Optimal Structuring of Collateralized Debt Obligation Contracts: an Optimization Approach
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The TIPS-Treasury Bond Puzzle
The TIPS-Treasury Bond Puzzle Matthias Fleckenstein, Francis A. Longstaff and Hanno Lustig The Journal of Finance, October 2014 Presented By: Rafael A. Porsani The TIPS-Treasury Bond Puzzle 1 / 55 Introduction The TIPS-Treasury Bond Puzzle 2 / 55 Introduction (1) Treasury bond and the Treasury Inflation-Protected Securities (TIPS) markets: two of the largest and most actively traded fixed-income markets in the world. Find that there is persistent mispricing on a massive scale across them. Treasury bonds are consistently overpriced relative to TIPS. Price of a Treasury bond can exceed that of an inflation-swapped TIPS issue exactly matching the cash flows of the Treasury bond by more than $20 per $100 notional amount. One of the largest examples of arbitrage ever documented. The TIPS-Treasury Bond Puzzle 3 / 55 Introduction (2) Use TIPS plus inflation swaps to create synthetic Treasury bond. Price differences between the synthetic Treasury bond and the nominal Treasury bond: arbitrage opportunities. Average size of the mispricing: 54.5 basis points, but can exceed 200 basis points for some pairs. I The average size of this mispricing is orders of magnitude larger than transaction costs. The TIPS-Treasury Bond Puzzle 4 / 55 Introduction (3) What drives the mispricing? Slow-moving capital may help explain why mispricing persists. Is TIPS-Treasury mispricing related to changes in capital available to hedge funds? Answer: Yes. Mispricing gets smaller as more capital gets to the hedge fund sector. The TIPS-Treasury Bond Puzzle 5 / 55 Introduction (4) Also find that: Correlation in arbitrage strategies: size of TIPS-Treasury arbitrage is correlated with arbitrage mispricing in other markets. -
How Much Do Banks Use Credit Derivatives to Reduce Risk?
How much do banks use credit derivatives to reduce risk? Bernadette A. Minton, René Stulz, and Rohan Williamson* June 2006 This paper examines the use of credit derivatives by US bank holding companies from 1999 to 2003 with assets in excess of one billion dollars. Using the Federal Reserve Bank of Chicago Bank Holding Company Database, we find that in 2003 only 19 large banks out of 345 use credit derivatives. Though few banks use credit derivatives, the assets of these banks represent on average two thirds of the assets of bank holding companies with assets in excess of $1 billion. To the extent that banks have positions in credit derivatives, they tend to be used more for dealer activities than for hedging activities. Nevertheless, a majority of the banks that use credit derivative are net buyers of credit protection. Banks are more likely to be net protection buyers if they engage in asset securitization, originate foreign loans, and have lower capital ratios. The likelihood of a bank being a net protection buyer is positively related to the percentage of commercial and industrial loans in a bank’s loan portfolio and negatively or not related to other types of bank loans. The use of credit derivatives by banks is limited because adverse selection and moral hazard problems make the market for credit derivatives illiquid for the typical credit exposures of banks. *Respectively, Associate Professor, The Ohio State University; Everett D. Reese Chair of Banking and Monetary Economics, The Ohio State University and NBER; and Associate Professor, Georgetown University. We are grateful to Jim O’Brien and Mark Carey for discussions. -
Credit-Market Sentiment and the Business Cycle
