A Securitization-Based Model of Shadow Banking with Surplus Extraction and Credit Risk Transfer
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Shadow Banking
Federal Reserve Bank of New York Staff Reports Shadow Banking Zoltan Pozsar Tobias Adrian Adam Ashcraft Hayley Boesky Staff Report No. 458 July 2010 Revised February 2012 FRBNY Staff REPORTS 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 necessar- ily 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 Banking Zoltan Pozsar, Tobias Adrian, Adam Ashcraft, and Hayley Boesky Federal Reserve Bank of New York Staff Reports, no. 458 July 2010: revised February 2012 JEL classification: G20, G28, G01 Abstract The rapid growth of the market-based financial system since the mid-1980s changed the nature of financial intermediation. Within the market-based financial system, “shadow banks” have served a critical role. Shadow banks are financial intermediaries that con- duct maturity, credit, and liquidity transformation without explicit access to central bank liquidity or public sector credit guarantees. Examples of shadow banks include finance companies, asset-backed commercial paper (ABCP) conduits, structured investment vehicles (SIVs), credit hedge funds, money market mutual funds, securities lenders, limited-purpose finance companies (LPFCs), and the government-sponsored enterprises (GSEs). Our paper documents the institutional features of shadow banks, discusses their economic roles, and analyzes their relation to the traditional banking system. Our de- scription and taxonomy of shadow bank entities and shadow bank activities are accom- panied by “shadow banking maps” that schematically represent the funding flows of the shadow banking system. -
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. -
Expected Stock Returns and Volatility Kenneth R
University of Pennsylvania ScholarlyCommons Finance Papers Wharton Faculty Research 1987 Expected Stock Returns and Volatility Kenneth R. French G. William Schwert Robert F. Stambaugh University of Pennsylvania Follow this and additional works at: http://repository.upenn.edu/fnce_papers Part of the Finance Commons, and the Finance and Financial Management Commons Recommended Citation French, K. R., Schwert, G., & Stambaugh, R. F. (1987). Expected Stock Returns and Volatility. Journal of Financial Economics, 19 (1), 3-29. http://dx.doi.org/10.1016/0304-405X(87)90026-2 At the time of publication, author Robert F. Stambaugh was affiliated with the University of Chicago. Currently, he is a faculty member at the Wharton School at the University of Pennsylvania. This paper is posted at ScholarlyCommons. http://repository.upenn.edu/fnce_papers/363 For more information, please contact [email protected]. Expected Stock Returns and Volatility Abstract This paper examines the relation between stock returns and stock market volatility. We find ve idence that the expected market risk premium (the expected return on a stock portfolio minus the Treasury bill yield) is positively related to the predictable volatility of stock returns. There is also evidence that unexpected stock market returns are negatively related to the unexpected change in the volatility of stock returns. This negative relation provides indirect evidence of a positive relation between expected risk premiums and volatility. Disciplines Finance | Finance and Financial Management Comments At the time of publication, author Robert F. Stambaugh was affiliated with the University of Chicago. Currently, he is a faculty member at the Wharton School at the University of Pennsylvania. -
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”. -
Estimating Value at Risk
Estimating Value at Risk Eric Marsden <[email protected]> Do you know how risky your bank is? Learning objectives 1 Understand measures of financial risk, including Value at Risk 2 Understand the impact of correlated risks 3 Know how to use copulas to sample from a multivariate probability distribution, including correlation The information presented here is pedagogical in nature and does not constitute investment advice! Methods used here can also be applied to model natural hazards 2 / 41 Warmup. Before reading this material, we suggest you consult the following associated slides: ▷ Modelling correlations using Python ▷ Statistical modelling with Python Available from risk-engineering.org 3 / 41 Risk in finance There are 1011 stars in the galaxy. That used to be a huge number. But it’s only a hundred billion. It’s less than the national deficit! We used to call them astronomical numbers. ‘‘ Now we should call them economical numbers. — Richard Feynman 4 / 41 Terminology in finance Names of some instruments used in finance: ▷ A bond issued by a company or a government is just a loan • bond buyer lends money to bond issuer • issuer will return money plus some interest when the bond matures ▷ A stock gives you (a small fraction of) ownership in a “listed company” • a stock has a price, and can be bought and sold on the stock market ▷ A future is a promise to do a transaction at a later date • refers to some “underlying” product which will be bought or sold at a later time • example: farmer can sell her crop before harvest, -
Risk, Return, and Diversification a Reading Prepared by Pamela Peterson Drake
