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Realized volatility forecasting for new issues and spin-offs using multi-source transfer learning

Andreas Teller et al.

International Journal of Forecasting2026https://doi.org/10.1016/j.ijforecast.2026.02.004article
AJG 3ABDC A
Weight
0.50

Abstract

Forecasting the volatility of financial assets is essential for various financial applications. This paper addresses the challenging task of forecasting the volatility of financial assets with limited historical data, such as new issues or spin-offs, by proposing a multi-source transfer learning approach. Specifically, we exploit complementary source data of assets with a substantial historical data record by selecting source time series instances that are most similar to the limited target data of the new issue/spin-off. Based on these instances and the target data, we estimate linear and non-linear realized volatility models and compare their forecasting performance to forecasts of models trained exclusively on the target data, and models trained on the entire source and target data. The results show that our transfer learning approach outperforms the alternative models and that the integration of complementary data is also beneficial immediately after the initial trading day of the new issue/spin-off. • This study considers forecasting volatility of financial assets with limited data. • We propose a multi-source transfer learning (MTL) approach applying data-rich assets. • The approach is combined with linear and non-linear realized volatility models. • An extensive empirical analysis shows the superior forecasting performance of MTL. • Complementary data is beneficial after the first trading day of new issues/spin-offs.

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https://doi.org/https://doi.org/10.1016/j.ijforecast.2026.02.004

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@article{andreas2026,
  title        = {{Realized volatility forecasting for new issues and spin-offs using multi-source transfer learning}},
  author       = {Andreas Teller et al.},
  journal      = {International Journal of Forecasting},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.ijforecast.2026.02.004},
}

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0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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