IEOR Faculty Profiles 2011-2012
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Financial Darwinism
Praise for Financial Darwinism “The world’s political and economic uncertainties, exacerbated by a serious lack of financial transparency, can lead business leaders to feel like they may be virtually flying blind in a rapidly changing global economy. Leo Tilman offers some important tools to address the clear imperative of better strategic and systemic risk management.” William E. Brock Former United States Senator United States Trade Representative and United States Secretary of Labor “History is littered with the wrecks of financial institutions. Some failed to change their strategies. Others pursued tantalizing returns while paying insufficient attention to the risks. Judging from recent financial crises, many financial institutions still have not learned how to avoid crippling, perhaps even life-threatening, wrecks. Leo Tilman’s timely book is a navigator’s manual for managers of 21st-century financial institutions. To prosper, even to survive, Tilman clearly and forcefully shows that they must abandon outmoded strategies, adopt new ones, and pay much more attention to the trade-off between risk and return. He blends theory with experience to show how this can be done, and even how it has been done.” Dr. Richard Sylla Henry Kaufman Professor of The History of Financial Institutions and Markets Professor of Economics Leonard N. Stern School of Business, New York University Financial Darwinism Create Value or Self-Destruct in a World of Risk LEO M. TILMAN John Wiley & Sons, Inc. Copyright C 2009 by Leo M. Tilman. All rights reserved. Published -
Liquidity at Risk Joint Stress Testing of Liquidity and Solvency Risk
Liquidity at Risk Joint Stress Testing of Liquidity and Solvency Risk Rama Cont & Artur Kotlicki University of Oxford Laura Valderrama International Monetary Fund 1 Solvency stress testing 2 Solvency Risk • Solvency risk is driven by the difference in firm’s asset values and its liabilities. • Bank stress testing, which has become a key tool for bank supervisors, has also mainly focused on solvency risk. • Regulation of insurance companies also focused on solvency risk (Solvency II, Swiss Solvency test). • However, solvency risk does not give the full picture – we have spectacular failures of SIFIs due to lack of liquidity: • Bear Stearns held excess capital at the time of its default. • AIG, which failed to fulfill large payment triggered by a downgrade, was not insolvent at the time of failure. • Banco Popular, which failed through a lack of liquidity in 2017, displayed a capital ratio 6:6% in the 2016 EBA adverse scenario. 3 Liquidity Risk and Default • Liquidity risk: failure to meet a short-term payment obligation. • Default of payment is the legal definition of default. • Inherent risk to instability in short-term funding (e.g. debt roll-over risk) and cash-flows (e.g. variation margin). • Supervision and regulation of bank liquidity: • Liquidity Coverage Ratio (LCR): banks to hold liquidity provision for expected outflows over 30-day time horizon. • Net Stable Funding Ratio (NSFR): limits over-reliance on short-term wholesale funding and aims to increase funding stability. • Liquidity stress testing: e.g. ECB’s 2019 sensitivity analysis of liquidity risk (LiST) typically done separately from solvency stress testing. 4 Liquidity Risk Defining liquidity requires the introduction of a horizon T. -
7Th International Young Finance Scholars' Conference
7th International Young Finance Scholars' Conference 12-13 JULY 2021 Event Programme ZOOM PHBS UK CAMPUS Register at http://www.pku.org.uk/iyfs 1 Table of Contents Programme Overview ................................................................................................................................... 3 Welcome to the 7th IYFS Conference .............................................................................................................. 4 Registration .................................................................................................................................................. 5 How to register ......................................................................................................................................................5 Where to find your tickets ......................................................................................................................................5 What to do if your tickets are missing .....................................................................................................................5 Conference Organization .............................................................................................................................. 5 About Zoom breakout rooms .................................................................................................................................5 Eventbrite online event page..................................................................................................................................6 -
A New Approach to the Minimum Cut Problem
