DOCSLIB.ORG

Albanese, C., 285 Altman E., 61 Andersen, L., 134 Anderson, M., 65

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Albanese, C., 285 Altman E., 61 Andersen, L., 134 Anderson, M., 65 3GBINDEX 11/18/2013 16:4:36 Page 315 Index Albanese, C., 285 about, 251–252 Altman E., 61 Basel I, 23–24, 252 Andersen, L., 134 Basel I, reason for, 23 Anderson, M., 65 Basel II, 23, 153, 252 Anderson Darling test, 51 Basel II, credit value at risk ARCH model, 50, 161, 177 (CVaR) approach, 252–258 Artificial intelligence and financial Basel II, default probability- modeling, 287–299 default correlation relationship, Bayesian probabilities, 295–297 259–260 chaos theory, 291–295 Basel II, reason for, 23 chaos theory and finance, Basel II, required capital (RC) for 293–295 credit risk, 258–259 fuzzy logic, 290 Basel III, 23, 153, 252, 262 genetic algorithms, 290–291 Basel III, credit value at risk genetic fuzzy neural algorithms, (CVaR) approach, 262 291 Basel III, reason for, 23 neural networks, 287–289 and double default treatment, Artificial neural network (ANN), 269–274 288 double-default approach, Asset modeling, 174–176 270–274 Asset value, 159 substitution approach, Asymptotic single risk factor (ASRF) 269–270 approach, and correlation, Basel Committee for Banking 274 Supervision, 16 Attractor, 292 Baxter, N., 134 Autocorrelation (AC), 50 Bayes, Thomas, 295 Bayesian methods, 297, 299 Backhaus, J., 134 Bayesian probabilities, 295–297 Backpropagation, 288, 298 Bayesian statistics, 299 Backwardation techniques, 289 Bayesian theorem, 295–296 Bahar, R., 74 Bellaj, T., 285 Basel, derivative multiplier, 268–269 Binomial approach, 213 Basel accords. See also Correlation Binomial correlation approach, and Basel II and III 212 315 Correlation Risk Modeling and Management: An Applied Guide Including the Basel III Correlation Framework—with Interactive Correlation Models in Excel/VBA, First Edition. Gunter Meissner. © 2014 John Wiley & Sons Singapore Pte. Ltd. Published 2014 by John Wiley & Sons Singapore Pte. Ltd. 3GBINDEX 11/18/2013 16:4:37 Page 316 316 INDEX Binomial correlation measure Capital asset pricing model. See about, 72–73 Black-Scholes-Merton (BSM) binomial correlation measure option pricing model application, 73–74 Capital charge, 17, 273–274 vs. Pearson correlation model, 90 Capponi, A., 215 Binomial correlation measure Carr, P., 148, 159 application, 73–74 Cash CDOs, 103 Bivariate Gaussian copula, 75 n1, CDO’s (collateralized debt 132 obligations). See Collateralized Bivariate normal distribution, 80, debt obligations (CDOs) 270 CDS (credit default swap). See Black, F., 108, 176 Credit default swap (CDS) Black-Scholes-Merton (BSM) 1973 Chang, E., 148, 159 option pricing model, 9, 58, Chaos theory, 291–295, 299 106, 167 and finance, 293–294 Black-Scholes-Merton (BSM) option and finance application, 294–295 pricing model, 6 Chaotic system criteria, 291 Bollerslev, T., 160–161, 176 Cherubini, U., 65 Bond correlation Chi-squared test, 51 and default probability Cholesky decomposition, 81, 91–93, correlations, 53–54 116, 132 distribution, 54 Circularity, 89, 151 Bounded Jacobi process, 165–167 Clark, P., 148 Brace, A., 176 Clustering of volatility, 161 Brigo, D., 72, 215, 221, 225 Collateralized debt obligations Briys, E., 109 (CDO’s) Brooks, B., 61 about, 101–102 Brown, R., 163 advantages, 102–103 Brun, Marie-France, 169 basics, 101–105 Bubbles, 18–19 binomial correlation valuation, 73 Buraschi, A., 169, 171–172, 176 CDO complexity, 114 Buraschi, Porchia, and Trojani correlation, 222 model (2010), 168–169, global financial crisis, 18–19 171–172, 176 market price risk, 183 Burtchell, X., 65 model limitations in valuation, Buying correlation, 11 115 problems with Gaussian copula Calibration valuation, 113–116 financial models, 59 recovery