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Log Normal Asset TRS Funding
Liability-side Pricing of Swaps and Coherent CVA and FVA by Regression/Simulation Lou Wujiang1 1st Draft, August 8, 2014. Updated Dec 20, 2015. Abstract An uncollateralized swap hedged back-to-back by a CCP swap is used to introduce FVA. The open IR01 of FVA, however, is a sure sign of risk not being fully hedged, a theoretical no-arbitrage pricing concern, and a bait to lure market risk capital, a practical business concern. By dynamically trading the CCP swap, with the liability-side counterparty provides counterparty exposure hedge and swap funding, we find that the uncollateralized swap can be fully replicated, leaving out no IR01 leakage. The fair value of the swap is obtained by applying to swap’s net cash flows a discount rate switching to counterparty’s bond curve if the swap is a local asset or one’s own curve if a liability, and the total valuation adjustment is the present value of cost of funding the risk-free price discounted at the same switching rate. FVA is redefined as a liquidity or funding basis component of total valuation adjustment, coherent with CVA, the default risk component. A Longstaff-Schwartz style least-square regression and simulation is introduced to compute the recursive fair value and adjustments. A separately developed finite difference scheme is used to test and find regression necessary to decouple the discount rate switch. Preliminary results show the impact of counterparty risk to swap hedge ratios, swap bid/ask spreads, and valuation adjustments, and considerable errors of calculating CVA by discounting cash flow or potential future exposure. -
Credit Derivatives Handbook
08 February 2007 Fixed Income Research http://www.credit-suisse.com/researchandanalytics Credit Derivatives Handbook Credit Strategy Contributors Ira Jersey +1 212 325 4674 [email protected] Alex Makedon +1 212 538 8340 [email protected] David Lee +1 212 325 6693 [email protected] This is the second edition of our Credit Derivatives Handbook. With the continuous growth of the derivatives market and new participants entering daily, the Handbook has become one of our most requested publications. Our goal is to make this publication as useful and as user friendly as possible, with information to analyze instruments and unique situations arising from market action. Since we first published the Handbook, new innovations have been developed in the credit derivatives market that have gone hand in hand with its exponential growth. New information included in this edition includes CDS Orphaning, Cash Settlement of Single-Name CDS, Variance Swaps, and more. We have broken the information into several convenient sections entitled "Credit Default Swap Products and Evaluation”, “Credit Default Swaptions and Instruments with Optionality”, “Capital Structure Arbitrage”, and “Structure Products: Baskets and Index Tranches.” We hope this publication is useful for those with various levels of experience ranging from novices to long-time practitioners, and we welcome feedback on any topics of interest. FOR IMPORTANT DISCLOSURE INFORMATION relating to analyst certification, the Firm’s rating system, and potential conflicts -
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. -
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. -
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. -
The 3Rd Fixed Income Conference
THE 3RD FIXED INCOME CONFERENCE NH Grand Hotel Krasnapolsky, Amsterdam, The Netherlands 20th / 21st / 22nd September 2006 The 3rd Fixed Income Conference Due to the huge success of our previous two Fixed Income Conferences in Prague, WBS Training are pleased to announce that we will be heading to Amsterdam in September 2006 The three streamed format will be retained, with Credit Derivatives, Interest Rate Modelling and Hybrid Products streams. This year we will also have four workshops on Wednesday 20th September; Credit Derivatives Modelling, Credit Hybrids, Interest Rate Modelling & Interest Rate Hybrids. Gold Sponsors: Silver Sponsors: The 3rd Fixed Income Conference Presenter List: Claudio Albanese: Chair of Mathematical Finance, Imperial College London Jesper Andreasen: Head of Fixed Income Quantitative Research, Bank of America Alexandre Antonov NumeriX, Vice President of Quantitative Research Przemyslaw Bachert: Senior Financial Engineer, Global Financial Services Risk Management, Ernst & Young Martin Baxter: Analyst, Fixed Income Quantitative Research, Nomura International, plc Joerg Behrens Partner, Ernst & Young Nordine Bennani: Head of Credit Derivative Quantitative Research, DrKW Damiano Brigo: Head of Credit Models, Banca IMI Aaron Brown: Head of Credit Risk Architecture, Morgan Stanley Christian Fries DZ Bank Dariusz Gatarek: Glencore Helyette Geman: Professor of Mathematical Finance, Birkbeck,University of London and ESSEC Business School Jon Gregory: Global Credit Derivatives: Barclays Capital Patrick Hagan: Brevan Howard -
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. -
Credit Derivatives Handbook
Corporate Quantitative Research New York, London December, 2006 Credit Derivatives Handbook Detailing credit default swap products, markets and trading strategies About this handbook This handbook reviews both the basic concepts and more advanced Corporate Quantitative Research trading strategies made possible by the credit derivatives market. Readers AC seeking an overview should consider Sections 1.1 - 1.3, and 8.1. Eric Beinstein (1-212) 834-4211 There are four parts to this handbook: [email protected] Andrew Scott, CFA Part I: Credit default swap fundamentals 5 (1-212) 834-3843 [email protected] Part I introduces the CDS market, its participants, and the mechanics of the credit default swap. This section provides intuition about the CDS Ben Graves, CFA valuation theory and reviews how CDS is valued in practice. Nuances of (1-212) 622-4195 [email protected] the standard ISDA documentation are discussed, as are developments in documentation to facilitate settlement following credit events. Alex Sbityakov (1-212) 834-3896 Part II: Valuation and trading strategies 43 [email protected] Katy Le Part II provides a comparison of bonds and credit default swaps and (1-212) 834-4276 discusses why CDS to bond basis exists. The theory behind CDS curve [email protected] trading is analyzed, and equal-notional, duration-weighted, and carry- neutral trading strategies are reviewed. Credit versus equity trading European Credit Derivatives Research strategies, including stock and CDS, and equity derivatives and CDS, are analyzed. Jonny Goulden (44-20) 7325-9582 Part III: Index products 111 [email protected] The CDX and iTraxx products are introduced, valued and analyzed. -
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. -
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. -
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. -
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.