Arbitrage Pricing Theory
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MPFD Lesson 2B: Meeting Financial Goals—Rate of Return
Unit 2 Planning and Tracking Lesson 2B: Meeting Financial Goals—Rate of Return Rule 2: Have a Plan. Financial success depends primarily on two things: (i) developing a plan to meet your established goals and (ii) tracking your progress with respect to that plan. Too often peo - ple set vague goals (“I want to be rich.”), make unrealistic plans, or never bother to assess the progress toward their goals. These lessons look at important financial indicators you should understand and monitor both in setting goals and attaining them. Lesson Description Students are shown the two ways investments can earn a return and then calculate the annual rate of return, the real rate of return, and the expected rate of return on various assets. Standards and Benchmarks (see page 49) Grade Level 9-12 Concepts Appreciation Depreciation Expected rate of return Inflation Inflation rate Rate of return Real rate of return Return Making Personal Finance Decisions ©2019, Minnesota Council on Economic Education. Developed in partnership with the Federal Reserve Bank of St. Louis. Permission is granted to reprint or photocopy this lesson in its entirety for educational purposes, provided the user credits the Minnesota Council on Economic Education. 37 Unit 2: Planning and Tracking Lesson 2B: Meeting Financial Goals—Rate of Return Compelling Question How is an asset’s rate of return measured? Objectives Students will be able to • describe two ways that an asset can earn a return and • determine and distinguish among the rate of return on an asset, its real rate of return, and its expected rate of return. -
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. -
Financial Literacy and Portfolio Diversification
WORKING PAPER NO. 212 Financial Literacy and Portfolio Diversification Luigi Guiso and Tullio Jappelli January 2009 University of Naples Federico II University of Salerno Bocconi University, Milan CSEF - Centre for Studies in Economics and Finance DEPARTMENT OF ECONOMICS – UNIVERSITY OF NAPLES 80126 NAPLES - ITALY Tel. and fax +39 081 675372 – e-mail: [email protected] WORKING PAPER NO. 212 Financial Literacy and Portfolio Diversification Luigi Guiso and Tullio Jappelli Abstract In this paper we focus on poor financial literacy as one potential factor explaining lack of portfolio diversification. We use the 2007 Unicredit Customers’ Survey, which has indicators of portfolio choice, financial literacy and many demographic characteristics of investors. We first propose test-based indicators of financial literacy and document the extent of portfolio under-diversification. We find that measures of financial literacy are strongly correlated with the degree of portfolio diversification. We also compare the test-based degree of financial literacy with investors’ self-assessment of their financial knowledge, and find only a weak relation between the two measures, an issue that has gained importance after the EU Markets in Financial Instruments Directive (MIFID) has required financial institutions to rate investors’ financial sophistication through questionnaires. JEL classification: E2, D8, G1 Keywords: Financial literacy, Portfolio diversification. Acknowledgements: We are grateful to the Unicredit Group, and particularly to Daniele Fano and Laura Marzorati, for letting us contribute to the design and use of the UCS survey. European University Institute and CEPR. Università di Napoli Federico II, CSEF and CEPR. Table of contents 1. Introduction 2. The portfolio diversification puzzle 3. The data 4. -
The Wisdom of the Robinhood Crowd
NBER WORKING PAPER SERIES THE WISDOM OF THE ROBINHOOD CROWD Ivo Welch Working Paper 27866 http://www.nber.org/papers/w27866 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 September 2020, Revised December 2020 The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications. © 2020 by Ivo Welch. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. The Wisdom of the Robinhood Crowd Ivo Welch NBER Working Paper No. 27866 September 2020, Revised December 2020 JEL No. D9,G11,G4 ABSTRACT Robinhood (RH) investors collectively increased their holdings in the March 2020 COVID bear market, indicating an absence of panic and margin calls. Their steadfastness was rewarded in the subsequent bull market. Despite unusual interest in some “experience” stocks, their aggregated consensus portfolio (likely mimicking the household-equal-weighted portfolio) primarily tilted towards stocks with high past share volume and dollar-trading volume. These were mostly big stocks. Both their timing and their consensus portfolio performed well from mid-2018 to mid-2020. Ivo Welch Anderson School at UCLA (C519) 110 Westwood Place (951481) Los Angeles, CA 90095-1482 and NBER [email protected] The online retail brokerage company Robinhood (RH) was founded in 2013 based on a business plan to make it easier and cheaper for small investors to participate in the stock and option markets. -