rate, 113 Gaussian Copula limitations, 87 types of, 103–105, 115 Call options, 237 valuing, 105–113 buying and selling, 13 Commodity market, 181 3GBINDEX 11/18/2013 16:4:37 Page 317 Index 317 Commodity risk, 14 definition, 182 Computational finance, 284 financial practice examples, Concentration ratio, 33 184–187 Concentration risk, definition, 30 financial practices, 182–184 Conditional correlation, 162 and Gora in investments, Conditional defaults, 121 187–189 Conditional VaR risk, 24 measures of, 198–199 Conditionally independent default of risk/return ratio, 187 (CID) Cora financial practice examples application of, 149 in market risk management, contagion correlation models, 90 189–197 correlation approach, 134 option Cora and Gora, 185–187 correlation modeling, 122 option Vanna, 184–185 OFGC model, 120, 138 Cora in market risk management Constant asset correlation, vs. about, 189–195 stochastic asset correlation, 163 Gap-Cora, 196–197 Cont, R., 148 Correlated default distribution, Contagion correlation, 134 111 Contagion correlation models, Correlated default risk, 33 88–91, 148–150 Correlated default time Contagion default modeling, derivation of, 124 150–153 for multiple assets, 81–82 Copula applications, 85 using survival principles, 82–84 Copula correlation model, 18, 60 Correlated market risk, 33 Copula correlations Correlating Brownian motions, 90. about, 74–75 See also Geometric Brownian advantages and limitations, 90–91 motion (GBM) copula applications, 85 Correlation correlated default time for definition, 34–35 multiple assets, 81–82 definition by usage, 36–37 correlated default time using Correlation, basics, properties and survival principles, 82–84 terminology Gaussian copula, 76–81 about, 1–2 Gaussian copula limitations, correlation risk, 2–5 85–88 correlation risk as part of finance Copula functions, 75 risk, 24–33 Copula model, 27, 58 finance usage, 6–24 Copulas, limitations of, for finance, Correlation, empirical properties 90–91 bond correlations and default Cora probability correlations, 53–54 of a CDO, 229–230 equity correlation and for CDSs, 223–225 autocorrelation, 50–51 3GBINDEX 11/18/2013 16:4:37 Page 318 318 INDEX Correlation, empirical properties Correlation modeling future, (Continued) numerical finance, 283–287 equity correlation distribution, Correlation models, 34 51–52 Correlation risk equity correlation volatility as about, 2–5 indicator, 52–53 in Basel framework, 278 in expansion through recession in a collateralized debt obligation periods, 43–46 (CDO), 227 mean reversion and, 46–49 and concentration risk, 30–33 summary, 54–55 Cora for different tranches, 230 Correlation and Basel II and III Cora parameter, 182 Basel accords, about, 251–252 and credit risk, 25–27 Basel accords and double default hedging correlation risk, 238 treatment, 269–274 and market risk, 24 Basel II and III credit value at as part of finance risk, 24–33 risk (CVaR) approach, quantification of, 195 252–258 and systemic risk, 27–30 Basel II required capital (RC) for types of, 34–35 credit risk, 258–260 wrong-way risk (WWR), 222 credit value adjustment (CVA) Correlation risk as part of finance with wrong-way risk (WWR), risk, 24–33 264–269 correlation risk and concentration credit value adjustment (CVA) risk, 30–33 without wrong-way risk correlation risk and credit risk, (WWR), 261–264 25–27 debt value adjustment (DVA), correlation risk and market risk, 274–276 24 funding value adjustment (FVA), correlation risk and systemic risk, 276–278 27–30 wrong-way risk quantification, terminology, 33–34 268–269 summary, 34–35 summary, 278–279 Correlation risk in CDO Correlation coefficient, 33–34 Cora of a CDO, 229–230 Correlation concept Gora of a CDO, 230–231 about, 128–129 types of risk, 227–228 loss distribution of, 129–130 Correlation