arXiv 22 Oct 2025 · Machine Learning
arXiv:2510.20066 · PDF · DOI · OpenAlex · Extracted main text
We study whether liquidity and volatility proxies of a core set of cryptoassets generate spillovers that forecast market-wide risk. Our empirical framework integrates three statistical layers: (A) interactions between core liquidity and returns, (B) principal-component relations linking liquidity and returns, and (C) volatility-factor projections that capture cross-sectional volatility crowding. The analysis is complemented by vector autoregression impulse responses and forecast error variance decompositions (see Granger 1969; Sims 1980), heterogeneous autoregressive models with exogenous regressors (HAR-X, Corsi 2009), and a leakage-safe machine learning protocol using temporal splits, early stopping, validation-only thresholding, and SHAP-based interpretation. Using daily data from 2021 to 2025 (1462 observations across 74 assets), we document statistically significant Granger-causal relationships across layers and moderate out-of-sample predictive accuracy. We report the most informative figures, including the pipeline overview, Layer A heatmap, Layer C robustness analysis, vector autoregression variance decompositions, and the test-set precision-recall curve. Full data and figure outputs are provided in the artifact repository.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Yakov Amihud (2002) Illiquidity and Stock Returns: Cross-section and Time-series Effects | 0.843 | 3 | 3 | 100% |
| 2 | Fulvio Corsi (2009) A Simple Approximate Long-Memory Model of Realized Volatility | 0.843 | 3 | 3 | 100% |
| 3 | C. W. J. Granger (1969) Investigating Causal Relations by Econometric Models and Cross-spectral Methods | 0.843 | 3 | 3 | 100% |
| 4 | Christopher A. Sims (1980) Macroeconomics and Reality | 0.843 | 3 | 3 | 100% |
| 5 | Tianqi Chen and Carlos Guestrin (2016) XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery… | 0.644 | 2 | 2 | 100% |
| 6 | Scott M. Lundberg and Su-In Lee (2017) A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems. Curran Associates, I… | 0.644 | 2 | 2 | 100% |
| 7 | Ismail Adelopo and Xiaojun Luo (2025) Interconnectedness Among Cryptocurrencies and Financial Markets: A Systematic Literature Review | 0.405 | 1 | 1 | 100% |
| 8 | Jozef Barunḱ and Tomás Krehlḱ (2018) Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk | 0.405 | 1 | 1 | 100% |
| 9 | Tim Bollerslev (1986) Generalized Autoregressive Conditional Heteroskedasticity | 0.405 | 1 | 1 | 100% |
| 10 | Sergey Brin and Lawrence Page (1998) The Anatomy of a Large-scale Hypertextual Web Search Engine | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 18 scored citations.