Incremental fine-tuning of the TimesFM foundation model improves one-day-ahead realized volatility forecasts and beats HAR, ARFIMA, CHAR, and RGARCH benchmarks on average losses across 21 global equity indices.
Garch based artificial neural networks in forecasting conditional variance of stock returns
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Foundation Time-Series AI Model for Realized Volatility Forecasting
Incremental fine-tuning of the TimesFM foundation model improves one-day-ahead realized volatility forecasts and beats HAR, ARFIMA, CHAR, and RGARCH benchmarks on average losses across 21 global equity indices.