Securitization and Derivatives
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Analysis of Securitized Asset Liquidity June 2017 an He and Bruce Mizrach1
Analysis of Securitized Asset Liquidity June 2017 An He and Bruce Mizrach1 1. Introduction This research note extends our prior analysis2 of corporate bond liquidity to the structured products markets. We analyze data from the TRACE3 system, which began collecting secondary market trading activity on structured products in 2011. We explore two general categories of structured products: (1) real estate securities, including mortgage-backed securities in residential housing (MBS) and commercial building (CMBS), collateralized mortgage products (CMO) and to-be-announced forward mortgages (TBA); and (2) asset-backed securities (ABS) in credit cards, autos, student loans and other miscellaneous categories. Consistent with others,4 we find that the new issue market for securitized assets decreased sharply after the financial crisis and has not yet rebounded to pre-crisis levels. Issuance is below 2007 levels in CMBS, CMOs and ABS. MBS issuance had recovered by 2012 but has declined over the last four years. By contrast, 2016 issuance in the corporate bond market was at a record high for the fifth consecutive year, exceeding $1.5 trillion. Consistent with the new issue volume decline, the median age of securities being traded in non-agency CMO are more than ten years old. In student loans, the average security is over seven years old. Over the last four years, secondary market trading volumes in CMOs and TBA are down from 14 to 27%. Overall ABS volumes are down 16%. Student loan and other miscellaneous ABS declines balance increases in automobiles and credit cards. By contrast, daily trading volume in the most active corporate bonds is up nearly 28%. -
Asset Securitization
L-Sec Comptroller of the Currency Administrator of National Banks Asset Securitization Comptroller’s Handbook November 1997 L Liquidity and Funds Management Asset Securitization Table of Contents Introduction 1 Background 1 Definition 2 A Brief History 2 Market Evolution 3 Benefits of Securitization 4 Securitization Process 6 Basic Structures of Asset-Backed Securities 6 Parties to the Transaction 7 Structuring the Transaction 12 Segregating the Assets 13 Creating Securitization Vehicles 15 Providing Credit Enhancement 19 Issuing Interests in the Asset Pool 23 The Mechanics of Cash Flow 25 Cash Flow Allocations 25 Risk Management 30 Impact of Securitization on Bank Issuers 30 Process Management 30 Risks and Controls 33 Reputation Risk 34 Strategic Risk 35 Credit Risk 37 Transaction Risk 43 Liquidity Risk 47 Compliance Risk 49 Other Issues 49 Risk-Based Capital 56 Comptroller’s Handbook i Asset Securitization Examination Objectives 61 Examination Procedures 62 Overview 62 Management Oversight 64 Risk Management 68 Management Information Systems 71 Accounting and Risk-Based Capital 73 Functions 77 Originations 77 Servicing 80 Other Roles 83 Overall Conclusions 86 References 89 ii Asset Securitization Introduction Background Asset securitization is helping to shape the future of traditional commercial banking. By using the securities markets to fund portions of the loan portfolio, banks can allocate capital more efficiently, access diverse and cost- effective funding sources, and better manage business risks. But securitization markets offer challenges as well as opportunity. Indeed, the successes of nonbank securitizers are forcing banks to adopt some of their practices. Competition from commercial paper underwriters and captive finance companies has taken a toll on banks’ market share and profitability in the prime credit and consumer loan businesses. -
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. -
Understanding Securitization
Understanding Securitization It is important to have a general familiarity with mortgage securitization in order to understand the foreclosure process. Securitization involves a series of conveyances of the note evidencing the residential loan and assignment of the mortgage or trust deed securing it. Therefore, chain of title and beneficial interest issues frequently turn on the securitization trajectories. Securitization is the process pooling loans into “mortgage‐ backed securities” or “MBS” for sale to investors. MBS is an investment instrument backed by an undivided interest in a pool of mortgages or trust deeds. Income from the underlying mortgages is used to pay interest and principal on the securities. Figure A below is a simplified schematic depicting the general securitization process and some of the parties involved. The process begins with Originators, which are the lenders (such as banks or finance companies) that initially make the loans to homeowners. Sponsor/Sellers (or “sponsors”) purchase these loans from one or more Originators to form the pool of assets to be securitized. (Most large financial institutions are both Originators and Sponsor/Sellers.) A Depositor creates a Securitization Trust, a special‐purpose entity, for the securitized transaction. The depositor acquires the pooled assets from the Sponsor/Seller and in turn deposits them into the Securitization Trust. An Issuer acquires the Securitization Trust and issues certificates to eventually be sold to investors. However, the Issuer does not directly offer the certificates for sale to the investors. Instead, the Issuer conveys the certificate to the Depositor in exchange for the pooled assets. An Underwriter, usually an investment bank, purchases all of the certificates from the Depositor with the responsibility of offering to them for sale to the ultimate investors. -
Ginnie Mae: How Does It Work and What Does It Do?
