Multi-jumps

Caporin, Massimiliano and Kolokolov, Aleksey and Renò, Roberto (2014): Multi-jumps.

Abstract

We provide clear-cut evidence for economically and statistically significant multivariate jumps (multi-jumps) occurring simultaneously in stock prices by using a novel nonparametric test based on smoothed estimators of integrated variances. Detecting multi-jumps in a panel of liquid stocks is more statistically powerful and economically informative than the detection of univariate jumps in the market index. On the contrary of index jumps, multi-jumps can indeed be associated with sudden and large increases of the variance risk-premium, and possess a statistically significant forecasting power for future volatility and correlations which implies a sizable deterioration in the diversification potential of asset allocation.

• Item Type: MPRA Paper
• Language: English
• Keywords: multi-jumps, co-jumps, price jumps, multivariate jumps, jumps testing
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C12 - Hypothesis Testing: General; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics; G - Financial Economics > G0 - General > G01 - Financial Crises; G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice ; Investment Decisions; G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation

References

Ait-Sahalia, Y. and J. Jacod (2009). Testing for jumps in a discretely observed process. Annals of Statistics 37, 184-222.

Ait-Sahalia, Y. and J. Jacod (2012). Analyzing the spectrum of asset returns: Jump and volatility components in high frequency data. Journal of Economic Literature 50 (4), 1007-1050.

Andersen, T., L. Benzoni, and J. Lund (2002). An empirical investigation of continuous-time equity return models. Journal of Finance 57, 1239-1284.

Andersen, T., T. Bollerslev, and F. X. Diebold (2007). Roughing it up: Including jump components in the measurement, modeling and forecasting of return volatility. Review of Economics and Statistics 89, 701-720.

Andersen, T., T. Bollerslev, and D. Dobrev (2007). No-arbitrage semi-martingale restrictions for continuous-time volatility models subject to leverage effects, jumps and iid noise: Theory and testable distributional implications. Journal of Econometrics 138 (1), 125-180.

Barndorff-Nielsen, O. and N. Shephard (2003). Measuring the impact of jumps in multivariate price processes using bi-power covariation. Working paper,Nuffeld College, oxford University.

Barndorff-Nielsen, O. E. and N. Shephard (2006). Econometrics of testing for jumps in Financial economics using bipower variation. Journal of Financial Econometrics 4, 1-30.

Barro, R. (2006). Rare disasters and asset markets in the twentieth century. The Quarterly Journal of Economics 121 (3), 823-866.

Bibinger, M. and L. Winkelmann (2013). Econometrics of co-jumps in high-frequency data with noise. Working Paper.

Bollerslev, T., T. H. Law, and G. Tauchen (2008). Risk, jumps and diversification. Journal of Econo- metrics 144, 234-256.

Bollerslev, T., G. Tauchen, and H. Zhou (2008). Expected stock returns and variance risk premia. Review of Financial Studies 22 (11), 4463-4492.

Bollerslev, T. and V. Todorov (2011). Tails, fears, and risk premia. The Journal of Finance 66 (6), 2165-2211.

Busch, T., B. Christensen, and M. Nielsen (2011). The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets. Journal of Econometrics 160, 48-57.

Carr, P. and L. Wu (2009). Variance risk premiums. Review of Financial Studies 22 (3), 1311-1341.

Christensen, K., R. Oomen, and M. Podolskij (2014). Fact or friction: Jumps at ultra high frequency. Journal of Financial Economics. Forthcoming.

Clements, A. and Y. Liao (2013). The dynamics of co-jumps, volatility and correlation. Working Paper.

Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics 7, 174-196.

Corsi, F., D. Pirino, and R. Reno (2010). Threshold bipower variation and the impact of jumps on volatility forecasting. Journal of Econometrics 159, 276-288.

Das, S. and R. Uppal (2004). Systemic Risk and International Portfolio Choice. The Journal of Fi- nance 59 (6), 2809-2834.

DeMiguel, V., L. Garlappi, and R. Uppal (2009). Optimal versus naive diversification: How inrefficient is the 1/N portfolio strategy? Review of Financial Studies 22, 1915-1953.

Drechsler, I. (2013). Uncertainty, time-varying fear, and asset prices. The Journal of Finance 68 (5), 1843-1889.

Drechsler, I. and A. Yaron (2011). What's vol got to do with it. Review of Financial Studies 24 (1), 1-45.

Dumitru, A.-M. and G. Urga (2012). Identifying jumps in financial assets: a comparison between nonparametric jump tests. Journal of Business & Economic Statistics 30 (2), 242-255.

Evans, K. P. (2011). Intraday jumps and us macroeconomic news announcements. Journal of Banking & Finance 35 (10), 2511-2527.

Gabaix, X. (2012). Variable rare disasters: An exactly solved framework for ten puzzles in macro-finance. The Quarterly Journal of Economics 127 (2), 645-700.

Gilder, D., M. B. Shackleton, and S. J. Taylor (2014). Cojumps in stock prices: Empirical evidence. Journal of Banking & Finance 40, 443-459.

Hautsch, N. and M. Podolskij (2013). Preaveraging-based estimation of quadratic variation in the presence of noise and jumps: theory, implementation, and empirical evidence. Journal of Business & Economic Statistics 31 (2), 165-183.

Jacod, J. and V. Todorov (2009). Testing for common arrivals of jumps for discretely observed multidimensional processes. Annals of Statistics 37, 1792-1838.

Jiang, G. and R. Oomen (2008). Testing for jumps when asset prices are observed with noise a "swap variance" approach. Journal of Econometrics 144 (2), 352-370.

Jiang, G. J. and Y. S. Tian (2005). The model-free implied volatility and its information content. Review of Financial Studies 18 (4), 1305-1342.

Lahaye, J., S. Laurent, and C. J. Neely (2011). Jumps, cojumps and macro announcements. Journal of Applied Econometrics 26 (6), 893-921.

Lee, S. (2012). Jumps and information ow in financial markets. Review of Financial Studies 25 (2), 439-479.

Lee, S. and P. Mykland (2008). Jumps in financial markets: A new nonparametric test and jump dynamics. Review of Financial studies 21 (6), 25-35.

Longin, F. and B. Solnik (2001). Extreme correlation of international equity markets. Journal of Finance 56 (2), 649-676.

Mancini, C. (2009). Non-parametric threshold estimation for models with stochastic diffusion coeffcient and jumps. Scandinavian Journal of Statistics 36 (2), 270-296.

Mancini, C. and F. Gobbi (2012). Identifying the brownian covariation from the co-jumps given discrete observations. Econometric Theory 28 (2), 2-49.

Podolskij, M. and M. Vetter (2009). Bipower-type estimation in a noisy diffusion setting. Stochastic Processes and their Applications 119, 2803-2831.

Podolskij, M. and D. Ziggel (2007). Bootstrapping bipower variation. Tech. rep., Ruhr-Universitat Bochum.

Podolskij, M. and D. Ziggel (2010). New tests for jumps in semimartingale models. Statistical inference for stochastic processes 13 (1), 15-41.

Todorov, V. (2010). Variance risk-premium dynamics: The role of jumps. Review of Financial Stud- ies 23 (1), 345-383.

Todorov, V. and G. Tauchen (2010). Activity signature functions for high-frequency data analysis. Journal of Econometrics 54, 125-138.