risk parameters Cora tranche spread-correlation and Gora, 182–184 relationship, 130–131 Correlation smile, 87, 136 Correlation desks, 8, 33 Correlation swap, 11–13, 244–245 Correlation hedge, 189 payoff of, 12 Correlation matrix, 75 n1 Correlation swap hedge, 246 Correlation modeling, 144 Correlation trading, 8, 33 3GBINDEX 11/18/2013 16:4:37 Page 319 Index 319 Correlation via stochastic time indexes, 104 change, 148–150 market price risk, 183 Correlation via transition rate payoff tree, 206–208, 216–217 volatilities, 146–147 payoff tree and CDS spread Correlation volatility, 45, 53 payment tree, 219–220 Correlation-dependent option, payoff tree and CDS spread tree, 239–244 210–211 Counter-party default risk, 211 premium tree, 206 Counter-party risk, 102 vs. put option, 247 Counting process, 152 spread, 205 Covariance, 36 spread impact testing, 211–213 Covered put buying, 236 spread payment tree, 217–219 Cox, J., 108, 184 spread tree, 207–210 Cox-Ingersoll-Ross (CIR) process, in synthetic CDO, 103 70, 173, 221 Credit exposure, 266, 268 Credit collection risk Credit products, 183 definition, 202 Credit risk positive vs. negative, 204 collateralized debt obligation Credit correlation, 34 (CDO), 101–102, 227 Credit correlation risk, 231 and correlation risk, 24 Credit correlation risk quantification credit exposure, 266 about, 201–203 credit value at risk (CVaR) in a CDS, 203–205 calculation, 252, 263–264 correlation risk in CDO, 227–231 types of, 201 pricing CDSs including credit Credit triangle, 112 correlations, 215–227 Credit value adjustment (CVA) pricing CDSs with entity- types of correlations, 23, 251 counterparty credit correlation, without wrong-way risk (WWR), 205–215 261–264 summary, 231–232 with wrong-way risk (WWR), Credit counterparty risk, 251 264–269 Credit default swap (CDS). See also Credit value at risk (CVaR) Pricing CDSs Basel accord, 251–253, 271 bond risk, 208–210 copula model, 58 counterparty risk with correlated default risk, 33 correlation-dependent option, default probability PD(T), 257 239–244 definition, 252 in a credit correlation risk valuation, 253 quantification, 203–205 Crossover method, 290 defined, 13 n3 Cumulative default probability, definition,
Load more

Recommended publications
  • Correlation Risk Modeling and Management 3GFFIRS 11/21/2013 17:55:45 Page Ii
    3GFLAST 11/18/2013 11:40:37 Page xviii 3GFFIRS 11/21/2013 17:55:45 Page i Correlation Risk Modeling and Management 3GFFIRS 11/21/2013 17:55:45 Page ii Founde d in 1807, John Wiley & Son s is th e oldest independ ent publ ishing company in the Uni ted States . With of fices in North America , Euro pe, Austral ia, and Asia, Wil ey is global ly committed to develop ing an d market ing print and electron ic products and services for our cu stomers ’ profess ional and personal knowle dge an d unde rstandi ng. The Wil ey Finan ce series contains books written speci fically for financ e and invest ment pr ofessionals as wel l as sophist icated indivi dual investo rs and their financial advisor s. Book topics range from portfol io managem ent to e-comm erce, risk managem ent, finan cial engineer ing, valuation , and fi nancial instrument analysis, as well as much more. For a lis t of available titles, visit our Web site at ww w.Wil eyFinance. com. 3GFFIRS 11/21/2013 17:55:45 Page iii Correlation Risk Modeling and Management An Applied Guide Including the Basel III Correlation Framework— with Interactive Correlation Models in Excel/VBA GUNTER MEISSNER 3GFFIRS 11/21/2013 17:55:45 Page iv Cover image: iStockphoto.com/logoboom Cover design: John Wiley & Sons, Inc. Copyright 2014 by John Wiley & Sons Singapore Pte. Ltd. Published by John Wiley & Sons Singapore Pte. Ltd. 1 Fusionopolis Walk, #07-01, Solaris South Tower, Singapore 138628 All rights reserved.
