investment viewpoints

Do volatility forecasts benefit from range-based measures?

Do volatility forecasts benefit from range-based measures?
François Chareyron - Portfolio Manager

François Chareyron

Portfolio Manager
Julien Royer, PhD - Quantitative Analyst, Multi Asset

Julien Royer, PhD

Quantitative Analyst, Multi Asset
Gabriele Grignani - Quantitative Analyst Intern

Gabriele Grignani

Quantitative Analyst Intern

MARS, our Multi-Asset Research Series, focuses on complex topics in the realm of multi asset to bring quantitative investment research back to Earth. In this second issue, we continue to explore range-based volatility measures and consider their advantages to improve volatility forecasts versus other estimators.


Key areas covered include:

  • Risk-based portfolio construction is directly linked to the ability to build forecasts of volatility and translate them into investment rules. Are range-based measures useful in generating more accurate volatility forecasts?
  • We begin by looking at how to incorporate range-based measures into standard volatility models to generate forecasts, considering different approaches and specifications
  • Choosing the scoring function to compute the loss series of the forecasts is key when the true process is unobservable. This notably enables the comparison of different volatility models, highlighting the additional information carried by intraday data when forecasting daily volatility
  • Since most portfolio constructions require forecasting farther than one day ahead, we explore how range-based exogenous variables can help build more accurate forecasts for longer horizons
  • Concluding that range-based volatility measures provide a significant edge to forecasting, we acknowledge that assessing the extent of that edge is more complex. Further areas of research will address the economic value of enhanced volatility forecasts in the context of risk-based portfolios


For more, please read the second issue of MARS.

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