Risk, return, and diversification A reading prepared by Pamela Peterson Drake O U T L I N E 1. Introduction 2. Diversification and risk 3. Modern portfolio theory 4. Asset pricing models 5. Summary 1. Introduction As managers, we rarely consider investing in only one project at one time. Small businesses and large corporations alike can be viewed as a collection of different investments, made at different points in time. We refer to a collection of investments as a portfolio. While we usually think of a portfolio as a collection of securities (stocks and bonds), we can also think of a business in much the same way -- a portfolios of assets such as buildings, inventories, trademarks, patents, et cetera. As managers, we are concerned about the overall risk of the company's portfolio of assets. Suppose you invested in two assets, Thing One and Thing Two, having the following returns over the next year: Asset Return Thing One 20% Thing Two 8% Suppose we invest equal amounts, say $10,000, in each asset for one year. At the end of the year we will have $10,000 (1 + 0.20) = $12,000 from Thing One and $10,000 (1 + 0.08) = $10,800 from Thing Two, or a total value of $22,800 from our original $20,000 investment. The return on our portfolio is therefore: ⎛⎞$22,800-20,000 Return = ⎜⎟= 14% ⎝⎠$20,000 If instead, we invested $5,000 in Thing One and $15,000 in Thing Two, the value of our investment at the end of the year would be: Value of investment =$5,000 (1 + 0.20) + 15,000 (1 + 0.08) = $6,000 + 16,200 = $22,200 and the return on our portfolio would be: ⎛⎞$22,200-20,000 Return = ⎜⎟= 11% ⎝⎠$20,000 which we can also write as: Risk, return, and diversification, a reading prepared by Pamela Peterson Drake 1 ⎡⎤⎛⎞$5,000 ⎡ ⎛⎞$15,000 ⎤ Return = ⎢⎥⎜⎟(0.2) +=⎢ ⎜⎟(0.08) ⎥ 11% ⎣⎦⎝⎠$20,000 ⎣ ⎝⎠$20,000 ⎦ As you can see more immediately by the second calculation, the return on our portfolio is the weighted average of the returns on the assets in the portfolio, where the weights are the proportion invested in each asset. -
The Cross-Section of Volatility and Expected Returns
THE JOURNAL OF FINANCE • VOL. LXI, NO. 1 • FEBRUARY 2006 The Cross-Section of Volatility and Expected Returns ANDREW ANG, ROBERT J. HODRICK, YUHANG XING, and XIAOYAN ZHANG∗ ABSTRACT We examine the pricing of aggregate volatility risk in the cross-section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. Stocks with high idiosyncratic volatility relative to the Fama and French (1993, Journal of Financial Economics 25, 2349) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book-to-market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility. IT IS WELL KNOWN THAT THE VOLATILITY OF STOCK RETURNS varies over time. While con- siderable research has examined the time-series relation between the volatility of the market and the expected return on the market (see, among others, Camp- bell and Hentschel (1992) and Glosten, Jagannathan, and Runkle (1993)), the question of how aggregate volatility affects the cross-section of expected stock returns has received less attention. Time-varying market volatility induces changes in the investment opportunity set by changing the expectation of fu- ture market returns, or by changing the risk-return trade-off. If the volatility of the market return is a systematic risk factor, the arbitrage pricing theory or a factor model predicts that aggregate volatility should also be priced in the cross-section of stocks. -
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]). -
CHAPTER 7 Smart Excel Appendix Appendix Contents
CHAPTER 7 Smart Excel Appendix Appendix Contents Excel prerequisites Relative, absolute, and mixed cell references, covered in Chapter 4 Learn to solve for Expected stock returns using the historical approach Expected stock returns using the probabilistic approach Expected stock returns using the CAPM approach Expected portfolio return and risk measures Use the Smart Excel spreadsheets and animated tutorials at the Smart Finance section of http://www.cengage.co.uk/megginson. EXCEL PREREQUISITES There are no new Excel features used in this appendix, but there is extensive use of absolute and mixed cell references. This material is reviewed and extended on the Excel Prereqs tab of the Chapter 7 file located at the Smart Finance Web site. EXPECTED RETURN Problem: You are considering investing in four companies and want to determine the expected return on each investment, if held on its own, over the next year. You are interested in comparing the expected return using three approaches: historical, probabilistic, and CAPM. You also want to find the return on a portfolio invested equally in each of the four investments. Last, you want to assess the risk of the potential investments. Information on the investments and current market conditions is provided here and below. You have return information for the 1964–2003 period. The current Treasury bill rate is 2.2%, and the average Treasury bill return over the period 1964–2003 was 4.1%. The expected return on a market portfolio is 11%. You want to consider returns under three possible economic scenarios over the next year: weak economy, average economy, strong economy. -
Speculation and Risk in Foreign Exchange Markets
Chapter 7 Speculation and Risk in the Foreign Exchange Market © 2018 Cambridge University Press 7-1 7.1 Speculating in the Foreign Exchange Market • Uncovered foreign money market investments • Kevin Anthony, a portfolio manager, is considering several ways to invest $10M for 1 year • The data are as follows: • USD interest rate: 8.0% p.a.; GBP interest rate: 12.0% p.a.; Spot: $1.60/£ • Remember that if Kevin invests in the USD-denominated asset at 8%, after 1 year he will have × 1.08 = $10. • What if Kevin invests his $10M in the pound money market, but decides not to hedge the foreign$10 exchange risk?8 © 2018 Cambridge University Press 7-2 7.1 Speculating in the Foreign Exchange Market • As before, we can calculate his dollar return in three steps: • Convert dollars into pounds in the spot market • The $10M will buy /($1.60/£) = £6. at the current spot exchange rate • This is Kevin’s pound principal. $10 25 • Calculate pound-denominated interest plus principal • Kevin can invest his pound principal at 12% yielding a return in 1 year of £6. × 1.12 = £7 • Sell the pound principal plus interest at the spot exchange rate in 1 year 25 • 1 = × ( + 1, $/£) ( + 1) = ( + 1) × ( / ) – (1 + 0.08) • £7 £7 $10 © 2018 Cambridge University Press 7-3 Return and Excess Return in Foreign Market • We can use the previous calculation to deduce a formula for calculating the return in the foreign market, 1 rt( +=1) ×+( 1it( ,£)) × St( + 1) St( ) • The return on the British investment is uncertain because of exchange rate uncertainty.