Risk Parity: in the Spotlight After 50 Years
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Lecture 07: Mean-Variance Analysis & Variance Analysis & Capital Asset
Fin 501: Asset Pricing Lecture 07: MeanMean--VarianceVariance Analysis & Capital Asset Pricing Model (CAPM) Prof. Markus K. Brunnermeier MeanMean--VarianceVariance Analysis and CAPM Slide 0707--11 Fin 501: Asset Pricing OiOverview 1. Simple CAPM with quadratic utility functions (derived from statestate--priceprice beta model) 2. MeanMean--variancevariance preferences – Portfolio Theory – CAPM (intuition) 3. CAPM – Projections – Pricing Kernel and Expectation Kernel MeanMean--VarianceVariance Analysis and CAPM Slide 0707--22 Fin 501: Asset Pricing RllSttRecall Statee--prpriBtice Beta mod dlel Recall: E[Rh] - Rf = βh E[R*- Rf] where βh := Cov[R*,Rh] / Var[R*] very general – but what is R* in reality? MeanMean--VarianceVariance Analysis and CAPM Slide 0707--33 Fin 501: Asset Pricing Simple CAPM with Quadratic Expected Utility 1. All agents are identical • Expected utility U(x 0, x1)=) = ∑s πs u(x0, xs) ⇒ m= ∂1u/E[u / E[∂0u] 2 • Quadratic u(x0,x1)=v0(x0) - (x1- α) ⇒ ∂1u = [-2(x111,1- α),…, -2(xS1S,1- α)] •E[Rh] – Rf = - Cov[m,Rh] / E[m] f h = -R Cov[∂1u, R ] / E[∂0u] f h = -R Cov[-2(x1- α), R ]/E[] / E[∂0u] f h = R 2Cov[x1,R ] / E[∂0u] • Also holds for market portfolio m f f m •E[R] – R = R 2Cov[x1,R ]/E[∂0u] ⇒ MeanMean--VarianceVariance Analysis and CAPM Slide 0707--44 Fin 501: Asset Pricing Simple CAPM with Quadratic Expected Utility 2. Homogenous agents + Exchange economy m ⇒ x1 = agg. enddiflldihdowment and is perfectly correlated with R E[E[RRh]=]=RRf + βh {E[{E[RRm]]--RRf} Market Security Line * f M f N.B.: R =R (a+b1R )/(a+b1R ) in this case (where b1<0)! MeanMean--VarianceVariance Analysis and CAPM Slide 0707--55 Fin 501: Asset Pricing OiOverview 1. -
A Retirement Portfolio's Efficient Frontier
Paper ID #13258 Teaching Students about the Value of Diversification - A Retirement Portfo- lio’s Efficient Frontier Dr. Ted Eschenbach P.E., University of Alaska Anchorage Dr. Ted Eschenbach, P.E. is the principal of TGE Consulting, an emeritus professor of engineering man- agement at the University of Alaska Anchorage, and the founding editor emeritus of the Engineering Management Journal. He is the author or coauthor of over 250 publications and presentations, including 19 books. With his coauthors he has won best paper awards at ASEE, ASEM, ASCE, & IIE conferences, and the 2009 Grant award for the best article in The Engineering Economist. He earned his B.S. from Purdue in 1971, his doctorate in industrial engineering from Stanford University in 1975, and his masters in civil engineering from UAA in 1999. Dr. Neal Lewis, University of Bridgeport Neal Lewis received his Ph.D. in engineering management in 2004 and B.S. in chemical engineering in 1974 from the University of Missouri – Rolla (now the Missouri University of Science and Technology), and his MBA in 2000 from the University of New Haven. He is an associate professor in the School of Engineering at the University of Bridgeport. He has over 25 years of industrial experience, having worked at Procter & Gamble and Bayer. Prior to UB, he has taught at UMR, UNH, and Marshall University. Neal is a member of ASEE, ASEM, and IIE. c American Society for Engineering Education, 2015 Teaching Students about the Value of Diversification— A Retirement Portfolio’s Efficient Frontier Abstract The contents of most engineering economy texts imply that relatively few engineering economy courses include coverage of investing. -
An Analytic Derivation of the Efficient Portfolio Frontier