    [Show full text]
  • Updated Trading Participants' Trading Manual
    Annexure 3 TRADING MANUAL BURSA MALAYSIA DERIVATIVES BHD TRADING MANUAL (Version 4.10) This manual is the intellectual property of BURSA MALAYSIA. No part of the manual is to be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system, without permission in writing from Head of BMD Exchange Operations. TRADING MANUAL Version History Version Date Author Comments V 1.0 9 Aug 2010 BMDB Initial Version V 1.1 27 Aug 2010 BMDB Updated # 6.6.3 Review of Trades – Price Adjustments and Cancellations V1.2 6 Sep 2010 BMDB Inserted 15. Operator ID (“Tag 50 ID”) Required for All BMD orders traded on CME Globex V1.3 9 Sep 2010 BMDB Update 1. Introduction – 1.6 TPs’ compliance in relation to access, connectivity, specification or use of CME Globex V1.4 13 Sep 2010 BMDB Updated 13. Messaging And Market Performance Protection Policy V1.5 9 Nov 2011 BMDB Inserted 16 Negotiated Large Trade V1.6 18 Nov 2011 BMDB Amended section 16 for typo errors, consistency and clarity. V1.7 24 Nov 2011 BMDB Amended section 16 - Extended NLT cut-off time for FKLI, FKB3 and FMG5 to 4.00pm and for FCPO to 5.00pm. -Amended the NLT Facility Trade Registration form. V1.8 10 Feb 2012 BMDB Updated section 11 EFP to EFRP V1.9 23 Mar 2012 BMDB Amended sections 11 and 16 (forms and processes) V2.0 5 Apr 2012 BMDB Renamed to “Trading Manual” V2.1 14 May 2012 BMDB Updated Section 6 for OKLI and to align with CME practice V2.2 29 May 2012 BMDB i) Updated for OCPO ii) Change of terminology to be consistent with CME iii) Updated Sections 7.7 & 14.1 for consistency with Rules iv) Updated Sections 12.3 & 12.4 for accuracy v) Updated Section 3.1 on options naming convention V2.3 18 Feb 2013 BMDB Updated Section 16 NLT V2.4 3 Apr 2013 BMDB Updated Section 9 Circuit Breaker on timing V2.5 2 Jul 2013 BMDB Updated FGLD.
    [Show full text]
  • Three Essays on Variance Risk and Correlation Risk
    Imperial College London Imperial College Business School Three Essays on Variance Risk and Correlation Risk Xianghe Kong Submitted in part fulfilment of the requirements for the degree of Doctor of Philosophy in Finance of Imperial College and the Diploma of Imperial College October 2010 1 Abstract This thesis focuses on variance risk and correlation risk in the equity market, and consists of three essays. The first essay demonstrates that the variance risk, mea- sured as the difference between the realized return variance and its risk-neutral expectation, is an important determinant of the cross-sectional variation of hedge fund returns. Empirical evidence shows that funds with significantly higher loadings on variance risk outperform lower-loading funds on average. However, they incur severe losses during market downturns. Failure to account for variance risk results in overestimation of funds' absolute returns and underestimation of risk. The results provide important implications for hedge fund risk management and performance evaluations. The second essay examines the empirical properties of a widely-used correlation risk proxy, namely the dispersion trade between the index and individual stock options. I find that discrete hedging errors in such trading strategy can result in incorrect inferences on the magnitude of correlation risk premium and render the proxy unreliable as a measure of pure exposure to correlation risk. I implement a dynamic hedging scheme for the dispersion trade, which significantly improves the estimation accuracy of correlation risk and enhances the risk-return profile of the trading strategy. Finally, the third essay aims to forecast the average pair-wise correlations between stocks in the market portfolio.
