Computational and Methodological Statistics (ERCIM 2013)
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Computational and Financial Econometrics (CFE 2017)
CFE-CMStatistics 2017 PROGRAMME AND ABSTRACTS 11th International Conference on Computational and Financial Econometrics (CFE 2017) http://www.cfenetwork.org/CFE2017 and 10th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (CMStatistics 2017) http://www.cmstatistics.org/CMStatistics2017 Senate House & Birkbeck University of London, UK 16 – 18 December 2017 ⃝c ECOSTA ECONOMETRICS AND STATISTICS. All rights reserved. I CFE-CMStatistics 2017 ISBN 978-9963-2227-4-2 ⃝c 2017 - ECOSTA ECONOMETRICS AND STATISTICS Technical Editors: Gil Gonzalez-Rodriguez and Marc Hofmann. All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted, in any other form or by any means without the prior permission from the publisher. II ⃝c ECOSTA ECONOMETRICS AND STATISTICS. All rights reserved. CFE-CMStatistics 2017 International Organizing Committee: Ana Colubi, Erricos Kontoghiorghes, Marc Levene, Bernard Rachet, Herman Van Dijk. CFE 2017 Co-chairs: Veronika Czellar, Hashem Pesaran, Mike Pitt and Stefan Sperlich. CFE 2017 Programme Committee: Knut Are Aastveit, Alessandra Amendola, Josu Arteche, Monica Billio, Roberto Casarin, Gianluca Cubadda, Manfred Deistler, Jean-Marie Dufour, Ekkehard Ernst, Jean-David Fermanian, Catherine Forbes, Philip Hans Franses, Marc Hallin, Alain Hecq, David Hendry, Benjamin Holcblat, Jan Jacobs, Degui Li, Alessandra Luati, Richard Luger, J Isaac Miller, Claudio Morana, Bent -
Academic Appointments Education Honours and Prizes from Universities and Learned Societies
Neil Shephard (this version: October 2019) Harvard University offices: Department of Economics, M29, Littauer Center, 1805 Cambridge Street, Cambridge, MA 02138 Department of Statistics, Science Center, 1 Oxford Street, Cambridge, MA 02138 http://www.people.fas.harvard.edu/~shephard/ Born October 8, 1964. Married, 2 children. Academic Appointments Current academic position: • Frank B. Baird Jr, Professor of Science, Harvard University, 2018-. Professor of Economics and Professor of Statistics, Harvard University. Time equally split between Department of Economics and Department of Statistics. • Chair (head of department), Department of Statistics, Harvard University, 2015-2018, 2019- 2022. Current editorial position: • Associate editor, Econometrica, 2002-. Past academic positions: • Faculty member, Harvard University, 2013- o Professor of Economics and of Statistics, time equally split between Department of Economics and Department of Statistics, 2013-2018. • Professor of Economics, Department of Economics, Oxford University, 1999-2013. • Fellow, Nuffield College, Oxford, 1993- o Emeritus Fellow, Nuffield College, Oxford, 2014-. o Professorial Fellow in Economics, Nuffield College, Oxford, 2006-2013. o Official Fellow in Economics, Nuffield College, Oxford, 1993-2006. o Gatsby Research Fellow in Econometrics, Nuffield College, Oxford, 1991-1993. • Lecturer in Statistics, London School of Economics, 1988-1993. Tenured aged 28. Education Ph.D., London School of Economics, 1990. M.Sc., London School of Economics, 1987, Statistics (awarded -
CFE-Cmstatistics 2015
CFE-CMStatistics 2015 PROGRAMME AND ABSTRACTS 9th International Conference on Computational and Financial Econometrics (CFE 2015) http://www.cfenetwork.org/CFE2015 and 8th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (CMStatistics 2015) http://www.cmstatistics.org/CMStatistics2015 Senate House & Birkbeck University of London, UK 12 – 14 December 2015 Econometrics and Statistics http://CFEnetwork.org http://CMStatistics.org c CFE and CMStatistics networks. All rights reserved. I CFE-CMStatistics 2015 ISBN 978-9963-2227-0-4 c 2015 - CFE and CMStatistics networks Technical Editors: Angela Blanco-Fernandez and Gil Gonzalez-Rodriguez. All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted, in any other form or by any means without the prior permission from the publisher. II c CFE and CMStatistics networks. All rights reserved. CFE-CMStatistics 2015 International Organizing Committee: Ana Colubi, Liudas Giraitis, Erricos Kontoghiorghes, Irini Moustaki, Zacharias Psaradakis, Berc Rustem and Herman Van Dijk. CFE 2015 Co-chairs: Richard T. Baillie, Jean-Pierre Urbain and Mike West. CFE 2015 Programme Committee: Manabu Asai, Tomaso Aste, Hilde C. Bjornlans, Peter Boswijk, Gianluca Cubadda, Manfred Deistler, Jean- Marie Dufour, Marco Gallegati, Christian Gourieroux, Jim Griffin, Marc Hallin, Alain Hecq, David Hendry, Robert Hudson, Degui Li, Zudi Lu, Richard Luger, Gael Martin, Gian Luigi Mazzi, Serena Ng, Yasuhiro Omori, Christopher Otrok, Michael Pitt, D.S.G. Pollock, Artem Prokhorov, Francesco Ravazzolo, Willi Semmler, Pierre Siklos, Mike Smith, Laura Spierdijk, Genaro Sucarrat, Carsten Trenkler, Peter Zadrozny and Jean-Michel Zakoian. ERCIM 2015 Co-chairs: Irene Gijbels, Mia Hubert, Byeong U. -
Econometric Analysis of Multivariate Realised QML: Estimation of the Covariation of Equity Prices Under Asynchronous Trading
Econometric analysis of multivariate realised QML: estimation of the covariation of equity prices under asynchronous trading Neil Shephard Nuffield College, New Road, Oxford OX1 1NF, UK, Department of Economics, University of Oxford [email protected] Dacheng Xiu 5807 S. Woodlawn Ave, Chicago, IL 60637, USA Booth School of Business, University of Chicago [email protected] First full draft: February 2012 This version: 22nd October 2012 Abstract Estimating the covariance between assets using high frequency data is challenging due to market microstructure effects and asynchronous trading. In this paper we develop a multivari- ate realised quasi-likelihood (QML) approach, carrying out inference as if the observations arise from an asynchronously observed vector scaled Brownian model observed with error. Under stochastic volatility the resulting realised QML estimator is positive semi-definite, uses all avail- able data, is consistent and asymptotically mixed normal. The quasi-likelihood is computed using a Kalman filter and optimised using a relatively simple EM algorithm which scales well with the number of assets. We derive the theoretical properties of the estimator and prove that it achieves the efficient rate of convergence. We show how to make it obtain the non-parametric efficiency bound for this problem. The estimator is also analysed using Monte Carlo methods and applied to equity data with varying levels of liquidity. Keywords: EM algorithm; Kalman filter; market microstructure noise; non-synchronous data; portfolio optimisation; quadratic variation; quasi-likelihood; semimartingale; volatility. JEL codes: C01; C14; C58; D53; D81 1 Introduction 1.1 Core message The strength and stability of the dependence between asset returns is crucial in many areas of financial economics.