ALFRED P. SLOAN SCHOOL OF MANAGEMEI AN ANALYTIC DERIVATION OF THE EFFICIENT PORTFOLIO FRONTIER 493-70 Robert C, Merton October 1970 MASSACHUSETTS INSTITUTE OF TECHNOLOGY J 50 MEMORIAL DRIVE CAMBRIDGE, MASSACHUSETTS 02139 AN ANALYTIC DERIVATION OF THE EFFICIENT PORTFOLIO FRONTIER 493-70 Robert C, Merton October 1970 ' AN ANALYTIC DERIVATION OF THE EFFICIEm: PORTFOLIO FRONTIER Robert C. Merton Massachusetts Institute of Technology October 1970 I. Introduction . The characteristics of the efficient (in the mean-variance sense) portfolio frontier have been discussed at length in the literature. However, for more than three assets, the general approach has been to display qualitative results in terms of graphs. In this paper, the efficient portfolio frontiers are derived explicitly, and the characteristics claimed for these frontiers verified. The most important implication derived from these characteristics, the separation theorem, is stated andproved in the context of a mutual fund theorem. It is shown that under certain conditions, the classical graphical technique for deriving the efficient portfolio frontier is incorrect. II. The efficient portfolio set when all securities are risky . Suppose there are m risky securities with the expected return on the between the i i^ security denoted by 0< ; the covariance of returns . j'-^ the return on the i and security denoted by <J~ ; the variance of ij = assumed security denoted by CT^^ (T^ . Because all m securities are *I thank M, Scholes, S. Myers, and G. Pogue for helpful discussion. Aid from the National Science Foundation is gratefully acknowledged. E. Fama ^See H. Markowitz [6], J. Tobin [9], W. Sharpe [8], and [3]. ^The exceptions to this have been discussions of general equilibrium models: see, for example, E. -
The Capital Asset Pricing Model (Capm)
THE CAPITAL ASSET PRICING MODEL (CAPM) Investment and Valuation of Firms Juan Jose Garcia Machado WS 2012/2013 November 12, 2012 Fanck Leonard Basiliki Loli Blaž Kralj Vasileios Vlachos Contents 1. CAPM............................................................................................................................................... 3 2. Risk and return trade off ............................................................................................................... 4 Risk ................................................................................................................................................... 4 Correlation....................................................................................................................................... 5 Assumptions Underlying the CAPM ............................................................................................. 5 3. Market portfolio .............................................................................................................................. 5 Portfolio Choice in the CAPM World ........................................................................................... 7 4. CAPITAL MARKET LINE ........................................................................................................... 7 Sharpe ratio & Alpha ................................................................................................................... 10 5. SECURITY MARKET LINE ................................................................................................... -
Toward the Efficient Impact Frontier by Michael Mccreless
Features Toward the Efficient Impact Frontier By Michael McCreless Stanford Social Innovation Review Winter 2017 Copyright 2016 by Leland Stanford Jr. University All Rights Reserved Stanford Social Innovation Review www.ssir.org Email: [email protected] Stanford Social Innovation Review / Winter 2017 49 who join Furaha, by contrast, not only gain a route to a At Root Capital, leaders are using tools from safer and more reliable market, but also receive a price mainstream financial analysis to calibrate the , premium that Furaha has negotiated with foreign cof- role that subsidies play in their investing practice. fee buyers. In addition, Furaha provides clean water and electricity to farmers. Unlike the loans to UCC and GADC, Root Capital’s loan to Furaha required a subsidy: The cost of lending to the cooperative was greater than the interest that it would pay to Root Capital. For Tugume, building a high-impact, financially TOWARD THE sustainable loan portfolio requires a delicate balanc- ing act. “Each year, I try to make five or six big loans to large, well-established businesses,” he says. “These loans provide revenue to Root Capital, and the busi- nesses meet our social and environmental criteria: They purchase crops from local farmers and often pro- C vide services like agronomic training and farm inputs. EFFI IENT Then, in the rest of my portfolio, I make much smaller loans to earlier-stage businesses that have a harder time getting loans but show potential for growth.” Tugume, in short, has developed an intuitive approach to creating a portfolio that generates both impact and revenue. -