    [Show full text]
  • Fast Monte Carlo Simulation for Pricing Covariance Swap Under Correlated Stochastic Volatility Models Junmei Ma, Ping He
    IAENG International Journal of Applied Mathematics, 46:3, IJAM_46_3_09 ______________________________________________________________________________________ Fast Monte Carlo Simulation for Pricing Covariance Swap under Correlated Stochastic Volatility Models Junmei Ma, Ping He Abstract—The modeling and pricing of covariance swap with probability 1 − δ, where σn is the standard deviation derivatives under correlated stochastic volatility models are of estimation of V , δ is significance level and Z δ is the 2 studied. The pricing problem of the derivative under the case δ of discrete sampling covariance is mainly discussed, by efficient quantile of standard normal distribution under 2 . The error pσn is Z δ , which just rely on the deviation σ and sample Control Variate Monte Carlo simulation. Based on the closed n 2 form solutions derived for approximate models with correlated storage n.However, although Monte Carlo method is easy deterministic volatility by partial differential equation method, to implement and effective to solve the multi-dimensional two kinds of acceleration methods are therefore proposed when problem, it’s disadvantage is its slow convergence. The the volatility processes obey the Hull-White stochastic processes. − 1 Through analyzing the moments for the underlying processes, convergence rate of Monte Carlo method is O(n 2 ).We the efficient control volatility under the approximate model have to spend 100 times as much as computer time in order is constructed to make sure the high correlation between the to reduce the error by a factor of 10 [8]. control variate and the problem. The numerical results illustrate Many financial practitioners and researchers concentrate the high efficiency of the control variate Monte Carlo method; on the problem of computational efficiency, several ap- the results coincide with the theoretical results.
    [Show full text]
  • Credit Risk Meets Random Matrices: Coping with Non-Stationary Asset Correlations
    Review Credit Risk Meets Random Matrices: Coping with Non-Stationary Asset Correlations Andreas Mühlbacher * and Thomas Guhr Fakultät für Physik, Universität Duisburg-Essen, Lotharstraße 1, 47048 Duisburg, Germany; [email protected] * Correspondence: [email protected] Received: 28 February 2018; Accepted: 19 April 2018; Published: 23 April 2018 Abstract: We review recent progress in modeling credit risk for correlated assets. We employ a new interpretation of the Wishart model for random correlation matrices to model non-stationary effects. We then use the Merton model in which default events and losses are derived from the asset values at maturity. To estimate the time development of the asset values, the stock prices are used, the correlations of which have a strong impact on the loss distribution, particularly on its tails. These correlations are non-stationary, which also influences the tails. We account for the asset fluctuations by averaging over an ensemble of random matrices that models the truly existing set of measured correlation matrices. As a most welcome side effect, this approach drastically reduces the parameter dependence of the loss distribution, allowing us to obtain very explicit results, which show quantitatively that the heavy tails prevail over diversification benefits even for small correlations. We calibrate our random matrix model with market data and show how it is capable of grasping different market situations. Furthermore, we present numerical simulations for concurrent portfolio risks, i.e., for the joint probability densities of losses for two portfolios. For the convenience of the reader, we give an introduction to the Wishart random matrix model.
