Jumps in High-Frequency Data: Spurious Detections, Dynamics, and News

Applying tests for jumps to financial data sets can lead to an important number of spurious detections. Bursts of volatility are often incorrectly identified as jumps when the sampling is too sparse. At a higher frequency, methods robust to microstructure noise are required. We argue that whatever the jump detection test and the sampling frequency, a large number of spurious detections remain because of multiple testing issues. We propose a formal treatment based on an explicit thresholding on available test statistics. We prove that our method eliminates asymptotically all remaining spurious detections. In Dow Jones stocks between 2006 and 2008, spurious detections can represent up to 90% of the jumps detected initially. For the stocks considered, jumps are rare events, they do not cluster in time, and no cojump affects all stocks simultaneously, suggesting jump risk is diversifiable. We relate the remaining jumps to macroeconomic news, prescheduled company-specific announcements, and stories from news agencies which include a variety of unscheduled and uncategorized events. The vast majority of news do not cause jumps but may generate a market reaction in the form of bursts of volatility.

[1]  Francis X. Diebold,et al.  Real-Time Price Discovery in Global Stock, Bond and Foreign Exchange Markets , 2006 .

[2]  Torben G. Andersen,et al.  No-arbitrage semi-martingale restrictions for continuous-time volatility models subject to leverage effects, jumps and i.i.d. noise: Theory and testable distributional implications , 2007 .

[3]  F. Diebold,et al.  Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility , 2005, The Review of Economics and Statistics.

[4]  E. Lehmann.  The Fisher, Neyman-Pearson Theories of Testing Hypotheses: One Theory or Two? , 1993 .

[5]  Jean Jacod,et al.  Testing for Jumps in a Discretely Observed Process , 2007 .

[6]  A. Lo,et al.  THE ECONOMETRICS OF FINANCIAL MARKETS , 1996, Macroeconomic Dynamics.

[7]  Xin Huang,et al.  Macroeconomic News Announcements, Systemic Risk, Financial Market Volatility and Jumps , 2015 .

[8]  Azeem M. Shaikh,et al.  FORMALIZED DATA SNOOPING BASED ON GENERALIZED ERROR RATES , 2007, Econometric Theory.

[9]  Tim Bollerslev,et al.  Risk, Jumps, and Diversification , 2007 .

[10]  Jan Hannig,et al.  Detecting Jumps from Levy Jump Diffusion Processes , 2009 .

[11]  Dudley Gilder,et al.  An Empirical Investigation of Intraday Jumps and Cojumps in US Equities , 2009 .

[12]  N. Shephard,et al.  LIMIT THEOREMS FOR BIPOWER VARIATION IN FINANCIAL ECONOMETRICS , 2005, Econometric Theory.

[13]  Suzanne S. Lee,et al.  Jumps in Equilibrium Prices and Market Microstructure Noise , 2012 .

[14]  A. Kyle,et al.  The Flash Crash: The Impact of High Frequency Trading on an Electronic Market , 2011 .

[15]  Giovanni Urga,et al.  Identifying Jumps in Financial Assets: A Comparison Between Nonparametric Jump Tests , 2011 .

[16]  Andrew J. Patton,et al.  Does Beta Move with News? Firm-Specific Information Flows and Learning About Profitability , 2012 .

[17]  R. C. Merton,et al.  Option pricing when underlying stock returns are discontinuous , 1976 .

[18]  Jianqing Fan,et al.  Multi-Scale Jump and Volatility Analysis for High-Frequency Financial Data , 2006 .

[19]  Ernst Schaumburg,et al.  Federal Reserve Bank of New York Staff Reports Jump-robust Volatility Estimation Using Nearest Neighbor Truncation Jump-robust Volatility Estimation Using Nearest Neighbor Truncation , 2010 .

[20]  Cecilia Mancini,et al.  Non‐parametric Threshold Estimation for Models with Stochastic Diffusion Coefficient and Jumps , 2006, math/0607378.

[21]  Mikael Petitjean,et al.  Trading Activity, Realized Volatility and Jumps , 2009 .

[22]  George Tauchen,et al.  Cross-Stock Comparisons of the Relative Contribution of Jumps to Total Price Variance , 2012 .

