Scholars publish research paper on early pricing framework for computing power assets
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Federico M. Bandi and Yinan Su published a study on the early pricing framework for computing power assets on arXiv on October 22, 2026, noting that current computing power capital expenditures and service flows have exceeded 1% of US GDP and are growing rapidly, with computing power prices reflecting uncertainty about the adoption of AI applications. The study proposes that the non-storable nature of computing power invalidates the direct no-arbitrage relationship between futures and spot prices, and that synthetic futures prices constructed based on existing term lease contracts are likely to be the upper bound of true futures prices; preliminary evidence indicates a positive computing power risk premium, suggesting hedging pressure on the supply side of computing power.
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- arXiv q-fin(Early) AI Compute Asset Pricing Research
This study proposes an early asset pricing framework for compute, pointing out that compute capital expenditures and service flows have exceeded 1% of US GDP and are growing rapidly, with compute prices reflecting the uncertainty of AI application adoption. The non-storable nature of compute invalidates the direct no-arbitrage relationship between futures and spot prices, and synthetic futures prices constructed based on existing term lease contracts are most likely an upper bound for true futures prices. Preliminary evidence from the study shows a positive compute risk premium, indicating hedging pressure on the supply side of compute.
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