    [Show full text]
  • Variance Risk: a Bird's Eye View
    Variance Risk: A Bird’s Eye View∗ Fabian Hollstein† and Chardin Wese Simen‡ January 11, 2018 Abstract Prior research documents a significant variance risk premium (VRP) for the S&P 500 index but only for few equities. Using high-frequency data, we show that these results are affected by measurement errors in the realized variance estimates. We decompose the index VRP into factors related to the VRP of equities and the correlation risk pre- mium. The former mostly drives the variations in the index VRP while the latter mainly captures the level of the index VRP. The two factors predict excess stock returns in the time-series and cross-section, but at different horizons. Together, they improve the return predictability. JEL classification: G11, G12 Keywords: Correlation Swaps, Realized Variance, Return Predictability, Variance Swaps ∗We are grateful to Arndt Claußen, Maik Dierkes, Binh Nguyen, Marcel Prokopczuk, Roméo Tédon- gap and seminar participants at Leibniz University Hannover for helpful comments and discussions. Part of this research project was completed when Chardin Wese Simen visited Leibniz University Hannover. †School of Economics and Management, Leibniz University Hannover, Koenigsworther Platz 1, 30167 Hannover, Germany. Contact: [email protected]. ‡ICMA Centre, Henley Business School, University of Reading, Reading, RG6 6BA, UK. Contact: [email protected]. 1 Introduction Carr & Wu(2009) and Driessen et al.(2009) document a significantly negative av- erage variance swap payoff (VSP) for the U.S. stock market index.1 However, they find significant VSPs only for a small number of individual equities that make up the stock index.
    [Show full text]
  • Mathematical Analysis in Investment Theory
    Mathematical Analysis in Investment Theory: Applications to the Nigerian Stock Market NNANWA, Chimezie Peters Available from Sheffield Hallam University Research Archive (SHURA) at: http://shura.shu.ac.uk/25369/ This document is the author deposited version. You are advised to consult the publisher's version if you wish to cite from it. Published version NNANWA, Chimezie Peters (2018). Mathematical Analysis in Investment Theory: Applications to the Nigerian Stock Market. Doctoral, Sheffield Hallam University. Copyright and re-use policy See http://shura.shu.ac.uk/information.html Sheffield Hallam University Research Archive http://shura.shu.ac.uk Mathematical Analysis in Investment Theory: Applications to the Nigerian Stock Market By NNANWA Chimezie Peters Sheffield Hallam University, Sheffield United Kingdom 1 Faculty of Art, Computing, Engineering and Sciences Material and Engineering Research Institute Department of Mathematics and Engineering Mathematical Analysis in Investment Theory: Applications to the Nigerian Stock Market A Dissertation Submitted in Partial Fulfilment of the requirements for the award of PhD Sheffield Hallam University By NNANWA Chimezie Peters Supervisors: Dr Alboul Lyuba (DOS) Prof Jacques Penders October 2018 2 DECLARATION I certify that the substance of this thesis has not been already submitted for any degree and is not currently considering for any other degree. I also certify that to the best of my knowledge any assistance received in preparing this thesis, and all sources used, have been acknowledged and referenced in this thesis. 3 Abstract This thesis intends to optimise a portfolio of assets from the Nigerian Stock Exchange (NSE) using mathematical analysis in the investment theory to model the Nigerian financial market data better.
    [Show full text]
  • Correlation Risk and the Cross Section of Hedge Fund Returns
    ‘When There Is No Place to Hide’: Correlation Risk and the Cross-Section of Hedge Fund Returns ANDREA BURASCHI, ROBERT KOSOWSKI and FABIO TROJANI ABSTRACT This paper analyzes the relation between correlation risk and the cross-section of hedge fund re- turns. Legal framework and investment mandate a¤ect the nature of risks that hedge funds are exposed to: Hedge funds’ability to enter long-short positions can be useful to reduce market beta, but it can severely expose the funds to unexpected changes in correlations. We use a novel dataset on correlation swaps and investigate this link. We …nd a number of interesting results. First, the dy- namics of hedge funds’absolute returns are explained to a statistically and economically signi…cant percentage by exposure to correlation risk. Second, di¤erent exposures to correlation risk explain cross-sectional di¤erences in hedge fund excess returns. Third, Fama-Macbeth regressions highlight that correlation risk carries the largest and most signi…cant risk premium in the cross-section of hedge fund returns. Fourth, exposure to correlation risk is linked to an asymmetric risk pro…le: Funds selling protection against correlation increases have maximum drawdowns much higher than funds buying protection against correlation risk. Fifth, failure to account for correlation risk expo- sures leads to a strongly biased estimation of funds’risk-adjusted performance. These …ndings have implications for hedge fund risk management, the categorization of hedge funds according to their risk pro…le and recent legislation that allows mutual funds to follow so-called 130/30 long-short strategies. JEL classi…cation: D9, E3, E4, G11, G14, G23 Keywords: Stochastic correlation and volatility, hedge fund performance, optimal portfolio choice.