[23]  Yacine Ait-Sahalia,et al.  How to Stop a Herd of Running Bears? Market Response to Policy Initiatives During the Global Financial Crisis , 2009, SSRN Electronic Journal.

[24]  Jean Jacod,et al.  Testing for Common Arrivals of Jumps for Discretely Observed Multidimensional Processes , 2009 .

[25]  Mark Podolskij,et al.  Bipower-Type Estimation in a Noisy Diffusion Setting , 2008 .

[26]  Around-the-Clock Media Coverage and the Timing of Earnings Announcements , 2005 .

[27]  R. Gencay,et al.  An Introduc-tion to High-Frequency Finance , 2001 .

[28]  A. Gallant,et al.  Alternative models for stock price dynamics , 2003 .

[29]  S. Holm.  A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[30]  Jumps in high frequency data , 2011 .

[31]  P. Mykland,et al.  Jumps in Financial Markets: A New Nonparametric Test and Jump Dynamics , 2008 .

[32]  Tim Bollerslev,et al.  Tails, Fears and Risk Premia , 2009 .

[33]  Pierre Bajgrowicz,et al.  Technical Trading Revisited: False Discoveries, Persistence Tests, and Transaction Costs , 2011 .

[34]  Xin Huang.  Macroeconomic News Announcements, Financial Market Volatility and Jumps , 2007 .

[35]  Mark Podolskij,et al.  Fact or Friction: Jumps at Ultra High Frequency , 2014 .

[36]  N. Shephard,et al.  Realized Kernels in Practice: Trades and Quotes , 2009 .

[37]  Francis X. Diebold,et al.  Real-Time Price Discovery in Global Stock, Bond and Foreign Exchange Markets , 2006 .

[38]  N. Shephard,et al.  Power and bipower variation with stochastic volatility and jumps , 2003 .

[39]  Christopher J. Neely,et al.  Jumps, Cojumps and Macro Announcements , 2009 .

[40]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[41]  L. Ederington,et al.  How Markets Process Information: News Releases and Volatility , 1993 .

[42]  Sofus A. Macskassy,et al.  More than Words: Quantifying Language to Measure Firms' Fundamentals the Authors Are Grateful for Assiduous Research Assistance from Jie Cao and Shuming Liu. We Appreciate Helpful Comments From , 2007 .

[43]  L. Smith,et al.  Empirical Evidence on Jumps in the Term Structure of the US Treasury Market . , 2008 .

[44]  P. Hansen,et al.  Realized Variance and Market Microstructure Noise , 2005 .

[45]  I. Johnstone,et al.  Adapting to unknown sparsity by controlling the false discovery rate , 2005, math/0505374.

[46]  Yacine Aït-Sahalia,et al.  Disentangling diffusion from jumps , 2004 .

[47]  A. Mood.  The Distribution Theory of Runs , 1940 .

[48]  L. Ederington,et al.  The Creation and Resolution of Market Uncertainty: The Impact of Information Releases on Implied Volatility , 1996, Journal of Financial and Quantitative Analysis.

[49]  Campbell R. Harvey,et al.  . . . And the Cross-Section of Expected Returns , 2014 .

[50]  Ion Grama.  On moderate deviations for martingales , 1997 .

[51]  P. Carr,et al.  What Type of Process Underlies Options? A Simple Robust Test , 2003 .

[52]  R. Oomen,et al.  Testing for Jumps When Asset Prices are Observed with Noise - A Swap Variance Approach , 2007 .

[53]  E. Fama.  The Behavior of Stock-Market Prices , 1965 .

[54]  Nicholas G. Polson,et al.  The Impact of Jumps in Volatility and Returns , 2000 .

[55]  Suzanne S. Lee,et al.  Jumps and Information Flow in Financial Markets , 2011 .

[56]  J. Poterba,et al.  What moves stock prices? , 1988 .

[57]  Francis X. Diebold,et al.  Modeling and Forecasting Realized Volatility , 2001 .

[58]  O. Scaillet,et al.  False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas , 2005 .

[59]  N. Shephard,et al.  Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation , 2005 .

[60]  Chenchuramaiah T. Bathala.  Giving Content to Investor Sentiment: The Role of Media in the Stock Market , 2007 .