Shadow Banking
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Five Years After Dodd-Frank: Unintended Consequences and Room for Improvement
University of Pennsylvania ScholarlyCommons Wharton Public Policy Initiative Issue Briefs Wharton Public Policy Initiative 12-2015 Five Years after Dodd-Frank: Unintended Consequences and Room for Improvement David A. Skeel University of Pennsylvania Law School, [email protected] Follow this and additional works at: https://repository.upenn.edu/pennwhartonppi Part of the Economic Policy Commons, and the Public Policy Commons Recommended Citation Skeel, David A., "Five Years after Dodd-Frank: Unintended Consequences and Room for Improvement" (2015). Wharton Public Policy Initiative Issue Briefs. 11. https://repository.upenn.edu/pennwhartonppi/11 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/pennwhartonppi/11 For more information, please contact [email protected]. Five Years after Dodd-Frank: Unintended Consequences and Room for Improvement Summary This brief offers a 5-year retrospective on Dodd-Frank, pointing out aspects of the legislation that would benefit from correction or amendment. Dodd-Frank has yielded several key surprises—in particular, the problematic extent to which the Federal Reserve has become the primary regulator of the financial industry. The author offers several recommendations including: clarification of the rules yb which strategically important financial institutions (SIFIs) are identified; overhauling the incentives offered to banks; instituting bankruptcy reforms that would discourage government bailouts; and easing regulatory burdens on smaller banks that are disproportionately -
How Does Monetary Policy Affect Shadow Bank Money Creation? I
How Does Monetary Policy Aect Shadow Bank Money Creation? ∗ Kairong Xiaoy June 17, 2016 Abstract This paper studies the impact of monetary policy on money creation of the shadow banking system. Using the U.S. money supply data over the past thirty years, I nd that shadow banks behave in the opposite way to commercial banks: shadow banks create more money exactly when the Fed tightens monetary policy to reduce money supply. Using a structural model of bank competition, I show that this phenomenon can be explained by clientele heterogeneity between the shadow and commercial banking sector. Monetary tightening allows commercial banks to charge higher prices on their depository services by driving up the opportunity cost of using cash. However, shadow banks cannot do so because their main clientele are more yield-sensitive. As a result, monetary tightening makes shadow bank money cheaper than commercial bank money, which drives marginal depositors of commercial banks to switch to shadow banks. My nding cautions against using monetary tightening to address nancial stability concerns, as it may unintentionally expand the shadow banking sector. ∗I am grateful to my thesis advisors Adlai Fisher, Lorenzo Garlappi, Carolin Pueger, and Francesco Trebbi for their generous support and guidance. I also benet from helpful comments from Markus Baldauf, Paul Beaudry, Jan Bena, Murray Carlson, Ron Giammarino, Will Gornall, Tan Wang, and seminar participants at the University of British Columbia. All errors are my own. ySauder School of Business, University of British Columbia. Email: [email protected] 1 1 Introduction Economists have traditionally focused on the role of commercial banks in the transmission of monetary policy. -
Users/Robertjfrey/Documents/Work
AMS 511.01 - Foundations Class 11A Robert J. Frey Research Professor Stony Brook University, Applied Mathematics and Statistics [email protected] http://www.ams.sunysb.edu/~frey/ In this lecture we will cover the pricing and use of derivative securities, covering Chapters 10 and 12 in Luenberger’s text. April, 2007 1. The Binomial Option Pricing Model 1.1 – General Single Step Solution The geometric binomial model has many advantages. First, over a reasonable number of steps it represents a surprisingly realistic model of price dynamics. Second, the state price equations at each step can be expressed in a form indpendent of S(t) and those equations are simple enough to solve in closed form. 1+r D-1 u 1 1 + r D 1 + r D y y ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ = u fl u = 1+r D u-1 u 1+r-u 1 u 1 u yd yd ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ1+r D ÅÅÅÅÅÅÅÅ1 uêÅÅÅÅ-ÅÅÅÅu ÅÅ H L H ê L i y As we will see shortlyH weL Hwill solveL the general problemj by solving a zsequence of single step problems on the lattice. That K O K O K O K O j H L H ê L z sequence solutions can be efficientlyê computed because wej only have to zsolve for the state prices once. k { 1.2 – Valuing an Option with One Period to Expiration Let the current value of a stock be S(t) = 105 and let there be a call option with unknown price C(t) on the stock with a strike price of 100 that expires the next three month period. -
Regulation Shadow Banking
