Expected Revenue, Risk, and Grid Impact of Bitcoin Mining: A Decision-Theoretic Perspective
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917166806204416 |
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| author | Cai, Yuting Sadali, Ruthav Ray, Korok Tian, Chao |
| author_facet | Cai, Yuting Sadali, Ruthav Ray, Korok Tian, Chao |
| contents | Most current assessments use ex post proxies that miss uncertainty and fail to consistently capture the rapid change in bitcoin mining. We introduce a unified, ex ante statistical model that derives expected return, downside risk, and upside potential profit from the first principles of mining: Each hash is a Bernoulli trial with a Bitcoin block difficulty-based success probability. The model yields closed-form expected revenue per hash-rate unit, risk metrics in different scenarios, and upside-profit probabilities for different fleet sizes. Empirical calibration closely matches previously reported observations, yielding a unified, faithful quantification across hardware, pools, and operating conditions. This foundation enables more reliable analysis of mining impacts and behavior. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20518 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Expected Revenue, Risk, and Grid Impact of Bitcoin Mining: A Decision-Theoretic Perspective Cai, Yuting Sadali, Ruthav Ray, Korok Tian, Chao Computational Engineering, Finance, and Science Systems and Control Most current assessments use ex post proxies that miss uncertainty and fail to consistently capture the rapid change in bitcoin mining. We introduce a unified, ex ante statistical model that derives expected return, downside risk, and upside potential profit from the first principles of mining: Each hash is a Bernoulli trial with a Bitcoin block difficulty-based success probability. The model yields closed-form expected revenue per hash-rate unit, risk metrics in different scenarios, and upside-profit probabilities for different fleet sizes. Empirical calibration closely matches previously reported observations, yielding a unified, faithful quantification across hardware, pools, and operating conditions. This foundation enables more reliable analysis of mining impacts and behavior. |
| title | Expected Revenue, Risk, and Grid Impact of Bitcoin Mining: A Decision-Theoretic Perspective |
| topic | Computational Engineering, Finance, and Science Systems and Control |
| url | https://arxiv.org/abs/2512.20518 |