Mini flash crashes: Review,taxonomy and policy responses

We focus on extreme price movements known as mini flash crashes (MFCs). After reviewing the literature, we provide a taxonomy based on a sample of MFCs identified by Nanex on the U.S. financial markets over a three-year period. We detect significant differences between crashes and exchanges. In comparison to ‘up crashes’, we find that ‘down crashes’ exhibit lower absolute returns but have longer duration. We also show that the dynamics of MCFs varies across exchanges. For example, the MFCs on ARCA are on average both less severe and shorter in duration than those on the NASDAQ. We finally review all the key implications of MCFs in terms of public policy.     

PREDICTING STOCK RETURNS AND VOLATILITY WITH INVESTOR SENTIMENT INDICES: A RECONSIDERATION USING A NONPARAMETRIC CAUSALITY‐IN‐QUANTILES TEST

Evidence of monthly stock returns predictability based on popular investor sentiment indices, namely S^BW and S^PLS as introduced by Baker and Wurgler (2006, 2007) and Huang et al. (2015) respectively are mixed. While, linear predictive models show that only S^PLS can predict excess stock returns, nonparametric models (which accounts for misspecification of the linear frameworks due to nonlinearity and regime changes) finds no evidence of predictability based on either of these two indices for not only stock returns, but also its volatility. However, in this paper, we show that when we use a more general nonparametric causality‐in‐quantiles model of Balcilar et al., (forthcoming), in fact, both S^BW and S^PLS can predict stock returns and its volatility, with S^PLS being a relatively stronger predictor of excess returns during bear and bull regimes, and S^BW being a relatively powerful predictor of volatility of excess stock returns, barring the median of the conditional distribution.     

Analysis of High Frequency Data in Finance: A Survey

This study examines the use of high frequency data in finance, including volatility estimation and jump tests. High frequency data allows the construction of model-free volatility measures for asset returns. Realized variance is a consistent estimator of quadratic variation under mild regularity conditions. Other variation concepts, such as power variation and bipower variation, are useful and important for analyzing high frequency data when jumps are present. High frequency data can also be used to test jumps in asset prices. We discuss three jump tests: bipower variation test, power variation test, and variance swap test in this study. The presence of market microstructure noise complicates the analysis of high frequency data. The survey introduces several robust methods of volatility estimation and jump tests in the presence of market microstructure noise. Finally, some applications of jump tests in asset pricing are discussed in this article.