Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility

Abstract Using estimators of the variation of positive and negative returns (realized semivariances) and high-frequency data for the S&P 500 Index and 105 individual stocks, this paper sheds new light on the predictability of equity price volatility.We showthat future volatility is more strongly related to the volatility of past negative returns than to that of positive returns and that the impact of a price jump on volatility depends on the sign of the jump, with negative (positive) jumps leading to higher (lower) future volatility. We show that models exploiting these findings lead to significantly better out-of-sample forecast performance.

[1]  Kevin Sheppard,et al.  Optimal combinations of realised volatility estimators , 2009 .

[2]  N. Shephard,et al.  Econometric Analysis of Realized Covariation: High Frequency Based Covariance, Regression, and Correlation in Financial Economics , 2004 .

[3]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[4]  M. Dacorogna,et al.  Volatilities of different time resolutions — Analyzing the dynamics of market components , 1997 .

[5]  M. Visser Forecasting S&P 500 Daily Volatility using a Proxy for Downward Price Pressure , 2008 .

[6]  A. Christie,et al.  The stochastic behavior of common stock variances: value , 1982 .

[7]  Markku Lanne Forecasting Realized Volatility by Decomposition , 2006 .

[8]  N. Shephard,et al.  Econometric analysis of realized volatility and its use in estimating stochastic volatility models , 2002 .

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

[10]  F. Diebold,et al.  VOLATILITY AND CORRELATION FORECASTING , 2006 .

[11]  Chris Kirby,et al.  The Economic Value of Volatility Timing Using 'Realized' Volatility , 2001 .

[12]  J. Zakoian Threshold heteroskedastic models , 1994 .

[13]  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 .

[14]  Pierre Bajgrowicz,et al.  Jumps in High-Frequency Data: Spurious Detections, Dynamics, and News , 2015, Manag. Sci..

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

[16]  W. Newey,et al.  A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelationconsistent Covariance Matrix , 1986 .

[17]  William W. Hogan,et al.  Toward the Development of an Equilibrium Capital-Market Model Based on Semivariance , 1974, Journal of Financial and Quantitative Analysis.

[18]  N. Shephard,et al.  Estimating quadratic variation using realized variance , 2002 .

[19]  F. Diebold,et al.  The Distribution of Realized Exchange Rate Volatility , 2000 .

[20]  P. Mykland,et al.  REALIZED VOLATILITY WHEN SAMPLING TIMES ARE POSSIBLY ENDOGENOUS , 2013, Econometric Theory.

[21]  Torben G. Andersen,et al.  Correcting the errors: Volatility forecast evaluation using high-frequency data and realized volatilities , 2005 .

[22]  E. Ghysels,et al.  Why Do Absolute Returns Predict Volatility So Well , 2006 .

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

[24]  F. Sortino,et al.  Managing downside risk in financial markets , 2001 .

[25]  T. Bollerslev,et al.  ANSWERING THE SKEPTICS: YES, STANDARD VOLATILITY MODELS DO PROVIDE ACCURATE FORECASTS* , 1998 .

[26]  Lan Zhang,et al.  A Tale of Two Time Scales , 2003 .

[27]  R. Engle New Frontiers for Arch Models , 2002 .

[28]  Thomas H. McCurdy,et al.  Do High-Frequency Measures of Volatility Improve Forecasts of Return Distributions? , 2008 .

[29]  Neil Shephard,et al.  Measuring Downside Risk - Realised Semivariance , 2008 .

[30]  W. Newey,et al.  A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelationconsistent Covariance Matrix , 1986 .

[31]  Robert F. Engle,et al.  Fitting Vast Dimensional Time-Varying Covariance Models , 2017, Journal of Business & Economic Statistics.

[32]  Federico M. Bandi,et al.  Microstructure Noise, Realized Variance, and Optimal Sampling , 2008 .

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

[34]  John Y. Campbell,et al.  No News is Good News: An Asymmetric Model of Changing Volatility in Stock Returns , 1991 .

[35]  R. Oomen Properties of Bias-Corrected Realized Variance Under Alternative Sampling Schemes , 2005 .

[36]  Alan L. Lewis,et al.  Semivariance and the Performance of Portfolios with Options , 1990 .

[37]  L. Glosten,et al.  On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks , 1993 .

[38]  Andrew J. Patton Data-based ranking of realised volatility estimators , 2011 .

[39]  Roberto Renò,et al.  Threshold Bipower Variation and the Impact of Jumps on Volatility Forecasting , 2008 .

[40]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[41]  Halbert White,et al.  Tests of Conditional Predictive Ability , 2003 .

[42]  Neil Shephard,et al.  Designing Realised Kernels to Measure the Ex-Post Variation of Equity Prices in the Presence of Noise , 2008 .

[43]  Eric Ghysels,et al.  News - Good or Bad - and its Impact on Volatility Predictions over Multiple Horizons , 2008 .

[44]  Markku Lanne,et al.  A Mixture Multiplicative Error Model for Realized Volatility , 2006 .

[45]  Fulvio Corsi,et al.  A Simple Approximate Long-Memory Model of Realized Volatility , 2008 .

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

[47]  Andrew Ang,et al.  Downside Risk , 2004 .

[48]  B. Christensen,et al.  The Role of Implied Volatility in Forecasting Future Realized Volatility and Jumps in Foreign Exchange, Stock, and Bond Markets , 2007 .

[49]  Andrew J. Patton Volatility Forecast Comparison Using Imperfect Volatility Proxies , 2006 .

[50]  Predicting Volatility: Getting the Most Out of Return Data Sampled at Different Frequencies , 2003 .

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

[52]  E. Ghysels,et al.  Série Scientifique Scientific Series Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies , 2022 .

[53]  Markku Lanne,et al.  Forecasting realized exchange rate volatility by decomposition , 2007 .

[54]  Kris Jacobs,et al.  Which Volatility Model for Option Valuation , 2002 .

[55]  Chris Kirby,et al.  The economic value of volatility timing using “realized” volatility ☆ , 2003 .

[56]  Jialin Yu,et al.  High Frequency Market Microstructure Noise Estimates and Liquidity Measures , 2008, 0906.1444.

[57]  N. Shephard,et al.  Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise , 2006 .

[58]  T. Bollerslev,et al.  Realized volatility forecasting and market microstructure noise , 2011 .

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

[60]  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.

[61]  F. Diebold,et al.  (Understanding, Optimizing, Using and Forecasting) Realized Volatility and Correlation * , 1999 .

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

[63]  Daniel B. Nelson CONDITIONAL HETEROSKEDASTICITY IN ASSET RETURNS: A NEW APPROACH , 1991 .

[64]  Jump Robust Volatility Estimation , 2008 .

[65]  Yacine Ait-Sahalia,et al.  How Often to Sample a Continuous-Time Process in the Presence of Market Microstructure Noise , 2003 .

[66]  Zhou Zhou,et al.  “A Tale of Two Time Scales: Determining Integrated Volatility with Noisy High-Frequency Data” , 2005 .

[67]  Olivier Scaillet,et al.  Jumps in High-Frequency Data: Spurious Detections, Dynamics, and News , 2016 .