ASRI: An Aggregated Systemic Risk Index for Cryptocurrency Markets
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866912905857859584 |
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| author | Farzulla, Murad Maksakov, Andrew |
| author_facet | Farzulla, Murad Maksakov, Andrew |
| contents | We introduce the Aggregated Systemic Risk Index (ASRI), comprising four weighted sub-indices: Stablecoin Concentration Risk (30%), DeFi Liquidity Risk (25%), Contagion Risk (25%), and Regulatory Opacity Risk (20%). Using data from DeFi Llama, Federal Reserve FRED, and on-chain analytics, we validate against four historical crises (Terra/Luna, Celsius/3AC, FTX, SVB). Event study analysis detects significant abnormal signals for all four (t-statistics 5.47-32.64, p < 0.01), with threshold-based detection achieving 30-day average lead time for three of four events. Walk-forward validation confirms 4/4 out-of-sample detection (18-day average lead), ruling out look-ahead bias. A three-regime HMM identifies distinct risk states with >97% persistence; structural stability tests pass (Chow p = 0.993). Benchmarking against Diebold-Yilmaz connectedness shows equivalent detection (75%) with higher precision (33.5% vs. 22.4%). Out-of-sample specificity testing on 2024-2025 data confirms zero false positives, correctly classifying the $1.5B Bybit hack as non-systemic. ASRI captures DeFi-specific vulnerabilities -- composability risk, flash loan exposure, and tokenized RWA linkages -- that SRISK and CoVaR cannot accommodate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_03874 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ASRI: An Aggregated Systemic Risk Index for Cryptocurrency Markets Farzulla, Murad Maksakov, Andrew Risk Management Computational Finance 91G40, 91G70, 91B84, 62M10 J.4; G.3; H.4.2 We introduce the Aggregated Systemic Risk Index (ASRI), comprising four weighted sub-indices: Stablecoin Concentration Risk (30%), DeFi Liquidity Risk (25%), Contagion Risk (25%), and Regulatory Opacity Risk (20%). Using data from DeFi Llama, Federal Reserve FRED, and on-chain analytics, we validate against four historical crises (Terra/Luna, Celsius/3AC, FTX, SVB). Event study analysis detects significant abnormal signals for all four (t-statistics 5.47-32.64, p < 0.01), with threshold-based detection achieving 30-day average lead time for three of four events. Walk-forward validation confirms 4/4 out-of-sample detection (18-day average lead), ruling out look-ahead bias. A three-regime HMM identifies distinct risk states with >97% persistence; structural stability tests pass (Chow p = 0.993). Benchmarking against Diebold-Yilmaz connectedness shows equivalent detection (75%) with higher precision (33.5% vs. 22.4%). Out-of-sample specificity testing on 2024-2025 data confirms zero false positives, correctly classifying the $1.5B Bybit hack as non-systemic. ASRI captures DeFi-specific vulnerabilities -- composability risk, flash loan exposure, and tokenized RWA linkages -- that SRISK and CoVaR cannot accommodate. |
| title | ASRI: An Aggregated Systemic Risk Index for Cryptocurrency Markets |
| topic | Risk Management Computational Finance 91G40, 91G70, 91B84, 62M10 J.4; G.3; H.4.2 |
| url | https://arxiv.org/abs/2602.03874 |