隨著金融市場變動日益劇烈，傳統風險指標已逐漸難以完整捕捉潛在崩盤風險。本研究旨在探討來自加密貨幣市場與傳統避險資產的跨市場因子，是否具備預測美國股市崩盤風險的能力。具體而言，選取恐慌指數（VIX）、比特幣期貨波動率（BTCF_VOL）、金銀比變動量（∆GSR）與成交量變動率（VOR）為主要自變數，評估其對最大累積跌幅（CMAX）、報酬負偏態（NCSKEW）與報酬波動不對稱（DUVOL）三項崩盤風險指標的預測效果。
研究期間涵蓋2018年至2025年，共1,506筆交易日資料，採用多元線性迴歸模型進行實證分析。為檢驗模型穩健性，本研究進行兩項強韌性檢定：一為調整比特幣期貨波動率計算視窗（由7日改為5日），二為針對比特幣第三次至第四次減半週期（2020年5月至2024年4月）進行樣本切割，以評估事件期間的模型適用性。
實證結果顯示，比特幣期貨波動率（BTCF_VOL）在三項崩盤風險指標上皆具穩健且顯著的正向預測能力，展現其前瞻性與系統性風險預警價值。VIX與NCSKEW及DUVOL呈顯著正相關，能有效反映市場恐慌與左尾風險，與CMAX的負相關則顯示其對最大跌幅存在滯後解釋效果。相較之下，金銀比變動量與成交量變動率多數結果未達顯著，顯示其預測力可能受限於特定事件條件與樣本結構。此外，Plosser（1982）指出多數總體經濟變數往往具有非定態（nonstationary）特性，金銀比亦可能隨樣本區間或事件條件而呈現結構性不穩定，此特徵或為其預測效果缺乏一致性的原因之一。
本研究建議，未來可結合非線性計量模型、機器學習演算法與鏈上數據分析，強化對金融市場複雜結構、高度連動性及跨資產傳染效應之監測與預測能力。同時，也可持續優化風險衡量工具與樣本切割設計，進一步提升模型在極端市況與動態變化下的外部適用性與預警效度。

This study evaluates the predictive power of key cross-market risk indicators—including the CBOE Volatility Index (VIX), Bitcoin futures realized volatility (BTCF_VOL), gold-silver ratio (∆GSR), and S&P 500 turnover oscillation rate (VOR)—for U.S. stock market crash risk. Using multivariate linear regression and 1,506 daily observations (2018–2025), crash risk is proxied by cumulative maximum drawdown (CMAX), negative conditional skewness (NCSKEW), and down-to-up volatility ratio (DUVOL). Robustness is assessed by adjusting volatility windows and analyzing event-driven subsamples like the Bitcoin halving cycle.
Results indicate BTCF_VOL is a robust, forward-looking indicator, with significant positive associations across all crash risk measures. VIX is positively related to NCSKEW and DUVOL, capturing investor fear and left-tail risk, while its negative link with CMAX suggests a lagged response to losses. ∆GSR and VOR lack consistent predictive power, with their utility mainly event-driven.
These findings highlight the value of integrating crypto-asset volatility with conventional indicators in crash risk warning systems. Future research should adopt nonlinear models, machine learning, and on-chain analytics to further enhance predictive accuracy and systemic risk monitoring, especially in volatile or rapidly changing markets.

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