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Toward Regime-Aware Risk Forecasts

Kevin Khang
The Journal of Portfolio Management April 2022, 48 (5) 49-70; DOI: https://doi.org/10.3905/jpm.2022.48.5.049
Kevin Khang
is a senior investment strategist with the Vanguard Group in Malvern, PA
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Abstract

Estimation lookback window is a key calibration parameter in industry-standard risk models. It determines how readily the model incorporates new data to form volatility forecasts. Most volatility environments can be characterized as either slow-moving or fast-moving, and no single calibration generates consistently reliable forecasts. A model’s strength in one environment is often the reason it is ill-suited for other environments. The potential impact of using a suboptimal model can be measured in real time with a cross-sectional dispersion of forecasts from different calibrations. This information can be used to inform timely, and disciplined, transitions between slow-moving and fast-moving models—the makings of regime-aware volatility forecasting.

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The Journal of Portfolio Management: 48 (5)
The Journal of Portfolio Management
Vol. 48, Issue 5
April 2022
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Toward Regime-Aware Risk Forecasts
Kevin Khang
The Journal of Portfolio Management Mar 2022, 48 (5) 49-70; DOI: 10.3905/jpm.2022.48.5.049

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Toward Regime-Aware Risk Forecasts
Kevin Khang
The Journal of Portfolio Management Mar 2022, 48 (5) 49-70; DOI: 10.3905/jpm.2022.48.5.049
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  • Article
    • Abstract
    • DIVERSITY OF ADAPTIVENESS SPECIFICATIONS IN RISK MODELS
    • ADAPTIVENESS AND FORECASTING EFFICACY
    • TOWARD REGIME-AWARE RISK FORECASTS
    • CONCLUSIONS
    • NOTES ON RISK
    • ACKNOWLEDGMENTS
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