CNMV ADVISORY COMMITTEE RESPONSE TO THE FSB CONSULTATIVE DOCUMENTS: A POLICY FRAMEWORK FOR STRENGTHENING OVERSIGHT AND REGULATION OF SHADOW BANKING ENTITIES AND A POLICY FRAMEWORK FOR ADDRESSING SHADOW BANKING RISKS IN SECURITIES LENDING AND REPOS The CNMV's Advisory Commit tee has been set by the Spanish Securities Market Law as the consultative body of the CNMV. This Committee is composed by market participants (members of secondary markets, issuers, retail investors, intermediaries, the collective investment industry, etc) andRegulating its opinions areshadow independent banking from those of the CNMV. Outline 1.The shadow banking system. 1.1. Definition and importance of the shadow banking system. 1.2. The growth of the shadow banking system. 2. Regulating the shadow banking system. 2.1. Reasons for regulating shadow banking. 2.2. Potential regulatory strategies. 2.3. Reflections on differences in regulation across jurisdictions. Regulation in Spain. 3. The regulatory proposals of the FSB. 3.1. Comments on “A Policy Framework for Strengthening Oversight and Regulation of Shadow Banking Entities”. 3.2. Comments on ““A Policy Framework for Addressing Shadow Banking Risks in Securities Lending and Repos”. References 1 1. The shadow banking system 1.1. Definition and importance of the shadow banking system There are many alternative definitions of shadow banking. The Financial Stability Board (FSB) defines shadow banking as “credit intermediation involving entities and activities outside the regular banking system”, but other authors give complementary definitions that emphasize different aspects of shadow banking. For example: • Adrian and Ashcraft (2012) say it is “a web of specialized financial institutions that channel funding from savers to investors through a range of securitization and secured funding techniques”. -
A Securitization-Based Model of Shadow Banking with Surplus Extraction and Credit Risk Transfer
A Securitization-based Model of Shadow Banking with Surplus Extraction and Credit Risk Transfer Patrizio Morganti∗ August, 2017 Abstract The paper provides a theoretical model that supports the search for yield motive of shadow banking and the traditional risk transfer view of securitization, which is consistent with the factual background that had characterized the U.S. financial system before the recent crisis. The shadow banking system is indeed an important provider of high-yield asset-backed securi- ties via the underlying securitized credit intermediation process. Investors' sentiment on future macroeconomic conditions affects the reservation prices related to the demand for securitized assets: high-willing payer (\optimistic") investors are attracted to these investment opportu- nities and offer to intermediaries a rent extraction incentive. When the outside wealth is high enough that securitization occurs, asset-backed securities are used by intermediaries to extract the highest feasible surplus from optimistic investors and to offload credit risk. Shadow banking is pro-cyclical and securitization allows risks to be spread among market participants coherently with their risk attitude. Keywords: securitization, shadow banking, credit risk transfer, surplus extraction JEL classification: E44, G21, G23 1 Introduction During the last four decades we witnessed to fundamental changes in financial techniques and financial regulation that have gradually transformed the \originate-to-hold" banking model into a \originate-to-distribute" model based on a securitized credit intermediation process relying upon i) securitization techniques, ii) securities financing transactions, and iii) mutual funds industry.1 ∗Tuscia University in Viterbo, Department of Economics and Engineering. E-mail: [email protected]. I am most grateful to Giuseppe Garofalo for his continuous guidance and support. -
Credit Derivatives in Managing Off Balance Sheet Risks by Banks
City University Business School MSc in Finance 2001 Credit Derivatives in Managing Off Balance Sheet Risks by Banks Submitted by Murat Cakir Supervisor: Giorgio S. Questa This project is submitted as part of the requirements for the award of the MSc in Finance. July 2001 ABSTRACT Credit risk has been a worrying type of risk for financial managers. Fortunately, a recent market development –credit derivatives- has made the credit risk more manageable. The loan portfolio management has become more practicable than it used to be in the past. However, credit derivatives are still not well examined. There are uncertainties about and difficulties in the pricing and portfolio management of credit derivatives due to the non-normality in probability distribution of credit risk. Various models have been developed for credit derivatives pricing. After having drawn the general picture for the credit derivatives, we have studied some recent pricing models in a Das (1999) framework, in this study. Also appended is a an attempt to a step forward for simulating the risk-free rates and spreads, to test how powerful simulation can be in modeling the credit risk and pricing of it. Moreover, with highly developed computer technology, it is possible to make sensitivity analysis under several scenarios, to form imaginary loan portfolios, find their risk exposures, and perform a successful risk management practice. COPYRIGHT MURAT ÇAKIR Central Bank of the Republic of Turkey. All rights reserved. No part of this work 2 may be reproduced, stored in a retrieval system, transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except formal use of reference to the author without the written permission thereof Acknowledgements I am deeply grateful to my supervisor Mr. -
Put/Call Parity
