用多层模型分析加密资产流动性溢出，预测市场风险。
A Multi-Layer Machine Learning and Econometric Pipeline for Forecasting Market Risk: Evidence from Cryptoasset Liquidity Spillovers
- 构建三层统计框架，融合流动性、波动率与收益关系
- 实证发现多资产间存在显著格兰杰因果关系，预测精度中等
- 适合量化风控、加密市场研究者参考
我们研究核心加密资产的流动性与波动率代理变量是否产生溢出效应，从而预测整体市场风险。实证框架包含三层：(A) 核心流动性与收益间的交互作用，(B) 通过主成分分析关联流动性与收益，(C) 捕捉跨资产波动率聚集的因子投影。分析结合向量自回归脉冲响应与方差分解（见Granger 1969；Sims 1980）、含外生变量的异质自回归模型（HAR-X, Corsi 2009），以及基于时间划分、早停、仅验证集阈值化和SHAP解释的抗泄漏机器学习协议。使用2021至2025年每日数据（共1462个观测值，涵盖74个资产），发现各层间存在统计显著的格兰杰因果关系，且具备中等水平的样本外预测能力。报告关键图表包括流程图、第A层热力图、第C层稳健性分析、向量自回归方差分解结果及测试集精确率-召回率曲线。完整数据与图表输出已上传至成果仓库。
原文摘要 · Abstract (English)
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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