本文探討股市波動性是否會引發股市進入熊市，是否可作為熊市的領先指標？以美國股價指數資料為研究對象，以Pagan & Sossounov（2003）無母數方法建構熊市序列，以realized volatility衡量股市波動性。因為熊市牛市序列是雙元變數的時間序列，故採用Probit模型進行估計。研究發現股市波動性確實含有預測未來股市熊市的資訊，可做為股市熊市的領先指標；大的波動性會引發股市進入熊市，且波動性對於短期的預測力優於長期的預測力。波動性對股市熊市的預測力，在樣本外也有很好的表現。模型強韌性檢測方面，本文考慮其他模型進行估計、子樣本、考慮解釋變數的自我相關性、加入其他對股市具有影響力的變數以及分析其他熊市定義，結果顯示股市波動性對未來熊市均具有顯著且很強烈的影響。

This paper investigates the predictability of U.S. bear markets using the volatility of stock return. Based on non-parametric Pagan-Sossounov (2003) approach for identifying bear markets and realized volatility, we find the volatility of stock market contain useful information about future bear market. Moreover, we find the high volatility will induce bear market. This variable also performs very well for out-of-sample examination. For robustness test, we examine the subsample, autocorrelations, with other predictors, and other definition of bear markets. We find the main results are also maintained.