An Overview of the Empirical Asset Pricing Approach By
AN OVERVIEW OF THE EMPIRICAL ASSET PRICING APPROACH BY Dr. GBAGU EJIROGHENE EMMANUEL TABLE OF CONTENT Introduction 1 Historical Background of Asset Pricing Theory 2-3 Model and Theory of Asset Pricing 4 Capital Asset Pricing Model (CAPM): 4 Capital Asset Pricing Model Formula 4 Example of Capital Asset Pricing Model Application 5 Capital Asset Pricing Model Assumptions 6 Advantages associated with the use of the Capital Asset Pricing Model 7 Hitches of Capital Pricing Model (CAPM) 8 The Arbitrage Pricing Theory (APT): 9 The Arbitrage Pricing Theory (APT) Formula 10 Example of the Arbitrage Pricing Theory Application 10 Assumptions of the Arbitrage Pricing Theory 11 Advantages associated with the use of the Arbitrage Pricing Theory 12 Hitches associated with the use of the Arbitrage Pricing Theory (APT) 13 Actualization 14 Conclusion 15 Reference 16 INTRODUCTION This paper takes a critical examination of what Asset Pricing is all about. It critically takes an overview of its historical background, the model and Theory-Capital Asset Pricing Model and Arbitrary Pricing Theory as well as those who introduced/propounded them. This paper critically examines how securities are priced, how their returns are calculated and the various approaches in calculating their returns. In this Paper, two approaches of asset Pricing namely Capital Asset Pricing Model (CAPM) as well as the Arbitrage Pricing Theory (APT) are examined looking at their assumptions, advantages, hitches as well as their practical computation using their formulae in their examination as well as their computation. This paper goes a step further to look at the importance Asset Pricing to Accountants, Financial Managers and other (the individual investor). -
Arbitrage Pricing Theory∗
ARBITRAGE PRICING THEORY∗ Gur Huberman Zhenyu Wang† August 15, 2005 Abstract Focusing on asset returns governed by a factor structure, the APT is a one-period model, in which preclusion of arbitrage over static portfolios of these assets leads to a linear relation between the expected return and its covariance with the factors. The APT, however, does not preclude arbitrage over dynamic portfolios. Consequently, applying the model to evaluate managed portfolios contradicts the no-arbitrage spirit of the model. An empirical test of the APT entails a procedure to identify features of the underlying factor structure rather than merely a collection of mean-variance efficient factor portfolios that satisfies the linear relation. Keywords: arbitrage; asset pricing model; factor model. ∗S. N. Durlauf and L. E. Blume, The New Palgrave Dictionary of Economics, forthcoming, Palgrave Macmillan, reproduced with permission of Palgrave Macmillan. This article is taken from the authors’ original manuscript and has not been reviewed or edited. The definitive published version of this extract may be found in the complete The New Palgrave Dictionary of Economics in print and online, forthcoming. †Huberman is at Columbia University. Wang is at the Federal Reserve Bank of New York and the McCombs School of Business in the University of Texas at Austin. The views stated here are those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of New York or the Federal Reserve System. Introduction The Arbitrage Pricing Theory (APT) was developed primarily by Ross (1976a, 1976b). It is a one-period model in which every investor believes that the stochastic properties of returns of capital assets are consistent with a factor structure. -
A Financially Justifiable and Practically Implementable Approach to Coherent Stress Testing
