Paper: arXiv 2503.12648

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.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced statistical and machine learning techniques (transfer learning, DTW, neural networks) and includes rigorous empirical testing with specific datasets, forecasting horizons, and comparative model evaluations.

Research Flowchart

  flowchart TD
  A["Research Goal<br/>Forecast RV for New Issues/Spin-Offs<br/>with limited data"] --> B["Input Data<br/>Source Assets: Extensive RV history<br/>Target Assets: Limited RV history"]
  
  B --> C["Methodology<br/>Multi-Source Transfer Learning"]
  
  C --> D{"Select Similar Instances<br/>Find historical periods in source data<br/>matching target asset characteristics"}
  
  D --> E["Model Estimation<br/>Train Linear & Non-linear RV models<br/>on combined data"]
  
  E --> F["Computational Process<br/>Forecast Volatility<br/>vs Baselines: Target-only & Full-source models"]
  
  F --> G["Key Findings<br/>Transfer learning outperforms alternatives<br/>Beneficial immediately after trading day 1"]

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0 of 75 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.