傳統對於傳染的定義為危機期間跨國股權市場的相關性顯著增加。眾所皆知，相關不代表因果，我們認為股權市場之間的相關性增加，並不會給出任何傳染方向的資訊。比較許多關於傳染辯論的文章，我們使用因子模型並將傳染定義在無法解釋的因子負荷量與殘差的因果關係增加，藉此探討 2007 年次貸危機與 2019 年新型冠狀病毒 (COVID-19) 期間美國股市崩盤對跨國股市的傳染性。
本論文的主要貢獻如下：首先，我們使用 ETF 來代表各國的股權市場，同時我們的結果可以明確的檢定國際多樣化在危機期間減少投資組合風險的有效性。其次，傳統衡量時間序列因果關係的迴歸分析普遍存在維度詛咒 (curse of dimensionality)，我們使用 Runge (2020) 提出基於條件獨立的因果網路發現方法，作為第一個結合計量經濟學與近幾年很熱門的因果推論科學 (science of causal inference) 的跨領域應用。除了解決傳統計量經濟學在大數據時代所面臨的問題，同時可以很好的適應於複雜時間序列中特定落後期 (lag-specific) 與當期 (contemporaneous) 的因果網路發現。最後，我們設計線性迴歸的傳染性檢定模型，可適用於不同的全球危機，同時考慮波動傳遞的時變性 (time-varying) 可提供未來傳染性週期的研究。
實證結果表明美國股市在次貸危機期間，對瑞典、比利時、奧地利、墨西哥與巴西具有傳染性，而在 COVID-19 期間，則是對日本、瑞典、瑞士與法國具有傳染性，但以上傳染效果都不大。但由於我們使用因果關係而不是相關性，本文結果可額外提供美國對跨國的傳染方向。

This paper utilizes the definition of contagion as a significant increase in the “causality” rather than the “correlation” of cross-market co-movement during a crisis. We adopt the causal network discovery method based on conditional independence, PCMCI+, proposed by Runge (2020) to estimate the causality of the U.S. on global equity markets to discuss the U.S contagion of the financial crisis in 2007 and that of the COVID-19 in 2019. The empirical results show that the U.S. has a significant but modest contagion effect on the equity markets of Sweden, Belgium, Austria, Mexico, and Brazil respectively during the financial crisis in 2007 and on those of Japan, Sweden, Switzerland, and France during the COVID-19 in 2019. Additionally, this paper can determine the direction of contagion from the U.S. to global equity market with the usage of the causality rather than the correlation.

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