———————— An EDHEC-Risk Institute Working Paper A Financially Justifiable and Practically Implementable Approach to Coherent Stress Testing September 2018 ———————— 1. Introduction and Motivation 5 2. Bayesian Nets for Stress Testing 10 3. What Constitutes a Stress Scenario 13 4. The Goal 15 5. Definition of the Variables 17 6. Notation 19 7. The Consistency and Continuity Requirements 21 8. Assumptions 23 9. Obtaining the Distribution of Representative Market Indices. 25 10. Limitations and Weaknesses of the Approach 29 11. From Representative to Granular Market Indices 31 12. Some Practical Examples 35 13. Simple Extensions and Generalisations 41 Portfolio weights 14. Conclusions 43 back to their original References weights, can be a 45 source of additional About EDHEC-Risk Institute performance. 48 EDHEC-Risk Institute Publications and Position Papers (2017-2019) 53 ›2 Printed in France, July 2019. Copyright› 2EDHEC 2019. The opinions expressed in this study are those of the authors and do not necessarily reflect those of EDHEC Business School. Abstract ———————— We present an approach to stress testing that is both practically implementable and solidly rooted in well-established financial theory. We present our results in a Bayesian-net context, but the approach can be extended to different settings. We show i) how the consistency and continuity conditions are satisfied; ii) how the result of a scenario can be consistently cascaded from a small number of macrofinancial variables to the constituents of a granular portfolio; and iii) how an approximate but robust estimate of the likelihood of a given scenario can be estimated. This is particularly important for regulatory and capital-adequacy applications. -
Arbitrage Pricing Theory
Federal Reserve Bank of New York Staff Reports Arbitrage Pricing Theory Gur Huberman Zhenyu Wang Staff Report no. 216 August 2005 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 the 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. Arbitrage Pricing Theory Gur Huberman and Zhenyu Wang Federal Reserve Bank of New York Staff Reports, no. 216 August 2005 JEL classification: G12 Abstract Focusing on capital asset returns governed by a factor structure, the Arbitrage Pricing Theory (APT) is a one-period model, in which preclusion of arbitrage over static portfolios of these assets leads to a linear relation between the expected return and its covariance with the factors. The APT, however, does not preclude arbitrage over dynamic portfolios. Consequently, applying the model to evaluate managed portfolios is contradictory to the no-arbitrage spirit of the model. An empirical test of the APT entails a procedure to identify features of the underlying factor structure rather than merely a collection of mean-variance efficient factor portfolios that satisfies the linear relation. Key words: arbitrage, asset pricing model, factor model Huberman: Columbia University Graduate School of Business (e-mail: [email protected]). Wang: Federal Reserve Bank of New York and University of Texas at Austin McCombs School of Business (e-mail: [email protected]). This review of the arbitrage pricing theory was written for the forthcoming second edition of The New Palgrave Dictionary of Economics, edited by Lawrence Blume and Steven Durlauf (London: Palgrave Macmillan). -
Dividend Valuation Models Prepared by Pamela Peterson Drake, Ph.D., CFA
Dividend valuation models Prepared by Pamela Peterson Drake, Ph.D., CFA Contents 1. Overview ..................................................................................................................................... 1 2. The basic model .......................................................................................................................... 1 3. Non-constant growth in dividends ................................................................................................. 5 A. Two-stage dividend growth ...................................................................................................... 5 B. Three-stage dividend growth .................................................................................................... 5 C. The H-model ........................................................................................................................... 7 4. The uses of the dividend valuation models .................................................................................... 8 5. Stock valuation and market efficiency ......................................................................................... 10 6. Summary .................................................................................................................................. 10 7. Index ........................................................................................................................................ 11 8. Further readings ....................................................................................................................... -
Building Efficient Hedge Fund Portfolios August 2017