    [Show full text]
  • Options Trading
    OPTIONS TRADING: THE HIDDEN REALITY RI$K DOCTOR GUIDE TO POSITION ADJUSTMENT AND HEDGING Charles M. Cottle ● OPTIONS: PERCEPTION AND DECEPTION and ● COULDA WOULDA SHOULDA revised and expanded www.RiskDoctor.com www.RiskIllustrated.com Chicago © Charles M. Cottle, 1996-2006 All rights reserved. No part of this publication may be printed, reproduced, stored in a retrieval system, or transmitted, emailed, uploaded in any form or by any means, electronic, mechanical photocopying, recording, or otherwise, without the prior written permission of the publisher. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold with the understanding that neither the author or the publisher is engaged in rendering legal, accounting, or other professional service. If legal advice or other expert assistance is required, the services of a competent professional person should be sought. From a Declaration of Principles jointly adopted by a Committee of the American Bar Association and a Committee of Publishers. Published by RiskDoctor, Inc. Library of Congress Cataloging-in-Publication Data Cottle, Charles M. Adapted from: Options: Perception and Deception Position Dissection, Risk Analysis and Defensive Trading Strategies / Charles M. Cottle p. cm. ISBN 1-55738-907-1 ©1996 1. Options (Finance) 2. Risk Management 1. Title HG6024.A3C68 1996 332.63’228__dc20 96-11870 and Coulda Woulda Shoulda ©2001 Printed in the United States of America ISBN 0-9778691-72 First Edition: January 2006 To Sarah, JoJo, Austin and Mom Thanks again to Scott Snyder, Shelly Brown, Brian Schaer for the OptionVantage Software Graphics, Allan Wolff, Adam Frank, Tharma Rajenthiran, Ravindra Ramlakhan, Victor Brancale, Rudi Prenzlin, Roger Kilgore, PJ Scardino, Morgan Parker, Carl Knox and Sarah Williams the angel who revived the Appendix and Chapter 10.
    [Show full text]
  • October 4, 2017 Extent, the Dividend Acts Like an Anchor, Slowing the Stock Down
    In many time periods, dividends accounted for much more than 50% of the S&P 500’s total return. Non- dividend-paying stocks only have one way to recognize value… their share prices must rise. Dividend-paying stocks have two ways to recognize value… regular cash dividend payments, and a rise in their share prices. Howard Silverblatt, a Senior Index Analyst with S&P Dow Jones Indices said: “Dividend stocks have several advantages. Since 1926, dividends have accounted for about 42% of investor re- turns, while being less volatile than the market. To some October 4, 2017 extent, the dividend acts like an anchor, slowing the stock down. The beauty of dividends is that you get paid, In Today’s Issue whether or not the market is up.” • The Dividend Issue We know dividends are a crucial component of long- term investment returns. So, how do you scour the • DIVY—The lone ETF that invests in pure dividend growth. smorgasbord of dividend funds available? • REGL and SMDV—Finding the next Dividend Aristocrats through Mid and small-cap dividend growth powerhouses. The general answer is pick a reputable fund family; a fund with a satisfactory asset level (volume too, if it’s an ETF), decent track record and low expenses and a divi- Finding a Needle(s) in the Dividend Haystack dend strategy that makes sense. Last year, Morningstar reported there were 469 US- One way to shorten the list is to strictly use ETFs. Many listed dividend strategies across the open-end, closed- advisors are doing this. According to FPA’s Trends in In- end and ETF universes.