THE EFFICIENT FRONTIER -.:: Oliver Capital Management, Inc
THE EFFICIENT FRONTIER INVESTMENT ADVICE - TIME FOR THE INTELLECTUALS? Markowitz, Nobel Prize Winner vs. Industry Practice If you haven’t made money in the last 5 years and if you haven’t heard of Harry Markowitz, read on! It could be time to review your benchmarks, your assets and even your advisor. This is especially true for the management of lump sums. During the 1990’s, a storming bull market made every advisor a winner. Whether you were a seasoned stockbroker, or a hair-dresser turned financial salesman, unreasonable 20% annual returns became achievable. The need for expertise was excellently masked. Traditional portfolio planning centered on mixing bonds and equities, with diversification being added only to ensure that there was a lot of eggs in the portfolio basket. The amount of intellectual empirical study on the portfolio – minimal. Recently, the “traditional” portfolio method has been described as “putting your head in the oven and your feet in the freezer”. Lots of extremes in terms of volatility and results. Simpletons of Investment So where’s the alternative? Well, back in the 1950’s, a bloke called Harry Markowitz studied a mix of maths and investments. It culminated in 1990 with the sharing of a Nobel Prize in Economics. This work now forms the backbone of Modern Portfolio thinking (Theory) and Efficient Frontiers in investment. If it took the intellectual world thirty-something years to spot Harry, you can hardly blame the Financial Services world (and the “simpletons” therein) for not noticing. Being one of the “simpletons”, I should present a defence. -
Stock Market Analysis Using Deep Learning and Efficient Frontier Algorithm
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 06 | June 2020 www.irjet.net p-ISSN: 2395-0072 STOCK MARKET ANALYSIS USING DEEP LEARNING AND EFFICIENT FRONTIER ALGORITHM Krish Shah1, Neha Joshi2, Abhishek Kumar Saxena3, Benoi Alex4 1-4Student, Dept. of Computer Science and Engineering, MIT School of Engineering, MIT ADT University, Pune Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - The stock market or share market is one of the stocks are the property of the owner associated, he could sell foremost difficult and complex tasks to do business. Tiny them at any value to an Emptor at an Exchange like Bombay ownership companies, brokerage companies, banking sectors, Stock Exchange or metropolis securities market. Traders and etc., all rely on it to form revenue and divide risks; a really patrons continue to commerce these shares at their own difficult model. As we all know machine learning and artificial value however the corporation solely gets to stay the cash intelligence have always helped the world in finding solutions created throughout the commerce. The continuing hopes of to almost every problem. So, this paper proposes to use shares from one party to a different so as to create a lot of statistics like efficient frontier and machine learning profits end up in a rise of the value of the actual share once algorithms like long short-term memory(LSTM), to predict the each profitable deal. However, if the corporate problems a longer-term stock worth for exchange by open supply libraries lot of stocks at lower commerce, then the value for exchange and antecedent algorithms to assist build this unpredictable goes down and traders suffer a loss. -
CHAPTER 11 Optimal Portfolio Choice and the Capital Asset Pricing Model