A New Approach to the Minimum Cut Problem DAVID R. KARGER Massachusetts Institute of Technology, Cambridge, Massachusetts AND CLIFFORD STEIN Dartmouth College, Hanover, New Hampshire Abstract. This paper presents a new approach to finding minimum cuts in undirected graphs. The fundamental principle is simple: the edges in a graph’s minimum cut form an extremely small fraction of the graph’s edges. Using this idea, we give a randomized, strongly polynomial algorithm that finds the minimum cut in an arbitrarily weighted undirected graph with high probability. The algorithm runs in O(n2log3n) time, a significant improvement over the previous O˜(mn) time bounds based on maximum flows. It is simple and intuitive and uses no complex data structures. Our algorithm can be parallelized to run in 51# with n2 processors; this gives the first proof that the minimum cut problem can be solved in 51#. The algorithm does more than find a single minimum cut; it finds all of them. With minor modifications, our algorithm solves two other problems of interest. Our algorithm finds all cuts with value within a multiplicative factor of a of the minimum cut’s in expected O˜(n2a) time, or in 51# with n2a processors. The problem of finding a minimum multiway cut of a graph into r pieces is solved in expected O˜(n2(r21)) time, or in 51# with n2(r21) processors. The “trace” of the algorithm’s execution on these two problems forms a new compact data structure for representing all small cuts and all multiway cuts in a graph. This data structure can be efficiently transformed into the more standard cactus representation for minimum cuts. -
Model Uncertainty and Its Impact on Derivative Instruments
Mathematical Finance, Vol. 16, No. 3 (July 2006), 519–547 MODEL UNCERTAINTY AND ITS IMPACT ON THE PRICING OF DERIVATIVE INSTRUMENTS RAMA CONT Centre de Math´ematiques Appliqu´ees, Ecole Polytechnique, Palaiseau, France Uncertainty on the choice of an option pricing model can lead to “model risk” in the valuation of portfolios of options. After discussing some properties which a quan- titative measure of model uncertainty should verify in order to be useful and relevant in the context of risk management of derivative instruments, we introduce a quantita- tive framework for measuring model uncertainty in the context of derivative pricing. Two methods are proposed: the first method is based on a coherent risk measure com- patible with market prices of derivatives, while the second method is based on a convex risk measure. Our measures of model risk lead to a premium for model uncertainty which is comparable to other risk measures and compatible with observations of mar- ket prices of a set of benchmark derivatives. Finally, we discuss some implications for the management of “model risk.” KEY WORDS: model uncertainty, Knightian uncertainty, option pricing, incomplete markets, volatility, ambiguity, coherent risk measures, convex risk measures 1. INTRODUCTION In March 1997, Bank of Tokyo/Mitsubishi announced that its New York-based deriva- tives unit had suffered a $83 million loss because their internal pricing model overvalued a portfolio of swaps and options on U.S. interest rates. A few weeks later, NatWest Capital Markets announced a £50 million loss because of a mispriced portfolio of German and U.K. interest rate options and swaptions run by a single-derivatives trader in London. -
Pathwise Derivatives Valuation As a Cauchy Problem on the Space of Monte Carlo Paths
UNIVERSITY OF SOUTHAMPTON FACULTY OF PHYSICAL SCIENCES AND ENGINEERING Electronics and Computer Science Pathwise Derivatives Valuation as a Cauchy Problem on the Space of Monte Carlo Paths by Laurie M. Carver Thesis for the degree of Doctor of Philosophy September 2019 UNIVERSITY OF SOUTHAMPTON ABSTRACT FACULTY OF PHYSICAL SCIENCES AND ENGINEERING Electronics and Computer Science Doctor of Philosophy PATHWISE DERIVATIVES VALUATION AS A CAUCHY PROBLEM ON THE SPACE OF MONTE CARLO PATHS by Laurie M. Carver It is well-known that, under classical assumptions, the arbitrage-free value of European options contracts in complete continuous Markovian models is given by the solution to a Cauchy problem on Rd. Recent research has shown that similar results hold in a path-dependent context, whereby values solve an analogous Cauchy problem on the non-separable path space Dd of càdlàg paths, in an almost sure sense, that is – on an implicit subset with model probability one. This presents difficulties for numerical solution, as practitioners must work with real data time series and at most countably many operations. This thesis resolves this in a wide class of continuous path- dependent market models, by showing that in this context derivatives’ valuation is equivalent to any explicit 2 d solving a Cauchy problem for path in an subset of a new Banach space MP .R / of Monte Carlo paths, that is naturally adapted to practitioner intuition and numerical methods. First, we develop a new framework for the pathwise analysis of market risk models. Based on a new notion of pathwise variance that generalises existing notions of quadratic variation, we con- 2 d struct the new Banach space MP .R / and show how its geometry captures practitioner intuition about risk models’ volatility. -
Introduction to Algorithms, 3Rd
