Statistical Analysis of Financial Data in S-Plus
Although there are many books on mathematical finance, few deal with the statistical aspects of modern data analysis as applied to financial problems. This book fills this gap by addressing some of the most challenging issues facing any financial engineer
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Editorial Board George Casella
Stephen Fienberg
Ingram Olkin
Department of Statistics University of Florida Gainesville, FL 32611-8545 USA
Department of Statistics Carnegie Mellon University Pittsburgh, PA 15213-3890 USA
Department of Statistics Stanford University Stanford, CA 94305 USA
Library of Congress Cataloging-in-Publication Data Carmona, R. (Rene´) Statistical analysis of financial data in S-PLUS / Rene´ A. Carmona. p. cm. — (Springer texts in statistics) Based on the author’s lecture notes for a course at Princeton University. Includes bibliographical references and index. ISBN 0-387-20286-2 (alk. paper) 1. Finance—Mathematical models. 2. Finance—Econometric models. 3. S-Plus. I. Title. II. Series. HG106.C37 2003 332′01′51955—dc22 2003066218 ISBN 0-387-20286-2
Printed on acid-free paper.
© 2004 Springer-Verlag New York, Inc. All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer-Verlag New York, Inc., 175 Fifth Avenue, New York, NY 10010, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed in the United States of America. 9 8 7 6 5 4 3 2 1
(MVY)
SPIN 10953280
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To Chanel, Chelsea and St´ephanie
Preface
This book grew out of lectures notes written for a one-semester junior statistics course offered to the undergraduate students majoring in the Department of Operations Research and Financial Engineering at Princeton University. Tidbits of the history of this course will shed light on the nature and spirit of the book. The purpose of the course is to introduce the students to modern data analysis with an emphasis on a domain of application that is of interest to most of them: financial engineering. The prerequisites for this course are minimal, however it is fair to say that all of the students have already taken a basic introductory statistics course. Thus the elementary notions of random variables, expectation and correlation are taken for granted, and earlier exposure to statistical inference (estimation, tests and confidence intervals) is assumed. It is also expected that the students are familiar with a minimum of linear algebra as well as vector and matrix calculus. Because of my background, the course is both computational and mathematical in nature. Most problems considered are formulated in a rigorous manner. Mathematical facts are motivated by applications, stated precisely, justified at an intuitive level, but essentially never proven rigorously. The emphasi
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