Credit-Market Sentiment and the Business Cycle David L´opez-Salido∗ Jeremy C. Stein† Egon Zakrajˇsek‡ First draft: April 2015 This draft: December 2016 Abstract Using U.S. data from 1929 to 2015, we show that elevated credit-market sentiment in year t−2 is associated with a decline in economic activity in years t and t + 1. Underlying this result is the existence of predictable mean reversion in credit-market conditions. When credit risk is aggressively priced, spreads subsequently widen. The timing of this widening is, in turn, closely tied to the onset of a contraction in economic activity. Exploring the mechanism, we find that buoyant credit-market sentiment in year t−2 also forecasts a change in the composition of exter- nal finance: Net debt issuance falls in year t, while net equity issuance increases, consistent with the reversal in credit-market conditions leading to an inward shift in credit supply. Unlike much of the current literature on the role of financial frictions in macroeconomics, this paper suggests that investor sentiment in credit markets can be an important driver of economic fluctuations. JEL Classification: E32, E44, G12 Keywords: credit-market sentiment; financial stability; business cycles We are grateful to Olivier Blanchard, Claudia Buch, Bill English, Robin Greenwood, Sam Hanson, Oscar` Jord`a, Larry Katz, Arvind Krishnamurthy, H´el`ene Rey, Andrei Shleifer, the referees, and seminar participants at numerous institutions for helpful comments. Miguel Acosta, Ibraheem Catovic, Gregory Cohen, George Gu, Shaily Patel, and Rebecca Zhang provided outstanding research assistance. The views expressed in this paper are solely the responsibility of the authors and should not be interpreted as reflecting the views of the Board of Governors of the Federal Reserve System or of anyone else associated with the Federal Reserve System. -
Implied Volatility Modeling
Implied Volatility Modeling Sarves Verma, Gunhan Mehmet Ertosun, Wei Wang, Benjamin Ambruster, Kay Giesecke I Introduction Although Black-Scholes formula is very popular among market practitioners, when applied to call and put options, it often reduces to a means of quoting options in terms of another parameter, the implied volatility. Further, the function σ BS TK ),(: ⎯⎯→ σ BS TK ),( t t ………………………………(1) is called the implied volatility surface. Two significant features of the surface is worth mentioning”: a) the non-flat profile of the surface which is often called the ‘smile’or the ‘skew’ suggests that the Black-Scholes formula is inefficient to price options b) the level of implied volatilities changes with time thus deforming it continuously. Since, the black- scholes model fails to model volatility, modeling implied volatility has become an active area of research. At present, volatility is modeled in primarily four different ways which are : a) The stochastic volatility model which assumes a stochastic nature of volatility [1]. The problem with this approach often lies in finding the market price of volatility risk which can’t be observed in the market. b) The deterministic volatility function (DVF) which assumes that volatility is a function of time alone and is completely deterministic [2,3]. This fails because as mentioned before the implied volatility surface changes with time continuously and is unpredictable at a given point of time. Ergo, the lattice model [2] & the Dupire approach [3] often fail[4] c) a factor based approach which assumes that implied volatility can be constructed by forming basis vectors. Further, one can use implied volatility as a mean reverting Ornstein-Ulhenbeck process for estimating implied volatility[5]. -
Module 6 Option Strategies.Pdf
zerodha.com/varsity TABLE OF CONTENTS 1 Orientation 1 1.1 Setting the context 1 1.2 What should you know? 3 2 Bull Call Spread 6 2.1 Background 6 2.2 Strategy notes 8 2.3 Strike selection 14 3 Bull Put spread 22 3.1 Why Bull Put Spread? 22 3.2 Strategy notes 23 3.3 Other strike combinations 28 4 Call ratio back spread 32 4.1 Background 32 4.2 Strategy notes 33 4.3 Strategy generalization 38 4.4 Welcome back the Greeks 39 5 Bear call ladder 46 5.1 Background 46 5.2 Strategy notes 46 5.3 Strategy generalization 52 5.4 Effect of Greeks 54 6 Synthetic long & arbitrage 57 6.1 Background 57 zerodha.com/varsity 6.2 Strategy notes 58 6.3 The Fish market Arbitrage 62 6.4 The options arbitrage 65 7 Bear put spread 70 7.1 Spreads versus naked positions 70 7.2 Strategy notes 71 7.3 Strategy critical levels 75 7.4 Quick notes on Delta 76 7.5 Strike selection and effect of volatility 78 8 Bear call spread 83 8.1 Choosing Calls over Puts 83 8.2 Strategy notes 84 8.3 Strategy generalization 88 8.4 Strike selection and impact of volatility 88 9 Put ratio back spread 94 9.1 Background 94 9.2 Strategy notes 95 9.3 Strategy generalization 99 9.4 Delta, strike selection, and effect of volatility 100 10 The long straddle 104 10.1 The directional dilemma 104 10.2 Long straddle 105 10.3 Volatility matters 109 10.4 What can go wrong with the straddle? 