Thomas H. Cormen Charles E. Leiserson Ronald L. Rivest Clifford Stein Introduction to Algorithms Third Edition The MIT Press Cambridge, Massachusetts London, England c 2009 Massachusetts Institute of Technology All rights reserved. No part of this book may be reproduced in any form or by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. For information about special quantity discounts, please email special [email protected]. This book was set in Times Roman and Mathtime Pro 2 by the authors. Printed and bound in the United States of America. Library of Congress Cataloging-in-Publication Data Introduction to algorithms / Thomas H. Cormen ...[etal.].—3rded. p. cm. Includes bibliographical references and index. ISBN 978-0-262-03384-8 (hardcover : alk. paper)—ISBN 978-0-262-53305-8 (pbk. : alk. paper) 1. Computer programming. 2. Computer algorithms. I. Cormen, Thomas H. QA76.6.I5858 2009 005.1—dc22 2009008593 10987654321 Index This index uses the following conventions. Numbers are alphabetized as if spelled out; for example, “2-3-4 tree” is indexed as if it were “two-three-four tree.” When an entry refers to a place other than the main text, the page number is followed by a tag: ex. for exercise, pr. for problem, fig. for figure, and n. for footnote. A tagged page number often indicates the first page of an exercise or problem, which is not necessarily the page on which the reference actually appears. ˛.n/, 574 (set difference), 1159 (golden ratio), 59, 108 pr. jj y (conjugate of the golden ratio), 59 (flow value), 710 .n/ (Euler’s phi function), 943 (length of a string), 986 .n/-approximation algorithm, 1106, 1123 (set cardinality), 1161 o-notation, 50–51, 64 O-notation, 45 fig., 47–48, 64 (Cartesian product), 1162 O0-notation, 62 pr. -
Systemic Risk : Channels of Contagion in Financial Systems Rama CONT
Systemic risk : channels of contagion in financial systems Rama CONT Systemic Risk • Systemic risk may be defined as the risk that a significant portion of the financial system fails to function properly. • The monitoring and management of systemic risk has become a major issue for regulators and market participants since the 2008 crisis. • The financial crisis has simultaneously underlined · the importance of contagion effects and systemic risk · the lack of adequate indicators for monitoring systemic risk. · the lack of adequate data for computing such indicators Many initiatives under way: new regulations (Basel III), new financial architecture (derivatives clearinghouses), legislation on transparency in OTC markets, creation of Office of Financial Research (US), various Financial Stability Boards BUT: methodological shortcomings, open questions Rama CONT: Contagion and systemic risk in financial networks Systemic Risk Various questions: Mechanisms which lead to systemic risk Measures / metrics of systemic risk Monitoring of systemic risk: data type/granularity ? Management and control of systemic risk by regulators Need for quantitative approaches and objective criteria to deal with these questions Rama CONT: Contagion and systemic risk in financial networks Channels of contagion: underlying network structure • Each of these mechanism may be viewed as a contagion process on some underlying “network”, but the relevant “network topologies’’ and data needed to track them are different in each case: • 1. Correlation: cross-sectional data on common exposures to risk factors/asset classes for tracking large-scale imbalances • 2. Balance sheet contagion: network of interbank exposures, cross-holdings and liabilities + capital • 3. Spirals of illiquidity: network of short-term liabilities (payables) and receivables + ‘liquidity reserves’ • 4. -
DEAR CONGRESS & Presldent OBAMA
STEPHEN A. ROSS, Professor of financial economics, massachusetts institute of technology • KENT SMETTERS, Professor of economics, the university of Pennsylvania • JAGADEESH GOKHALE, senior fellow, cato institute • DarON ACEMOGLU, Professor of economics, massachusetts institute of technology • JOHN MAULDIN, President, millenium wave advisors • EDWARD LEAMER, Professor of economics, university of california, los angeles • IVAN WERNING, Professor of economics, massachusetts institute of technology • DIRK KRUEGER, Professor of economics, university of Pennsylvania • JERRY HAUSMAN, Professor of economics, massachusetts institute of technology • ARNOLD C HARBERGER, distinguished Professor, university of california, los angeles • EUGENE STEUERLE, senior fellow, the urban institute • MICHAEL MANOVE, Professor of economics, boston university • TODD IDSON, associate Professor of economics, boston university • CHRISTOPHE CHAMLEY, Professor of economics, boston university • JIANJUN MIAO, associate Professor of economics, boston university • CAROLINE HOXBY, Professor of economics, stanford university • DILIP MOOKHERJEE, Professor of economics, boston university • FRANCESCO DECAROLIS, assistant Professor, boston university • PETER KARL KRESL, PROFESSOR OF ECONOMICS EMERITUS, bucknell university • RICHARD HOFLER, Professor of economics, university of central florida • ROGER FELDMAN, blue cross Professor of health insurance, university of minnesota • JERE R. BEHRMAN, Professor of economics, university of Pennsylvania • BOB CHIRINKO, Professor of finance, -
Volume 35, Number 2, June
