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Big Data, Artificial Intelligence and Data Analysis Set
coordinated by
Jacques Janssen
Volume 8
Applied Modeling Techniques and Data Analysis 2
Financial, Demographic, Stochastic and Statistical Models and Methods
Edited by
Yannis Dimotikalis
Alex Karagrigoriou
Christina Parpoula
Christos H. Skiadas
First published 2021 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd
27-37 St George’s Road
London SW19 4EU
UK
John Wiley & Sons, Inc.
111 River Street
Hoboken, NJ 07030
USA
© ISTE Ltd 2021
The rights of Yannis Dimotikalis, Alex Karagrigoriou, Christina Parpoula and Christos H. Skiadas to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.
Library of Congress Control Number: 2020951002
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-78630-674-6
Preface
Data analysis as an area of importance has grown exponentially, especially during the past couple of decades. This can be attributed to a rapidly growing technology industry and the wide applicability of computational techniques, in conjunction with new advances in analytic tools. Modeling enables analysts to apply various statistical models to the data they are investigating, to identify relationships between variables, to make predictions about future sets of data, as well as to understand, interpret and visualize the extracted information more strategically. Many new research results have recently been developed and published and many more are developing and in progress at the present time. The topic is also widely presented at many international scientific conferences and workshops. This being the case, the need for the literature that addresses this is self-evident. This book includes the most recent advances on the topic. As a result, on one hand, it unifies in a single volume all new theoretical and methodological issues and, on the other, introduces new directions in the field of applied data analysis and modeling, which are expected to further grow the applicability of data analysis methods and modeling techniques.
This book is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, who have been working on the front end of data analysis. The chapters included in this collective volume represent a cross-section of current concerns and research interests in the above-mentioned scientific areas. This volume is divided into two parts with a total of 17 chapters in a form that provides the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.
Part 1 focuses on financial and demographic modeling techniques and includes nine chapters: Chapter 1, “Data Mining Application Issues in the Taxpayer Selection Process”, by Mauro Barone, Stefano Pisani and Andrea Spingola; Chapter 2, “Asymptotics of Implied Volatility in the Gatheral Double Stochastic Volatility Model”, by Mohammed Albuhayri, Anatoliy Malyarenko, Sergei Silvestrov, Ying Ni, Christopher Engström, Finnan Tewolde and Jiahui Zhang; Chapter 3, “New Dividend Strategies”, by Ekaterina Bulinskaya; Chapter 4, “Introduction of Reserves in Self-adjusting Steering the Parameters