Execution * Research 141 W. Jackson Blvd. 1220A Chicago, IL 60604 Consulting * Asset Management The grain markets had an extremely volatile day today, and with the wheat market locked limit, many traders and customers have questions on how to figure out the futures price of the underlying commodities via the options market - Or the synthetic value of the futures. Below is an educational piece that should help brokers, traders, and customers find that synthetic value using the options markets. Any questions please do not hesitate to call us. Best Regards, Linn Group Management PUT/CALL PARITY Despite what sometimes seems like utter chaos and mayhem, options markets are in fact, orderly and uniform. There are some basic and easy to understand concepts that are essential to understanding the marketplace. The first and most important option concept is called put/call parity . This is simply the relationship between the underlying contract and the same strike, put and call. The formula is: Call price – put price + strike price = future price* Therefore, if one knows any two of the inputs, the third can be calculated. This triangular relationship is the cornerstone of understanding how options work and is true across the whole range of out of the money and in the money strikes. * To simplify the formula we have assumed no dividends, no early exercise, interest rate factors or liquidity issues. By then using this concept of put/call parity one can take the next step and create synthetic positions using options. For example, one could buy a put and sell a call with the same exercise price and expiration date which would be the synthetic equivalent of a short future position. -
Shadow Banking Concerns: the Case of Money Market Funds∗
Shadow Banking Concerns: The Case of Money Market Funds∗ Saad Alnahedh† , Sanjai Bhagat‡ Abstract Implosion of the Money Market Fund (MMF) industry in 2008 has raised alarms about MMF risk-taking; inevitably drawing the attention of financial regulators. Regulations were announced by the U.S. Securities and Exchange Commission (SEC) in July 2014 to increase MMF disclosures, lower incentives to take risks, and reduce the probability of future investor runs on the funds. The new regulations allowed MMFs to impose liquidity gates and fees, and required institutional prime MMFs to adopt a floating (mark-to-market) net asset value (NAV), starting October 2016. Using novel data compiled from algorithmic text-analysis of security-level MMF portfolio holdings, as reported to the SEC, this paper examines the impact of these reforms. Using a difference-in-differences analysis, we find that institutional prime funds responded to this regulation by significantly increasing risk of their portfolios, while simultaneously increasing holdings of opaque securities. Large bank affiliated MMFs hold the riskiest portfolios. This evidence suggests that the MMF reform of October 2016 has not been effective in curbing MMF risk-taking behavior; importantly, MMFs still pose a systemic risk to the economy given large banks’ significant exposure to them. We propose a two-pronged solution to the MMF risk-taking behavior. First, the big bank sponsoring the MMF should have sufficient equity capitalization. Second, the compensation incentives of the big bank managers and directors should be focused on creating and sustaining long-term bank shareholder value. JEL classification: G20, G21, G23, G24, G28 Keywords: Money Market Funds, MMFs, Shadow Banking, SEC Reform, Bank Governance, Bank Capital, Executive Compensation ∗We thank Tony Cookson, Robert Dam, David Scharfstein, and Edward Van Wesep for constructive comments on earlier drafts of this paper. -
Shadow Bank Monitoring
Federal Reserve Bank of New York Staff Reports Shadow Bank Monitoring Tobias Adrian Adam B. Ashcraft Nicola Cetorelli Staff Report No. 638 September 2013 This paper presents preliminary findings and is being distributed to economists and other interested readers solely to stimulate discussion and elicit comments. The views expressed in this paper are those of the authors and are not necessarily reflective of views at the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors. Shadow Bank Monitoring Tobias Adrian, Adam B. Ashcraft, and Nicola Cetorelli Federal Reserve Bank of New York Staff Reports, no. 638 September 2013 JEL classification: E44, G00, G01, G28 Abstract We provide a framework for monitoring the shadow banking system. The shadow banking system consists of a web of specialized financial institutions that conduct credit, maturity, and liquidity transformation without direct, explicit access to public backstops. The lack of such access to sources of government liquidity and credit backstops makes shadow banks inherently fragile. Shadow banking activities are often intertwined with core regulated institutions such as bank holding companies, security brokers and dealers, and insurance companies. These interconnections of shadow banks with other financial institutions create sources of systemic risk for the broader financial system. We describe elements of monitoring risks in the shadow banking system, including recent efforts by the Financial Stability Board. Key words: shadow banking, financial stability monitoring, financial intermediation _________________ Adrian, Ashcraft, Cetorelli: Federal Reserve Bank of New York (e-mails: [email protected], [email protected], [email protected]). -