Building Efficient Hedge Fund Portfolios August 2017 Investors typically allocate assets to hedge funds to access return, risk and diversification characteristics they can’t get from other investments. The hedge fund universe includes a wide variety of strategies and styles that can help investors achieve this objective. Why then, do so many investors make significant allocations to hedge fund strategies that provide the least portfolio benefit? In this paper, we look to hedge fund data for evidence that investors appear to be missing out on the core benefits of hedge fund investing and we attempt to understand why. The. Basics Let’s start with the assumption that an investor’s asset allocation is fairly similar to the general investing population. That is, the portfolio is dominated by equities and equity‐like risk. Let’s also assume that an investor’s reason for allocating to hedge funds is to achieve a more efficient portfolio, i.e., higher return per unit of risk taken. In order to make a portfolio more efficient, any allocation to hedge funds must include some combination of the following (relative to the overall portfolio): Higher return Lower volatility Lower correlation The Goal Identify return streams that deliver any or all of the three characteristics listed above. In a perfect world, the best allocators would find return streams that accomplish all three of these goals ‐ higher return, lower volatility, and lower correlation. In the real world, that is quite difficult to do. There are always trade‐ offs. In some cases, allocators accept tradeoffs to increase the probability of achieving specific objectives. -
The Impact of Portfolio Financing on Alpha Generation by H…
The Impact of Portfolio Financing on Alpha Generation by Hedge Funds An S3 Asset Management Commentary by Robert Sloan, Managing Partner and Krishna Prasad, Partner S3 Asset Management 590 Madison Avenue, 32nd Floor New York, NY 10022 Telephone: 212-759-5222 www.S3asset.com September 2004 Building a successful hedge fund requires more than just the traditional three Ps of Pedigree, Performance and Philosophy. As hedge funds’ popularity increases, it is increasingly clear that Process needs to be considered the 4th P in alpha generation. Clearly, balance sheet management, also known as securities or portfolio financing, is a key element of “process” as it adds to alpha (the hedge fund manager’s excess rate of return as compared to a benchmark). Typically, hedge funds surrender their balance sheet to their prime broker and do not fully understand the financing alpha that they often leave on the table. The prime brokerage business is an oligopoly and the top three providers virtually control the pricing of securities financing. Hedge funds and their investors therefore need to pay close heed to the value provided by their prime broker as it has a direct impact on alpha and the on-going health of a fund. Abstract As investors seek absolute returns, hedge funds have grown exponentially over the past decade. In the quest for better performance, substantial premium is being placed on alpha generation skills. Now, more than ever before, there is a great degree of interest in deconstructing and better understanding the drivers of hedge fund alpha. Investors and hedge fund managers have focused on the relevance of asset allocation, stock selection, portfolio construction and trading costs on alpha. -
1 Factor Models a (Linear) Factor Model Assumes That the Rate of Return of an Asset Is Given By
1Factor Models The Markowitz mean-variance framework requires having access to many parameters: If there are n risky assets, with rates of return ri,i=1, 2,...,n, then we must know 2 − all the n means (ri), n variances (σi ) and n(n 1)/2covariances (σij) for a total of 2n + n(n − 1)/2 parameters. If for example n = 100 we would need 4750 parameters, and if n = 1000 we would need 501, 500 parameters! At best we could try to estimate these, but how? In fact, it is easy to see that trying to estimate the means, for example, to a workable level of accuracy is almost impossible using historical (e.g., past) data over time.1 What happens is that the standard deviation of our estimate is too large (for example larger than the estimate itself), thus rendering the estimate worthless. One can bring the standard deviation down only by increasing the data to go back to (say) over ahundred years! To see this: If we want to estimate expected rate of return over a typical 1-month period we could take n monthly data points r(1),...,r(n) denoting the rate of return over individual months in the past, and then average 1 n r(j). (1) n j=1 This estimate (assuming independent and identically distributed (iid) returns over months) has a mean√ and standard deviation given by r, σ/ n respectively, where r and σ denote the true mean and standard deviation over 1-month. If, for example, a stock’s yearly expected rate of return is 16%, then the monthly such rate is r =16/12=1.333% = 0.0133.