Economic Policy Program Housing Commission Ginnie Mae: How Does it Work and What Does it Do? The Government National Mortgage What Does Ginnie Mae Do? Association (or Ginnie Mae) is a Ginnie Mae only guarantees securities created by approved government corporation within the issuers and backed by mortgages covered by other federal U.S. Department of Housing and programs. Urban Development (HUD). It was The Ginnie Mae guarantee ensures that investors in these established in 1968 when Fannie mortgage-backed securities (MBS) do not experience any Mae was privatized. Its mission is disruption of the timely payment of principal and interest, thus shielding them from losses resulting from borrower to expand funding for mortgages defaults. As a result, these MBS appeal to a wide range that are insured or guaranteed by of investors and trade at a price similar to that of U.S. other federal agencies. When these government bonds of comparable maturity. However, the guarantee also places Ginnie Mae, and ultimately American mortgages are bundled into securities, taxpayers, at risk of losses not covered by the other federal Ginnie Mae provides a full-faith-and- programs (see When Does a Ginnie Mae Guarantee Apply?). credit guarantee on these securities, thus lessening the risk for investors and broadening the market for the securities. WWW.BIPARTISANPOLICY.ORG When Does a Ginnie Mae Why Does Ginnie Mae Use the Term Guarantee Apply? “Issuer/Servicer?” Ginnie Mae guarantees MBS backed by loans covered Ginnie Mae MBS can include mortgages by the following programs: purchased from multiple originators n The Federal Housing Administration’s single-family and multifamily mortgage insurance programs, which and serviced by third parties. -
Housing and the Financial Crisis
This PDF is a selection from a published volume from the National Bureau of Economic Research Volume Title: Housing and the Financial Crisis Volume Author/Editor: Edward L. Glaeser and Todd Sinai, editors Volume Publisher: University of Chicago Press Volume ISBN: 978-0-226-03058-6 Volume URL: http://www.nber.org/books/glae11-1 Conference Date: November 17-18, 2011 Publication Date: August 2013 Chapter Title: The Future of the Government-Sponsored Enterprises: The Role for Government in the U.S. Mortgage Market Chapter Author(s): Dwight Jaffee, John M. Quigley Chapter URL: http://www.nber.org/chapters/c12625 Chapter pages in book: (p. 361 - 417) 8 The Future of the Government- Sponsored Enterprises The Role for Government in the US Mortgage Market Dwight Jaffee and John M. Quigley 8.1 Introduction The two large government- sponsored housing enterprises (GSEs),1 the Federal National Mortgage Association (“Fannie Mae”) and the Federal Home Loan Mortgage Corporation (“Freddie Mac”), evolved over three- quarters of a century from a single small government agency, to a large and powerful duopoly, and ultimately to insolvent institutions protected from bankruptcy only by the full faith and credit of the US government. From the beginning of 2008 to the end of 2011, the two GSEs lost capital of $266 billion, requiring draws of $188 billion under the Treasured Preferred Stock Purchase Agreements to remain in operation; see Federal Housing Finance Agency (2011). This downfall of the two GSEs was primarily a question of “when,” not “if,” given that their structure as a public/private Dwight Jaffee is the Willis Booth Professor of Banking, Finance, and Real Estate at the University of California, Berkeley. -
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]. -
Appraisal and Property Related Faqs
Appraisal and Property-Related Frequently Asked Questions Updated August 2021 This FAQ document provides responses to common questions related to Fannie Mae’s property eligibility and appraisal policies. Table of Contents Resources .............................................................................................................................................................................................. 1 FAQs ....................................................................................................................................................................................................... 1 Property Eligibility ............................................................................................................................................................................. 1 Appraiser Selection and Management ............................................................................................................................................. 2 Appraisal Submission and Forms ..................................................................................................................................................... 3 Appraisal Policy ................................................................................................................................................................................. 4 Resources For additional information about Fannie Mae’s appraisal policies, refer to the Selling Guide. Other resources are available on the Appraisers page on Fannie Mae’s -
Fruzsina Mák Volume Risk in the Power Market