    [Show full text]
  • Equity Correlation
    Equity correlation – explaining the investment opportunity Barclays Capital provides an overview of the equity derivatives flows and products that contribute to dealers’ correlation exposures, presenting several strategies that allow investors, of all types, to take advantage of the resultant opportunities One of the most talked-about metrics in the world of equity options on higher volatility underlyings (for example, stocks for derivatives currently is correlation. While the basic intuition behind single underlying reverse convertibles) and purchase options the metric is quite straightforward, correlation risks and exposures can on lower volatility underlyings (for example, indices for optically be significant. Indeed, dealers have focused much time and effort into higher participation rates in capital guaranteed notes). managing correlation risk. Moreover, many of these efforts have been While many of the above flows are over the counter and aimed at isolating this risk to make it more efficient to trade. evidence is anecdotal at best, we are confident that these flows are responsible for a bulk of dealers’ short correlation exposures. Correlation is in demand We now consider why and how we think investors should take We believe there are two primary flows that contribute to dealers’ advantage of opportunities arising from this phenomenon. overall short correlation positioning. The first flow comes from structured products and is the most important, in our view. As Opportunities for investors investors generally like diversification and limited losses, they prefer On the back of the above flows and recent sell-offs in the equity products with exposures to multiple underlyings and some sort markets, implied correlation (i.e.
    [Show full text]
  • Equity Correlation Swaps: a New Approach for Modelling & Pricing Columbia University — Financial Engineering Practitioners Seminar
    26 February 2007 Equity Correlation Swaps: A New Approach For Modelling & Pricing Columbia University — Financial Engineering Practitioners Seminar Sebastien Bossu Equity Derivatives Structuring — Product Development Equity Correlation Swaps: A New Approach For Modelling & Pricing Disclaimer This document only reflects the views of the author and not necessarily those of Dresdner Kleinwort research, sales or trading departments. This document is for research or educational purposes only and is not intended to promote any financial investment or security. 2 Equity Correlation Swaps: A New Approach For Modelling & Pricing Introduction X The daily life of an Equity Exotic Structurer (1): X Q: ‘Can you price this payoff: ⎡ ⎤ ⎛ ST − S0 ⎞ min⎢20%,max⎜5%, ⎟⎥ ⎣ ⎝ S0 ⎠⎦ ?’ X A: Hedge is a fixed cash flow and a static call spread: 1 1 5% + max[]0, ST −105%× S0 − max[]0, ST −125%× S0 S0 S0 Price: PV(5%) + 1/S0 x (Call Spread 105-125) 3 Equity Correlation Swaps: A New Approach For Modelling & Pricing Introduction X The daily life of an Equity Exotic Structurer (2): X Q: ‘Can you price this payoff: 2 252 ⎛ St ⎞ ∑⎜ln ⎟ t=1 ⎝ St−1 ⎠ ?’ X A: 1-year variance. Hedge is a static portfolio of puts and calls approximating ln(ST/S0), delta-hedged daily. ⎡ 1 1 +∞ 1 ⎤ Price: 2 Put(k)dk + Call(k)dk ⎢ 2 2 ⎥ ⎣∫0 k ∫1 k ⎦ 4 Equity Correlation Swaps: A New Approach For Modelling & Pricing Introduction X The daily life of an Equity Exotic Structurer (3): X Q: ‘Can you price this payoff: 2 ∑ ρi, j N(N −1) i< j where ρi,j is the coefficient of correlation between stocks i and j observed between t = 0 and t = T?’ X A0: ‘Go home’ X A1: ‘Let me program this into our Monte-Carlo engine’ X A2: ‘Let me ask my broker’ 5 Equity Correlation Swaps: A New Approach For Modelling & Pricing Introduction X Pros and cons X A1: Monte-Carlo gives you a price but not a hedge.
    [Show full text]