Zero-shot time series foundation models largely fail to beat econometric benchmarks for realized volatility forecasting, with only TTM achieving a narrow, calibration-driven edge.
and Vorkink, Keith , title =
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Derives analytical expressions for Voigt distribution properties and introduces the GCC filter preserving Voigt prediction-error density under Masreliez approximation.
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Forecasting Realized Volatility with Time Series Foundation Models: A Comparison with Econometric Benchmarks
Zero-shot time series foundation models largely fail to beat econometric benchmarks for realized volatility forecasting, with only TTM achieving a narrow, calibration-driven edge.
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Exact Likelihood Inference and Robust Filtering for Gauss-Cauchy Convolution Models
Derives analytical expressions for Voigt distribution properties and introduces the GCC filter preserving Voigt prediction-error density under Masreliez approximation.