Abstract
This study introduces a ratio-based connectedness measure for mixed-frequency settings. We prove the existence of a systematic bias that overestimates the influence of high-frequency variables in the existing transformed connectedness measure for mixed-frequency data. Our metric integrates a modified forecast error variance decomposition and a ratio-based structure. We prove, and provide simulation evidence, that our measure effectively mitigates the bias present in the transformed connectedness approach. Using a mixed-frequency vector error correction model, our empirical analysis employs monthly climate data and daily commodity futures prices over the 2000-2024 period. Results show that climate variables act as significant net shock transmitters to commodity prices.
| Original language | English |
|---|---|
| Article number | nbag016 |
| Pages (from-to) | 1-58 |
| Number of pages | 58 |
| Journal | Journal of Financial Econometrics |
| Volume | 24 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2026 |
Bibliographical note
© The Author(s) 2026. Published by Oxford University Press. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.Keywords
- mixed frequency
- connectedness
- climate risk
- commodity markets
- vector error correction model
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