Characterizing financial crises using high-frequency data
提出一个新统计量，用于捕捉价格序列组成中的大幅不连续性，蒙特卡洛模拟表明该统计量能刻画不同样本期的尾部行为，应用于美国国债市场发现压力期存在“逃向现金”行为。
Recent advances in high-frequency financial econometrics enable us to characterize which components of the data generating processes change in crisis, and which do not. This paper introduces a new statistic which captures large discontinuities in the composition of a given price series. Monte Carlo simulations suggest that this statistic is useful in characterizing the tail behavior across different sample periods. An application to US Treasury market provides evidence consistent with identifying periods of stress via flight-to-cash behavior which results in increased abrupt price falls at the short end of the term structure and decreased negative price jumps at the long end.