111 zerodha.com/varsity 11 The short straddle 113 11.1 Context 113 11.2 The short straddle 114 11.3 Case study 116 11.4 The Greeks 119 12 The long & short straddle 121 12.1 Background 121 12.2 Strategy notes 122 12..3 Delta and Vega 128 12.4 Short strangle 129 13 Max pain & PCR ratio 130 13.1 My experience with option theory 130 13.2 Max pain theory 130 13.3 Max pain calculation 132 13.4 A few modifications 137 13.5 The put call ratio 138 13.6 Final thoughts 140 zerodha.com/varsity CHAPTER 1 Orientation 1.1 – Setting the context Before we start this module on Option Strategy, I would like to share with you a Behavioral Finance article I read couple of years ago. -
Mortgage-Backed Securities & Collateralized Mortgage Obligations
Mortgage-backed Securities & Collateralized Mortgage Obligations: Prudent CRA INVESTMENT Opportunities by Andrew Kelman,Director, National Business Development M Securities Sales and Trading Group, Freddie Mac Mortgage-backed securities (MBS) have Here is how MBSs work. Lenders because of their stronger guarantees, become a popular vehicle for finan- originate mortgages and provide better liquidity and more favorable cial institutions looking for investment groups of similar mortgage loans to capital treatment. Accordingly, this opportunities in their communities. organizations like Freddie Mac and article will focus on agency MBSs. CRA officers and bank investment of- Fannie Mae, which then securitize The agency MBS issuer or servicer ficers appreciate the return and safety them. Originators use the cash they collects monthly payments from that MBSs provide and they are widely receive to provide additional mort- homeowners and “passes through” the available compared to other qualified gages in their communities. The re- principal and interest to investors. investments. sulting MBSs carry a guarantee of Thus, these pools are known as mort- Mortgage securities play a crucial timely payment of principal and inter- gage pass-throughs or participation role in housing finance in the U.S., est to the investor and are further certificates (PCs). Most MBSs are making financing available to home backed by the mortgaged properties backed by 30-year fixed-rate mort- buyers at lower costs and ensuring that themselves. Ginnie Mae securities are gages, but they can also be backed by funds are available throughout the backed by the full faith and credit of shorter-term fixed-rate mortgages or country. The MBS market is enormous the U.S. -
Tax Treatment of Derivatives
United States Viva Hammer* Tax Treatment of Derivatives 1. Introduction instruments, as well as principles of general applicability. Often, the nature of the derivative instrument will dictate The US federal income taxation of derivative instruments whether it is taxed as a capital asset or an ordinary asset is determined under numerous tax rules set forth in the US (see discussion of section 1256 contracts, below). In other tax code, the regulations thereunder (and supplemented instances, the nature of the taxpayer will dictate whether it by various forms of published and unpublished guidance is taxed as a capital asset or an ordinary asset (see discus- from the US tax authorities and by the case law).1 These tax sion of dealers versus traders, below). rules dictate the US federal income taxation of derivative instruments without regard to applicable accounting rules. Generally, the starting point will be to determine whether the instrument is a “capital asset” or an “ordinary asset” The tax rules applicable to derivative instruments have in the hands of the taxpayer. Section 1221 defines “capital developed over time in piecemeal fashion. There are no assets” by exclusion – unless an asset falls within one of general principles governing the taxation of derivatives eight enumerated exceptions, it is viewed as a capital asset. in the United States. Every transaction must be examined Exceptions to capital asset treatment relevant to taxpayers in light of these piecemeal rules. Key considerations for transacting in derivative instruments include the excep- issuers and holders of derivative instruments under US tions for (1) hedging transactions3 and (2) “commodities tax principles will include the character of income, gain, derivative financial instruments” held by a “commodities loss and deduction related to the instrument (ordinary derivatives dealer”.4 vs. -
