Capital Asset Pricing Model and Factor Models 2.1 Capital Asset
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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. -
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. -
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. -
The Effect of Systematic Risk Factors on the Performance of the Malaysia Stock Market
57 Proceedings of International Conference on Economics 2017 (ICE 2017) PROCEEDINGS ICE 2017 P57 – P68 ISBN 978-967-0521-99-2 THE EFFECT OF SYSTEMATIC RISK FACTORS ON THE PERFORMANCE OF THE MALAYSIA STOCK MARKET Shameer Fahmi, Caroline Geetha, Rosle Mohidin Faculty of Business, Economics and Accountancy, Universiti Malaysia Sabah ABSTRACT Malaysia has undergone a tremendous transformation in its economic due to the New Economic This has created instability on the changes in the macroeconomic fundamental, called systematic risk. The study aims to determine the effect of the systematic risk towards the performance of the stock returns. The paper aims to use the arbitrage pricing theory (APT) framework to relate the systematic risk with the performance of stock return. The variables chosen to represent the systematic risk were variables that were in line with the transformation policy implemented. The independent variables chosen were interest rate, inflation rate due to the removal of fuel subsidy and the implementation of good and service tax, exchange rate which is influenced by the inflow of foreign direct investment, crude oil price that determines the revenue of the country being an oil exporter and industrial production index that reflect political as well as business news meanwhile the dependent variable is stock returns. All the macroeconomic variables will be regressed with the lagged 2 of its own variable to obtain the residuals. The residuals will be powered by two to obtain the variance which represents the risk of each variable. The variables were run for unit root to determine the level of stationarity. This was followed by the establishment of long run relationship using Johansen Cointegration and short run relationship using Vector Error Correction Modeling. -
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. -
Systematic Risk of Cdos and CDO Arbitrage
Systematic risk of CDOs and CDO arbitrage Alfred Hamerle (University of Regensburg) Thilo Liebig (Deutsche Bundesbank) Hans-Jochen Schropp (University of Regensburg) Discussion Paper Series 2: Banking and Financial Studies No 13/2009 Discussion Papers represent the authors’ personal opinions and do not necessarily reflect the views of the Deutsche Bundesbank or its staff. Editorial Board: Heinz Herrmann Thilo Liebig Karl-Heinz Tödter Deutsche Bundesbank, Wilhelm-Epstein-Strasse 14, 60431 Frankfurt am Main, Postfach 10 06 02, 60006 Frankfurt am Main Tel +49 69 9566-0 Telex within Germany 41227, telex from abroad 414431 Please address all orders in writing to: Deutsche Bundesbank, Press and Public Relations Division, at the above address or via fax +49 69 9566-3077 Internet http://www.bundesbank.de Reproduction permitted only if source is stated. ISBN 978-3–86558–570–7 (Printversion) ISBN 978-3–86558–571–4 (Internetversion) Abstract “Arbitrage CDOs” have recorded an explosive growth during the years before the outbreak of the financial crisis. In the present paper we discuss potential sources of such arbitrage opportunities, in particular arbitrage gains due to mispricing. For this purpose we examine the risk profiles of Collateralized Debt Obligations (CDOs) in some detail. The analyses reveal significant differences in the risk profile between CDO tranches and corporate bonds, in particular concerning the considerably increased sensitivity to systematic risks. This has far- reaching consequences for risk management, pricing and regulatory capital requirements. A simple analytical valuation model based on the CAPM and the single-factor Merton model is used in order to keep the model framework simple. -
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. -
Is the Insurance Industry Systemically Risky?