FRUZSINA MÁK VOLUME RISK IN THE POWER MARKET Department of Statistics Supervisors: Beatrix Oravecz, Senior lecturer, Ph.D. András Sugár, Associate professor, Head of Department of Statistics, Ph.D. Copyright © Fruzsina Mák, 2017 Corvinus University of Budapest Doctoral Programme of Management and Business Administration Volume risk in the power market Load profiling considering uncertainty Ph.D. Dissertation Fruzsina Mák Budapest, 2017 TABLE OF CONTENTS INTRODUCTION ................................................................................................................. 2 1. APPLICATION EXAMPLES AND TERMINOLOGY OF CONSUMER PROFILING ...................................................................................................................................... 12 1.1. Price- and volume uncertainty on the energy market ............................................ 12 1.2. Some application examples on profiling ............................................................... 19 1.2.1. Short and long term hedging and pricing ....................................................... 19 1.2.2. Demand side management ............................................................................. 22 1.2.3. Building portfolios and creating balancing groups ........................................ 24 1.3. Profile and profile-related risks ............................................................................. 26 1.3.1. Definition of consumer profile ....................................................................... 26 1.3.2. -
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. -
Securitization & Hedge Funds
SECURITIZATION & HEDGE FUNDS: COLLATERALIZED FUND OBLIGATIONS SECURITIZATION & HEDGE FUNDS: CREATING A MORE EFFICIENT MARKET BY CLARK CHENG, CFA Intangis Funds AUGUST 6, 2002 INTANGIS PAGE 1 SECURITIZATION & HEDGE FUNDS: COLLATERALIZED FUND OBLIGATIONS TABLE OF CONTENTS INTRODUCTION........................................................................................................................................ 3 PROBLEM.................................................................................................................................................... 4 SOLUTION................................................................................................................................................... 5 SECURITIZATION..................................................................................................................................... 5 CASH-FLOW TRANSACTIONS............................................................................................................... 6 MARKET VALUE TRANSACTIONS.......................................................................................................8 ARBITRAGE................................................................................................................................................ 8 FINANCIAL ENGINEERING.................................................................................................................... 8 TRANSPARENCY...................................................................................................................................... -
Private Mortgage Securitization and Loan Origination Quality - New Evidence from Loan Losses
Private Mortgage Securitization and Loan Origination Quality - New Evidence from Loan Losses Abdullah Yavas Robert E. Wangard Chair School of Business University of Wisconsin - Madison Madison, WI 53706 [email protected] and Shuang Zhu Associate Professor Department of Finance Kansas State University Manhattan, KS 66506 [email protected] December 11, 2018 1 Private Mortgage Securitization and Loan Origination Quality - New Evidence from Loan Losses Abstract Due to data constraints, earlier studies of the impact of securitization on loan quality have used default probability as a proxy for loan quality. In this paper, we utilize a unique data set that allows us to use loan losses, which incorporate both probability of default and loss given default, to proxy for mortgage quality. Our analysis of prime loans shows that higher expected loan losses are associated with higher probability of securitization. Lenders sell prime loans with lower observable quality and keep higher observable quality loans on their books. For subprime loans, we observe opposite results that lenders sell better quality loans and keep lower quality loans on their book. We then use the cutoff FICO score of 620 to infer the lender’s screening effort with respect to unobservable loan quality. We find that securitized prime loans exhibit no significant difference in default losses for 620- versus 620+ loans. However, securitized subprime loans with a 620- score incur significantly lower loan losses than securitized subprime loans with a 620+ score. By using loan losses as the proxy of loan quality, separating the analysis into prime and subprime samples, and distinguishing between observable and unobservable risk characteristics, this study sheds additional light on the potential channels that the securitization affects loan quality.