Hedging Credit Index Tranches Investigating Versions of the Standard Model Christopher C
Hedging credit index tranches Investigating versions of the standard model Christopher C. Finger chris.fi[email protected] Risk Management Subtle company introduction www.riskmetrics.com Risk Management 22 Motivation A standard model for credit index tranches exists. It is commonly acknowledged that the common model is flawed. Most of the focus is on the static flaw: the failure to calibrate to all tranches on a single day with a single model parameter. But these are liquid derivatives. Models are not used for absolute pricing, but for relative value and hedging. We will focus on the dynamic flaws of the model. www.riskmetrics.com Risk Management 32 Outline 1 Standard credit derivative products 2 Standard models, conventions and abuses 3 Data and calibration 4 Testing hedging strategies 5 Conclusions www.riskmetrics.com Risk Management 42 Standard products Single-name credit default swaps Contract written on a set of reference obligations issued by one firm Protection seller compensates for losses (par less recovery) in the event of a default. Protection buyer pays a periodic premium (spread) on the notional amount being protected. Quoting is on fair spread, that is, spread that makes a contract have zero upfront value at inception. www.riskmetrics.com Risk Management 52 Standard products Credit default swap indices (CDX, iTraxx) Contract is essentially a portfolio of (125, for our purposes) equally weighted CDS on a standard basket of firms. Protection seller compensates for losses (par less recovery) in the event of a default. Protection buyer pays a periodic premium (spread) on the remaining notional amount being protected. New contracts (series) are introduced every six months. -
Credit Derivative Handbook 2003 – 16 April 2003
16 April 2003 Credit Derivative CREDIT Chris Francis (44) 20 7995-4445 [email protected] Handbook 2003 Atish Kakodkar (44) 20 7995-8542 A Guide to Products, Valuation, Strategies and Risks [email protected] Barnaby Martin (44) 20 7995-0458 [email protected] Europe The Key Piece In The Puzzle V ol a Risk til t ity Managemen rate rpo Co ds Bon Capital Structure CreditCredit DerivativesDerivatives Arbitrage F irst D To efa dit ult Cre ed Link tes No Convertible Bonds Derivatives Refer to important disclosures at the end of this report. Merrill Lynch Global Securities Research & Economics Group Investors should assume that Merrill Lynch is Global Fundamental Equity Research Department seeking or will seek investment banking or other business relationships with the companies in this report. RC#60910601 Credit Derivative Handbook 2003 – 16 April 2003 CONTENTS n Section Page Market Evolution: Going 1. Market growth, importance and prospects 3 Mainstream? Credit Default Swap Basics 2. The basic concepts 10 Valuation of Credit Default 3. Arbitrage relationship and an introduction to survival probabilities 13 Swaps Unwinding Default Swaps 4. Mechanics for terminating contracts 19 Valuing the CDS Basis 5. Comparing CDS with the cash market 29 What Drives the Basis? 6. Why do cash and default market yields diverge? 35 CDS Investor Strategies 7. Using CDS to enhance returns 44 CDS Structural Roadmap 8. Key structural considerations 60 What Price Restructuring? 9. How to value the Restructuring Credit Event 73 First-to-Default Baskets 10. A guide to usage and valuation 84 Synthetic CDO Valuation 11. Mechanics and investment rationale 95 Counterparty Risk 12. -
YOUR QUICK START GUIDE to OPTIONS SUCCESS.Pages
