Forecasting High-Frequency Volatility Shocks An Analytical Real-Time
This thesis presents a new strategy that unites qualitative and quantitative mass data in form of text news and tick-by-tick asset prices to forecast the risk of upcoming volatility shocks. Holger Kömm embeds the proposed strategy in a monitoring system,
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Holger Kömm
Forecasting HighFrequency Volatility Shocks An Analytical Real-Time Monitoring System
Holger Kömm Ingolstadt, Germany Catholic University Eichstätt-Ingolstadt, 2015 First Supervisor: Prof. Dr. Küsters Second Supervisor: Prof. Dr. Wilde Date of disputation: April 29th, 2015
ISBN 978-3-658-12595-0 ISBN 978-3-658-12596-7 (eBook) DOI 10.1007/978-3-658-12596-7 Library of Congress Control Number: 2015960928 Springer Gabler © Springer Fachmedien Wiesbaden 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, speci¿cally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on micro¿lms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a speci¿c statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer Gabler is a brand of Springer Fachmedien Wiesbaden Springer Fachmedien Wiesbaden is part of Springer Science+Business Media (www.springer.com)
Dedicated to my parents and my priceless wife.
Abstract The effect of public observable but unexpected incidents on financial market volatility is of primary interest, both for tradespeople willing to take a risk as well as for risk regulators, willing to bypass potential imponderables. Therefore, this thesis presents a new strategy that unites qualitative and quantitative mass data in form of text news and tick-by-tick asset prices to forecast the risk of upcoming shocks. The proposed strategy is embedded in a monitoring system, using 1. a sequence of competing estimators to compute the unobservable volatility, 2. a new two-state Markov switching mixture model for autoregressive and zero-inflated time-series to identify structural breaks in a latent data generation process and 3. a selection of competing pattern recognition algorithms to classify the potential information embedded in unexpected, but public observable text data in shock and non-shock information. The monitor is trained, tested, and evaluated on a two year survey on the prime standard assets listed in the indices DAX, MDAX, SDAX and TecDAX.
VII
Acknowledgements It is a genuine pleasure to express my deep sense of thanks and gratitude to my mentor and guide, Prof. Ulrich Küsters, holder of the Chair of Statistics and Qu
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