry Post, Selçuk Karabatı, Stelios Arvanitis

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


We develop a methodology for constructing robust combinations of time series forecast models which improve upon a given benchmark specification for all symmetric and convex loss functions. Under standard regularity conditions, the optimal forecast combination asymptotically almost surely dominates the benchmark, and also optimizes the chosen goal function. The optimum in a given sample can be found by solving a convex optimization problem. An application to the forecasting of changes in the S&P 500 volatility index shows that robust optimized combinations improve significantly upon the out-of-sample forecasting accuracy of both simple averaging and unrestricted optimization.

Original languageEnglish
Pages (from-to)910-926
Number of pages17
JournalInternational Journal of Forecasting
Issue number3
Publication statusPublished - Jul 1 2019


  • Asymptotic theory
  • Convex optimization
  • Forecast combinations
  • Stochastic dominance
  • Time series analysis
  • Volatility index forecasting

ASJC Scopus subject areas

  • Business and International Management

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