YOUR QUICK START GUIDE TO OPTIONS SUCCESS Don Fishback IMPORTANT DISCLOSURE INFORMATION Fishback Management and Research, Inc. (FMR), its principles and employees reserve the right to, and indeed do, trade stocks, mutual funds, op?ons and futures for their own accounts. FMR, its principals and employees will not knowingly trade in advance of the general dissemina?on of trading ideas and recommenda?ons. There is, however, a possibility that when trading for these proprietary accounts, orders may be entered, which are opposite or otherwise different from the trades and posi?ons described herein. This may occur as a result of the use of different trading systems, trading with a different degree of leverage, or tes?ng of new trading systems, among other reasons. The results of any such trading are confiden?al and are not available for inspec?on. This publica?on, in whole or in part, may not be reproduced, retransmiDed, disseminated, sold, distributed, published, broadcast or circulated to anyone without the express prior wriDen permission of FMR except by bona fide news organiza?ons quo?ng brief passages for purposes of review. Due to the number of sources from which the informa?on contained in this newsleDer is obtained, and the inherent risks of distribu?on, there may be omissions or inaccuracies in such informa?on and services. FMR, its employees and contributors take every reasonable step to insure the integrity of the data. However, FMR, its owners and employees and contributors cannot and do not warrant the accuracy, completeness, currentness or fitness for a par?cular purpose of the informa?on contained in this newsleDer. -
Derivative Securities
2. DERIVATIVE SECURITIES Objectives: After reading this chapter, you will 1. Understand the reason for trading options. 2. Know the basic terminology of options. 2.1 Derivative Securities A derivative security is a financial instrument whose value depends upon the value of another asset. The main types of derivatives are futures, forwards, options, and swaps. An example of a derivative security is a convertible bond. Such a bond, at the discretion of the bondholder, may be converted into a fixed number of shares of the stock of the issuing corporation. The value of a convertible bond depends upon the value of the underlying stock, and thus, it is a derivative security. An investor would like to buy such a bond because he can make money if the stock market rises. The stock price, and hence the bond value, will rise. If the stock market falls, he can still make money by earning interest on the convertible bond. Another derivative security is a forward contract. Suppose you have decided to buy an ounce of gold for investment purposes. The price of gold for immediate delivery is, say, $345 an ounce. You would like to hold this gold for a year and then sell it at the prevailing rates. One possibility is to pay $345 to a seller and get immediate physical possession of the gold, hold it for a year, and then sell it. If the price of gold a year from now is $370 an ounce, you have clearly made a profit of $25. That is not the only way to invest in gold. -
Derivative Securities, Fall 2012 – Homework 3. Distributed 10/10
Derivative Securities, Fall 2012 { Homework 3. Distributed 10/10. This HW set is long, so I'm allowing 3 weeks: it is due by classtime on 10/31. Typos in pbm 3 (ambiguity in the payoff of a squared call) and pbm 6 (inconsistent notation for the dividend rate) have been corrected here. Problem 1 provides practice with lognormal statistics. Problems 2-6 explore the conse- quences of our formula for the value of an option as the discounted risk-neutral expected payoff. Problem 7 makes sure you have access to a numerical tool for playing with the Black-Scholes formula and the associated \Greeks." Problem 8 reinforces the notion of \implied volatility." Convention: when we say a process Xt is \lognormal with drift µ and volatility σ" we mean 2 ln Xt − ln Xs is Gaussian with mean µ(t − s) and variance σ (t − s) for all s < t; here µ and σ are constant. In this problem set, Xt will be either a stock price or a forward price. The interest rate is always r, assumed constant. (1) Suppose a stock price st is lognormal with drift µ and volatility σ, and the stock price now is s0. (a) Give a 95% confidence interval for sT , using the fact that with 95% confidence, a Gaussian random variable lies within 1:96 standard deviations of its mean. (b) Give the mean and variance of sT . (c) Give a formula for the likelihood that a call with strike K and maturity T will be in-the-money at maturity.