Is the Insurance Industry Systemically Risky? Viral V Acharya and Matthew Richardson1 1 Authors are at New York University Stern School of Business. E-mail: [email protected], [email protected]. Some of this essay is based on “Systemic Risk and the Regulation of Insurance Companies”, by Viral V Acharya, John Biggs, Hanh Le, Matthew Richardson and Stephen Ryan, published as Chapter 9 in Regulating Wall Street: The Dodd-Frank Act and the Architecture of Global Finance, edited by Viral V Acharya, Thomas Cooley, Matthew Richardson and Ingo Walter, John Wiley & Sons, November 2010. [Type text] Is the Insurance Industry Systemically Risky? i VIRAL V ACHARYA AND MATTHEW RICHARDSON I. Introduction As a result of the financial crisis, the Congress passed the Dodd-Frank Wall Street Reform and Consumer Protection Act and it was signed into law by President Obama on July 21, 2010. The Dodd-Frank Act did not create a new direct regulator of insurance but did impose on non-bank holding companies, possibly insurance entities, a major new and unknown form of regulation for those deemed “Systemically Important Financial Institutions” (SIFI) (sometimes denoted “Too Big To Fail” (TBTF)) or, presumably any entity which regulators believe represents a “contingent liability” for the federal government, in the event of severe stress or failure. In light of the financial crisis, and the somewhat benign changes to insurance regulation contained in the Dodd-Frank Act (regulation of SIFIs aside), how should a modern insurance regulatory structure be designed to deal with systemic risk? The economic theory of regulation is very clear. -
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. -
Multi-Factor Models and the Arbitrage Pricing Theory (APT)
Multi-Factor Models and the Arbitrage Pricing Theory (APT) Econ 471/571, F19 - Bollerslev APT 1 Introduction The empirical failures of the CAPM is not really that surprising ,! We had to make a number of strong and pretty unrealistic assumptions to arrive at the CAPM ,! All investors are rational, only care about mean and variance, have the same expectations, ... ,! Also, identifying and measuring the return on the market portfolio of all risky assets is difficult, if not impossible (Roll Critique) In this lecture series we will study an alternative approach to asset pricing called the Arbitrage Pricing Theory, or APT ,! The APT was originally developed in 1976 by Stephen A. Ross ,! The APT starts out by specifying a number of “systematic” risk factors ,! The only risk factor in the CAPM is the “market” Econ 471/571, F19 - Bollerslev APT 2 Introduction: Multiple Risk Factors Stocks in the same industry tend to move more closely together than stocks in different industries ,! European Banks (some old data): Source: BARRA Econ 471/571, F19 - Bollerslev APT 3 Introduction: Multiple Risk Factors Other common factors might also affect stocks within the same industry ,! The size effect at work within the banking industry (some old data): Source: BARRA ,! How does this compare to the CAPM tests that we just talked about? Econ 471/571, F19 - Bollerslev APT 4 Multiple Factors and the CAPM Suppose that there are only two fundamental sources of systematic risks, “technology” and “interest rate” risks Suppose that the return on asset i follows the -
Economic Aspects of Securitization of Risk
ECONOMIC ASPECTS OF SECURITIZATION OF RISK BY SAMUEL H. COX, JOSEPH R. FAIRCHILD AND HAL W. PEDERSEN ABSTRACT This paper explains securitization of insurance risk by describing its essential components and its economic rationale. We use examples and describe recent securitization transactions. We explore the key ideas without abstract mathematics. Insurance-based securitizations improve opportunities for all investors. Relative to traditional reinsurance, securitizations provide larger amounts of coverage and more innovative contract terms. KEYWORDS Securitization, catastrophe risk bonds, reinsurance, retention, incomplete markets. 1. INTRODUCTION This paper explains securitization of risk with an emphasis on risks that are usually considered insurable risks. We discuss the economic rationale for securitization of assets and liabilities and we provide examples of each type of securitization. We also provide economic axguments for continued future insurance-risk securitization activity. An appendix indicates some of the issues involved in pricing insurance risk securitizations. We do not develop specific pricing results. Pricing techniques are complicated by the fact that, in general, insurance-risk based securities do not have unique prices based on axbitrage-free pricing considerations alone. The technical reason for this is that the most interesting insurance risk securitizations reside in incomplete markets. A market is said to be complete if every pattern of cash flows can be replicated by some portfolio of securities that are traded in the market. The payoffs from insurance-based securities, whose cash flows may depend on Please address all correspondence to Hal Pedersen. ASTIN BULLETIN. Vol. 30. No L 2000, pp 157-193 158 SAMUEL H. COX, JOSEPH R. FAIRCHILD AND HAL W.