1 1 2 FINANCIAL CRISIS INQUIRY COMMISSION 3 4 Official Transcript
1 1 2 3 FINANCIAL CRISIS INQUIRY COMMISSION 4 5 Official Transcript 6 Hearing on "The Shadow Banking System" 7 Wednesday, May 5, 2010 8 Dirksen Senate Office Building, Room 538 9 Washington, D.C. 10 9:00 A.M. 11 12 COMMISSIONERS 13 PHIL ANGELIDES, Chairman 14 HON. BILL THOMAS, Vice Chairman 15 BROOKSLEY BORN, Commissioner 16 BYRON S. GEORGIOU, Commissioner 17 HON. BOB GRAHAM, Commissioner 18 KEITH HENNESSEY, Commissioner 19 DOUGLAS HOLTZ-EAKIN, Commissioner 20 HEATHER H. MURREN, Commissioner 21 JOHN W. THOMPSON, Commissioner 22 PETER J. WALLISON, Commissioner 23 24 Reported by: JANE W. BEACH 25 PAGES 1 - 369 2 1 Session 1: Investment Banks and the Shadow Banking System: 2 PAUL FRIEDMAN, Former Chief Operating Officer of 3 Fixed Income, Bear Stearns 4 SAMUEL MOLINARO, JR., Former Chief Financial 5 Officer and Chief Operating Officer, Bear Stearns 6 WARREN SPECTOR, former President and 7 Co-Chief Operating Officer, Bear Stearns 8 9 Session 2: Investment Banks and the Shadow Banking System: 10 JAMES E. CAYNE, Former Chairman and 11 Chief Executive Officer, Bear Stearns 12 ALAN D. SCHWARTZ, Former Chief Executive Officer, 13 Bear Stearns 14 15 16 17 18 19 20 21 22 23 24 3 1 Session 3: SEC Regulation of Investment Banks: 2 CHARLES CHRISTOPHER COX, Former Chairman 3 U.S. Securities and Exchange Commission 4 WILLIAM H. DONALDSON, Former Chairman, U.S. 5 Securities and Exchange Commission 6 H. DAVID KOTZ, Inspector General 7 U.S. Securities and Exchange Commission 8 ERIK R. SIRRI, Former Director, Division of 9 Trading & Markets, U.S. -
What Drives the Shadow Banking System in the Short and Long Run? John V
What Drives the Shadow Banking System in the Short and Long Run? John V. Duca Federal Reserve Bank of Dallas Research Department Working Paper 1401 What Drives the Shadow Banking System in the Short and Long Run? John V. Duca* Associate Director of Research and Vice President Research Department, Federal Reserve Bank of Dallas P.O. Box 655906, Dallas, TX 75265 (214) 922-5154, [email protected] and Adjunct Professor, Southern Methodist University, Dallas, TX February 2014 This paper analyzes how risk and other factors altered the relative use of short-term business debt funded by the shadow banking system since the early 1960s. Results indicate that the share was affected over the long-run not only by changing information and reserve requirement costs, but also by shifts in the impact of regulations on bank versus nonbank credit sources—such as Basel I in 1990 and reregulation in 2010. In the short-run, the shadow share rose when deposit interest rate ceilings were binding, the economic outlook improved, or risk premia declined, and fell when event risks disrupted financial markets. JEL Codes: E44, E50, N12 Key Words: Shadow Banking, Regulation, Financial Frictions, Credit Rationing * I thank Elizabeth Organ and Michael Weiss for comments, J.B. Cooke for excellent research assistance and for their comments on a summary of earlier and less complete findings in this paper, participants at the 16th Annual International Banking Conference, Shadow Banking: Within and Across National Borders, co-sponsored by the Federal Reserve Bank of Chicago and the International Monetary Fund. The views expressed are those of the author and are not necessarily those of the Federal Reserve Bank of Dallas or the Federal Reserve System. -
Implied Index and Option Pricing Errors: Evidence from the Taiwan Option Market
The International Journal of Business and Finance Research ♦ Volume 5 ♦ Number 2 ♦ 2011 IMPLIED INDEX AND OPTION PRICING ERRORS: EVIDENCE FROM THE TAIWAN OPTION MARKET Ching-Ping Wang, National Kaohsiung University of Applied Sciences Hung-Hsi Huang, National Pingtung University of Science and Technology Chien-Chia Hung, National Pingtung University of Science and Technology ABSTRACT This study examines both restricted and unrestricted Black-Sholes models, according to Longstaff (1995). Using the Taiwan index options for each day from January 2005 to December 2008, the unrestricted model simultaneously solves the implied index value and implied volatility whereas the restricted model only solves the implied volatility. Next, this study compares the pricing performance of restricted and unrestricted Black-Scholes models. The empirical results show he implied index value is almost higher than the actual index value. Moneyness has a significant negative impact on the index pricing error for calls but negative impact for puts. Open interest has a significantly negative impact on the index pricing error for calls. Volatility for calls has no significant effect on the index pricing error. The path-dependent effect on index pricing error increases with index returns. The unrestricted model has significantly less option pricing bias for calls than the restricted model. The option pricing error for calls in the restricted model has much larger negative bias near the middle maturity. The R-square in the restricted model is always much larger than the unrestricted model for both calls and puts. Finally, the option pricing errors are significantly affected by moneyness and time to expiration for all cases; this fact is consistent with Longstaff (1995).