Laurent

Publications

Volatility forecasts evaluation and comparisonJournal articleSébastien Laurent et Francesco Violante, Wiley Interdisciplinary Reviews: Computational Statistics, Volume 4, Issue 1, pp. 1-12, 2012

This article surveys the most important developments in volatility forecast comparison and model selection. We review a number of evaluation methods and testing procedures for predictive accuracy based on statistical loss functions. We also review recent contributions on the admissible form of loss functions ensuring consistency of the ordering when forecast performances are evaluated with respect to an imperfect volatility proxy. The techniques discussed are illustrated using artificial and EUR/USD exchange rate data. WIREs Comp Stat 2012, 4:1–12. doi: 10.1002/wics.190For further resources related to this article, please visit the WIREs website

Nonparametric Tests for Intraday Jumps: Impact of Periodicity and Microstructure NoiseBook chapterKris Boudt, Jonathan Cornelissen, Christophe Croux et Sébastien Laurent, In: Handbook of Volatility Models and Their Applications, Luc Bauwens, Christian M. Hafner et Sébastien Laurent (Eds.), 2012-04, Volume 18, pp. 447-463, John Wiley & Sons, Inc., 2012

This chapter contains sections titled: * Introduction * Model * Price Jump Detection Method * Simulation Study * Comparison on NYSE Stock Prices * Conclusion

Nonparametric Tests for Intraday Jumps: Impact of Periodicity and Microstructure NoiseBook chapterKris Boudt, Jonathan Cornelissen, Christophe Croux et Sébastien Laurent, In: Handbook of Volatility Models and Their Applications, Luc Bauwens, Christian M. Hafner et Sébastien Laurent (Eds.), 2012-04, Volume 18, pp. 447-463, John Wiley & Sons, Inc., 2012

This chapter contains sections titled: * Introduction * Model * Price Jump Detection Method * Simulation Study * Comparison on NYSE Stock Prices * Conclusion

Handbook of Volatility Models and Their ApplicationsBookLuc Bauwens, Christian M. Hafner et Sébastien Laurent (Eds.), 2012-04, 566 pages, John Wiley & Sons, 2012

A complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency. Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels.

Outlyingness Weighted CovariationJournal articleSébastien Laurent et Christophe Croux, Journal of Financial Econometrics, Volume 9, Issue 4, pp. 657-684, 2011

Quadratic covariation is a popular descriptive measure for the volatility of a multivariate price process. It is consistently estimated by the sum of outer products of high-frequency returns. The proposed realized outlyingness weighted covariation (ROWCov) is a weighted sum of outer products of high-frequency returns and downweights returns that, because of jumps or other reasons, are outliers under the Brownian semimartingale model. The ROWCov is positive semidefinite and remains consistent for the integrated covariance in the presence of a finite-activity jump process. We illustrate the usefulness of the estimator on five-minute returns on the transaction prices of the Dow Jones Industrial Average constituents. Copyright The Author 2011. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

Jumps, cojumps and macro announcementsJournal articleSébastien Laurent, Jérôme Lahaye et Christopher J. Neely, Journal of Applied Econometrics, Volume 26, Issue 6, pp. 893-921, 2011

We analyze and assess the impact of macroeconomic announcements on the discontinuities in many assets: stock index futures, bond futures, exchange rates, and gold. We use bi-power variation and the recently proposed non-parametric techniques of Lee and Mykland (2006) to extract jumps. Beyond characterizing the jump and cojump dynamics of many assets, we analyze how news arrival causes jumps and cojumps and estimate limited-dependent-variable models to quantify the impact of surprises. We confirm previous findings that some surprises create jumps. However, many announcements do not create jumps and many jumps are not related to announcements. The propensity of surprises to create jumps differs across asset classes, i.e., exchange rates, bonds, stock index. Payroll announcements are most important on stocks and bonds futures markets. Trade related news often creates cojumps on exchange rate markets.

Common Intraday PeriodicityJournal articleSébastien Laurent, Alain Hecq et Franz C. Palm, Journal of Financial Econometrics, Volume 10, Issue 2, pp. 325-353, 2011

Using a reduced rank regression framework as well as information criteria, we investigate the presence of commonalities in the intraday periodicity, a dominant feature in the return volatility of most intraday financial time series. We find that the test has little size distortion and reasonable power even in the presence of jumps. We also find that only three factors are needed to describe the intraday periodicity of 30 U.S. asset returns sampled at the 5-minute frequency. Interestingly, we find that for most series, the models imposing these commonalities deliver better forecasts of the conditional intraday variance than those where the intraday periodicity is estimated for each asset separately. Copyright The Author 2011. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

Robust estimation of intraweek periodicity in volatility and jump detectionJournal articleSébastien Laurent, Kris Boudt et Christophe Croux, Journal of Empirical Finance, Volume 18, Issue 2, pp. 353-367, 2011

Opening, lunch and closing of financial markets induce a periodic component in the volatility of high-frequency returns. We show that price jumps cause a large bias in the classical periodicity estimators and propose robust alternatives. We find that accounting for periodicity greatly improves the accuracy of intraday jump detection methods. It increases the power to detect the relatively small jumps occurring at times for which volatility is periodically low and reduces the number of spurious jump detections at times of periodically high volatility. We use the series of detected jumps to estimate robustly the long memory parameter of the squared EUR/USD, GBP/USD and YEN/USD returns.

Trading activity, realized volatility and jumpsJournal articleSébastien Laurent, Pierre Giot et Mikael Petitjean, Journal of Empirical Finance, Volume 17, Issue 1, pp. 168-175, 2010

This paper takes a new look at the relation between volume and realized volatility. In contrast to prior studies, we decompose realized volatility into two major components: a continuously varying component and a discontinuous jump component. Our results confirm that the number of trades is the dominant factor shaping the volume-volatility relation, whatever the volatility component considered. However, we also show that the decomposition of realized volatility bears on the volume-volatility relation. Trade variables are positively related to the continuous component only. The well-documented positive volume-volatility relation does not hold for jumps.

Does transparency in central bank intervention policy bring noise to the FX market?: The case of the Bank of JapanJournal articleSébastien Laurent, Jean-Yves Gnabo et Christelle Lecourt, Journal of International Financial Markets, Institutions and Money, Volume 19, Issue 1, pp. 94-111, 2009

This paper empirically investigates the induced effect of a more and less transparent central bank intervention (CBI) policy on rumors that can emerge. Using the case of Japan, we estimate a dynamic-probit model that explains the main determinants of false reports (i.e. falsely reported interventions) and anticipative rumors (i.e. rumors about future interventions) with reference to the intervention strategy adopted by the central bank for actual and oral interventions, and the uncertainty climate of the market captured by two volatility measures. Our results suggest that the induced effect of a transparent CBI policy on market rumors critically depends on the type of speeches made by officials.