CHAPTER 11 Optimal Portfolio Choice and the Capital Asset Pricing Model Chapter Synopsis 11.1 The Expected Return of a Portfolio The expected return on an n-asset portfolio is simply the weighted-average of the expected returns of the portfolio’s components: nn RxRxRPnniiPii=+++=⇒= 11 22 L xR ∑∑ xRERxER [] []. ii==11 where xi is the value of asset i divided by the portfolio’s total value. 11.2 The Volatility of a Two-Stock Portfolio The standard deviation of an n-asset portfolio is generally less than the weighted-average of the standard deviations of the portfolio’s components. When two or more stocks are combined in a portfolio, some of their risk will generally be eliminated through diversification. The amount of risk that will remain depends on the degree to which the stocks share systematic risk. Thus the risk of a portfolio depends on more than the risk and return of the component stocks, and the degree to which the stocks’ returns move together is important; their covariance or correlation must be considered: Covariance is the expected product of the deviation of each return from its mean, which can be measured from historical data as: 1 T Cov(RRij , ) =−− ∑( R it,, Rij)( R jt R) . T − 1t=1 Correlation is the covariance of the returns divided by the standard deviation of each return: ©2011 Pearson Education 122 Berk/DeMarzo • Corporate Finance, Second Edition Cov(RRij , ) Corr(RRij , ) = . SD(RRij )× SD( ) Correlation is generally easier to interpret because it always lies between –1 and 1. The closer the correlation is to 1, the more the returns tend to move together. -
The Efficient Frontier and International Portfolio Diversification 103
ZESZYTY NAUKOWE UNIWERSYTETU SZCZECIŃSKIEGO nr 803 Finanse, Rynki Finansowe, Ubezpieczenia nr 66 (2014) s. 101–110 The Effi cient Frontier and International Portfolio Diversifi cation Marcin Halicki, Andreas Uphaus* Abstract: This paper presents the main characteristics of the effi cient frontier. In addition, it presents the essence of international portfolio diversifi cation. It has been shown that this can be a tool for getting high re- turns on investments in stocks. Most important, however, is that the paper presents the possibility of building such portfolios consisting of international shares, which the effi ciency frontier can be expressed by a math- ematical function. To meet its function in practice, it is necessary to assume no short sales. Therefore, all combinations of portfolios have been prepared in such a case. This gives the refl ection of a useful character. It should be noted that the used research methods include: research literature and empirical studies. Keywords: portfolio management, stocks, expected rate of return, risk Introduction Portfolio Management from the perspective of capital investment should generate a number of benefits. The first is to achieve a high growth potential through properly selected invest- ment assets. The second is to reduce the risk of investment while working out positive re- turns in all market conditions for a long-term investment horizon. The third includes liquid- ity, which represents a supply of cash which could be used to acquire assets with higher risk (in the case of the favourable conditions in capital markets). However, practice shows that in the case of equity investments, portfolio managers build portfolios without generating the benefits presented above. -
Empirical Study on CAPM Model on China Stock Market
Empirical study on CAPM model on China stock market MASTER THESIS WITHIN: Business administration in finance NUMBER OF CREDITS: 15 ECTS TUTOR: Andreas Stephan PROGRAMME OF STUDY: international financial analysis AUTHOR: Taoyuan Zhou, Huarong Liu JÖNKÖPING 05/2018 Master Thesis in Business Administration Title: Empirical study on CAPM model of China stock market Authors: Taoyuan Zhou and Huarong Liu Tutor: Andreas Stephan Date: 05/2018 Key terms: CAPM model, β coefficient, empirical test Abstract Background: The capital asset pricing model (CAPM) describes the interrelationship between the expected return of risk assets and risk in the equilibrium of investment market and gives the equilibrium price of risky assets (Banz, 1981). CAPM plays a very important role in the process of establishing a portfolio (Bodie, 2009). As Chinese stock market continues to grow and expand, the scope and degree of attention of CAPM models in China will also increase day by