Checkmate philosophers

A chance to rip apart my work, in which I argue AI may warrant moral consideration: www.academia.edu/s/7abfbc90e9...

Political legitimacy formalized as stakes-weighted consent alignment (α), enabling systematic comparison across democratic, technocratic, and algorithmic governance systems. My paper: doi.org/10.5281/zeno...

Submitted to SSRN for review! Expected: papers.ssrn.com/abstract=578..., next step would be to submit to the journal of Digital Finance.

Preprint: doi.org/10.5281/zenodo.17677682
Code: github.com/studiofarzul...

Paper + code + data, CC-BY-4.0/MIT.

Methods: TARCH-X quasi-MLE, Bayesian (BF>10 for 4/6 assets), k-means clustering, VAR network connectedness, regime-switching, bootstrap (10k). 50 events, 6 cryptos, 2019-2025.

Sentiment evidence: TARCH-X with GDELT achieves superior AIC (5/6 assets) despite weekly aggregation and 100% negative raw bias. Markets so sentiment-driven even noisy proxies capture behavior.

Near-integrated volatility: All six assets hit stationarity boundaries (α+β≈0.999), half-lives >100 days vs 5-20 in equities. Crypto volatility shocks are quasi-permanent.

Crisis amplification: Infrastructure effects multiply 5× during stress periods (COVID-19, FTX collapse), explaining 67% of extreme volatility despite 18% sample coverage.

Network topology surprise: ETH is the primary systemic risk driver (eigenvector centrality 0.89) over BTC (0.71). Spillover analysis shows ETH is the volatility transmission hub.

Mechanism: decentralized protocols enable jurisdictional arbitrage (regulations are geographically constrained), while infrastructure failures cascade globally with no escape route.

New paper: Infrastructure events generate 5.7× larger volatility impacts than regulatory events in crypto markets (2.385% vs 0.419%, p=0.0008, d=2.753).

Inverts traditional finance where regulatory uncertainty dominates.