506 Rapport d’expert de Darrell Duffi e SYSTEMIC RISK IN FINANCIAL SYSTEMS AND CAPITAL MARKETS IN RELATIONSHIP WITH THE PROPOSED DRAFT CAPITAL MARKETS STABILITY ACT Report of Darrell Duffie, Dean Witter Distinguished Professor of Finance at the Graduate School of Business, Stanford University May 3, 2016 507 Rapport d’expert de Darrell Duffi e Résumé Le présent rapport examine certaines questions relatives à l’ébauche d’avant-projet de la Loi sur la stabilité des marchés des capitaux (LSMC). Les enjeux liés à la LSMC ne sont pas étudiés de façon exhaustive dans ce rapport. Il s’agit d’un rapport parmi plusieurs rapports d’experts, chacun traitant d’un sous-ensemble de questions. La partie 1 décrit le rôle du système financier au sein de l’économie nationale en général, en mettant l’accent sur le rôle des marchés de capitaux et les interdépendances au sein des marchés de capitaux. Le système financier (c.-à-d. la partie de l’économie qui gère les flux de capitaux, la liquidité et le risque financier) est essentiel à l’économie réelle (c.-à-d. la production, la distribution et la consommation de biens réels). Sans un accès stable et peu coûteux aux marchés financiers, l’économie réelle ne pourrait fonctionner. Les marchés de capitaux sont la sous-catégorie du système financier qui comprend les marchés des réclamations relatives aux obligations et aux capitaux propres, ainsi que les services financiers et les marchés qui les soutiennent. Les produits financiers, les fournisseurs de services financiers et les marchés au sein desquels les produits financiers sont échangés sont interreliés de façon complexe et forment le système financier. -
DISCRETE MATHEMATICS for COMPUTER SCIENTISTS This Page Intentionally Left Blank DISCRETE MATHEMATICS for COMPUTER SCIENTISTS
DISCRETE MATHEMATICS FOR COMPUTER SCIENTISTS This page intentionally left blank DISCRETE MATHEMATICS FOR COMPUTER SCIENTISTS Clifford Stein Columbia University Robert L. Drysdale Dartmouth College Kenneth Bogart Addison-Wesley Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo Editor in Chief: Michael Hirsch Editorial Assistant: Stephanie Sellinger Director of Marketing: Margaret Whaples Marketing Coordinator: Kathryn Ferranti Managing Editor: Jeffrey Holcomb Production Project Manager: Heather McNally Senior Manufacturing Buyer: Carol Melville Media Manufacturing Buyer: Ginny Michaud Art Director: Linda Knowles Cover Designer: Elena Sidorova Cover Art: Veer Media Project Manager: Katelyn Boller Full-Service Project Management: Bruce Hobart, Laserwords Composition: Laserwords Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook appear on appropriate page within text. The programs and applications presented in this book have been included for their instructional value. They have been tested with care, but are not guaranteed for any particular purpose. The publisher does not offer any warranties or representations, nor does it accept any liabilities with respect to the programs or applications. Copyright © 2011. Pearson Education, Inc., publishing as Addison-Wesley, 501 Boylston Street, Suite 900, Boston, Massachusetts 02116. All rights reserved. Manufactured in the United States of America. This publication is protected by Copyright, and permission should be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. -
Thomas H. Cormen Current Position Research Interests Education Honors and Awards
Thomas H. Cormen Department of Computer Science 6211 Sudikoff Laboratory Dartmouth College Hanover, NH 03755-3510 (603) 646-2417 [email protected] http://www.cs.dartmouth.edu/thc/ Current Position Professor of Computer Science. Research Interests Algorithm engineering, parallel computing, speeding up computations with high latency, Gray codes. Education Massachusetts Institute of Technology, Cambridge, Massachusetts Ph.D. in Electrical Engineering and Computer Science, February 1993. Thesis: “Virtual Memory for Data-Parallel Computing.” Advisor: Charles E. Leiserson. Minor: Engineering management and entrepreneurship. S.M. in Electrical Engineering and Computer Science, May 1986. Thesis: “Concentrator Switches for Routing Messages in Parallel Computers.” Advisor: Charles E. Leiserson. Princeton University, Princeton, New Jersey B.S.E. summa cum laude in Electrical Engineering and Computer Science, June 1978. Honors and Awards ACM Distinguished Educator, 2009. McLane Family Fellow, Dartmouth College, 2004–2005. Jacobus Family Fellow, Dartmouth College, 1998–1999. Dartmouth College Class of 1962 Faculty Fellowship, 1995–1996. Adopted Member, Dartmouth College Class of 1962, 2015. Professional and Scholarly Publishing Award in Computer Science and Data Processing, Association of American Publishers, 1990. Distinguished Presentation Award, 1987 International Conference on Parallel Processing, St. Charles, Illinois. Best Presentation Award, 1986 International Conference on Parallel Processing, St. Charles, Illinois. National Science Foundation Fellowship. Elected to Phi Beta Kappa, Tau Beta Pi, Eta Kappa Nu. Prepared on March 28, 2020. 1 Professional Experience Dartmouth College, Hanover, New Hampshire Chair, Department of Computer Science, July 2009–July 2015. Professor, Department of Computer Science, July 2004–present. Director of the Dartmouth Institute for Writing and Rhetoric, July 2007–June 2008.