day. Therefore, in China, such an emerging market, it is greatly necessary to test the applicability and validity of the CAPM model in the capital market. Purpose: Through the monthly data of 100 stocks from January 1, 2007 to February 1, 2018, the time series and cross-sectional data of the capital asset pricing model on Chinese stock market are tested. The main objectives are: (1) Empirical study of the relationship between risk and return using the data of Chinese stock market in recent years to test whether the CAPM model established in the developed western market is suitable for the Chinese market. (2) Through the empirical analysis of the results to analyse the characteristics and existing problems of Chinese capital market. -
Evaluation of a Portfolio in Dow Jones Industrial Average Optimized by Mean-Variance Analysis
EXAMENSARBETE INOM TEKNIK, GRUNDNIVÅ, 15 HP STOCKHOLM, SVERIGE 2020 Evaluation of a Portfolio in Dow Jones Industrial Average Optimized by Mean-Variance Analysis DANIEL LIU ALEXANDER STRID KTH SKOLAN FÖR TEKNIKVETENSKAP Evaluation of a Portfolio in Dow Jones Industrial Average Optimized by Mean-Variance Analysis Daniel Liu Alexander Strid ROYAL Degree Projects in Applied Mathematics and Industrial Economics (15 hp) Degree Programme in Industrial Engineering and Management (300 hp) KTH Royal Institute of Technology year 2020 Supervisor at KTH: Johan Karlsson Examiner at KTH: Sigrid Källblad Nordin TRITA-SCI-GRU 2020:103 MAT-K 2020:004 Royal Institute of Technology School of Engineering Sciences KTH SCI SE-100 44 Stockholm, Sweden URL: www.kth.se/sci Abstract This thesis evaluates the mean-variance analysis framework by comparing the perfor- mance of an optimized portfolio consisting of stocks from the Dow Jones Industrial Average to the performance of the Dow Jones Industrial Average index itself. The results show that the optimized portfolio performs better than the corresponding in- dex when evaluated on the period between 2015 and 2019. However, the variance of the returns are high and therefore it is difficult to determine if mean-variance analysis performs better than its corresponding index in the general case. Furthermore, it is shown that individual stocks can still influence the movement of an optimized portfolio significantly, even though the model is supposed to diversify firm-specific risk. Thus, the authors recommend modifying the model by restricting the amount that is allowed to be invested in a single stock, if one wishes to apply mean-variance analysis in real- ity. -
Modern Portfolio Theory
Modern portfolio theory “Portfolio analysis” redirects here. For theorems about where Rp is the return on the port- the mean-variance efficient frontier, see Mutual fund folio, Ri is the return on asset i and separation theorem. For non-mean-variance portfolio wi is the weighting of component analysis, see Marginal conditional stochastic dominance. asset i (that is, the proportion of as- set “i” in the portfolio). Modern portfolio theory (MPT), or mean-variance • Portfolio return variance: analysis, is a mathematical framework for assembling a P σ2 = w2σ2 + portfolio of assets such that the expected return is maxi- Pp P i i i w w σ σ ρ , mized for a given level of risk, defined as variance. Its key i j=6 i i j i j ij insight is that an asset’s risk and return should not be as- where σ is the (sample) standard sessed by itself, but by how it contributes to a portfolio’s deviation of the periodic returns on overall risk and return. an asset, and ρij is the correlation coefficient between the returns on Economist Harry Markowitz introduced MPT in a 1952 [1] assets i and j. Alternatively the ex- essay, for which he was later awarded a Nobel Prize in pression can be written as: economics. P P 2 σp = i j wiwjσiσjρij , where ρij = 1 for i = j , or P P 1 Mathematical model 2 σp = i j wiwjσij , where σij = σiσjρij is the (sam- 1.1 Risk and expected return ple) covariance of the periodic re- turns on the two assets, or alterna- MPT assumes that investors are risk averse, meaning that tively denoted as σ(i; j) , covij or given two portfolios that offer the same expected return, cov(i; j) .