THE JOURNAL OF FINANCIAL ECONOMICS· VOL.123, ISSUE.1 · JANUARY 2017

新聞的隐含波動率和對災難的憂慮

作者：Asaf Manela (Washington University), Alan Moreira (Yale University)

摘要：我們使用華爾街日報1890年至今的頭版文章，構建了一個從文本上測度不确定性的度量指标。新聞的隐含波動率（NVIX）在股市崩盤時期、政策不确定時期、世界大戰時期和金融危機時期達到峰值。根據美國戰後時期的數據，在度過了高隐含波動率（NVIX）的嚴冬後，迎接人們的是股票回報率高于平均水平的時期，即使控制了同期的和預測性的股市波動率之後也依然顯著。關于戰争和政策的新聞報道能夠解釋該指标中風險溢價中的大部分時間變化性。1890年—2009年的樣本區間包括了大蕭條時期和兩次世界大戰時期，顯示出高隐含波動率能夠預測未來正常時期的高回報率和預測經濟災難的到來。本文的論證與近期的理論一緻，該理論強調罕見災難風險的時間變化性是資産價格波動的加總。

關鍵字：文本分析，隐含波動率，罕見災難，股權溢價，收益的可預測性，機器學習

News Implied Volatility and Disaster Concerns

Asaf Manela (Washington University), Alan Moreira (Yale University)

ABSTRACT

We construct a text-based measure of uncertainty starting in 1890 using front-page articles of the Wall Street Journal. News implied volatility (NVIX) peaks during stock market crashes, times of policy-related uncertainty, world wars, and financial crises. In US postwar data, periods when NVIX is high are followed by periods of above average stock returns, even after controlling for contemporaneous and forward-looking measures of stock market volatility. News coverage related to wars and government policy explains most of the time variation in risk premia our measure identifies. Over the longer 1890–2009 sample that includes the Great Depression and two world wars, high NVIX predicts high future returns in normal times and rises just before transitions into economic disasters. The evidence is consistent with recent theories emphasizing time variation in rare disaster risk as a source of aggregate asset prices fluctuations.

Keywords: Text-based analysis, Implied volatility, Rare disasters, Equity premium, Return predictability, Machine learning

原文鍊接：

http://www.sciencedirect.com/science/article/pii/S0304405X16301751

翻譯：吳雨玲