Chapter 16, Part C Investment Portfolio. Risk is often measured by variance. For the binary gamble L= [, z z;1/2,1/2], recall that expected value is
|
|
- Simon Quinn
- 7 years ago
- Views:
Transcription
1 Chapter 16, Part C Investment Portfolio Risk is often measured b variance. For the binar gamble L= [, z z;1/,1/], recall that epected value is 1 1 Ez = z + ( z ) = 0. For this binar gamble, z represents the gamble size, and the variance of the gamble is z. That is, the variance of this lotter is simpl the gamble size squared. How is variance measured for general lotteries? In general, gambles are more comple. Variance is generall defined as ( ) = ( n ) = i( i ). i= 1 Var X E X EX p X EX The idea is to punish large deviations from the mean, and hence the errors or deviations from the mean are squared. Variance is then the epected value of the squared errors or deviations. Eample: Probabilit Prize 40% $10 50% $100 10% $80
2 Mean of this lotter was $106. What is the variance of this lotter? Var( X ) =.4 (10 106) +.5 ( ) +.1 (80 106) = + + = Portfolio Choice with One Risk Asset Consider an individual investing in two assets: a risk asset (stocks) and a safe asset (Treasur bills). Let X and Y denote the returns from the risk and riskless assets, respectivel. Let σ denote the variance of X, and let α denote the fraction of the portfolio invested in the risk asset. What is the problem with risk assets? Then one can choose a conve combination of the safe and risk asset. Epected return on the risk asset is higher than that of the riskless asset, i.e., EX > Y. However, there is a small chance that one ma earn less than from the safe asset. Accordingl, the risk asset has a higher standard deviation (or variance) than the riskless asset. Let the initial asset w = 1. (If not, the total return will be wr. In the following analsis, initial wealth w does not pla a role.) The total return is a random variable, R= α X + (1 α) Y. (1) Epected return is ER = αx + (1 α) Y, () where a bar denotes epected value, and X = EX is epected return from the risk asset. The variance of the portfolio is var α σ. = (3)
3 If one is risk averse, he will maimize epected utilit of the portfolio. For portfolio analses, epected utilit is often epressed as a weighted average of the epected return and variance, EU = ER kασ = α X + α Y kα σ (1 ). (4) Here, k represents the penalt per unit of variance. If the investor is risk neutral, then k is 0. If he is risk averse, k > 0. Thus, k can be treated as a risk aversion measure. This is different from the Arrow-Pratt absolute risk aversion measure, U"( w) A = U'( w) or relative risk aversion measure, U"( w) R = w, U '( w ) where w is income or wealth. These two measures are widel used in the theoretical literature. For more details, see another note. I notice that the absolute and relative risk aversion measures are NOT parameters, but functions, which depend on the level of wealth. For this reason, the are impractical for everda use. Business people buing and selling risk assets need rough and read measures of risk aversion. 3
4 Also, the mean-variance approach is often used for practical reasons that mean and variance of risk assets are easil obtainable. Epected utilit then depends on three parameters: mean (EX), variance (σ ), and the risk aversion measure, k The first order condition is: Just differentiate epected utilit in (4) with respect to α, we can find its optimal value. You can solve for α. deu dα = = ( X Y) kασ 0. The optimal fraction of our investment in the risk asset is: X Y α* =. kσ It is interesting to note that if the two assets have identical epected rates of return, i.e., X = Y, then α* = 0. That makes sense. If two assets have the same epected rates of return, there is no point in taking risk. If EX > Y, then one will hold some risk asset and some riskless asset. If the risk of the risk asset increases, Wouldn t the investment in the risk asset decline? A risk averse investor holds a risk asset onl if it ields a higher epected return. Differentiating (6) with respect to σ, we get 4
5 α * k( X Y) = < 0. σ ( kσ ) (7) That is, as the variance of the risk asset increases, the investor s holding of the risk asset declines. However, (6) shows that no matter how large the variance is, the investor alwas holds some portion of its portfolio in the risk asset, for the simple reason that it has a higher epected rate of return. If Jill is more risk averse than Jack, wouldn t she invest less in the risk asset? You can verif that b differentiating (6) with respect to k gives α k * σ ( X Y) = < 0. ( kσ ) (8) That is, a more risk averse person invests less in the risk asset. Two Risk Assets I understand now that one needs to invest in the risk assets if epected returns are higher than in safe assets. But there are man risk assets. Which ones does one choose? If one risk asset has a higher epected return than the other, should one invest all his mone into the asset with higher epected return? There is a proverb that sas Don t put all our eggs in one basket. The current financial crisis is partl caused b big banks and insurance companies that have ignored this adage. No. If one s wealth is infinite or one is risk neutral, in the long run it might be the best strateg. However, long run survival requires that the investor survive each period. 5
6 How does one choose among man risk assets? Suppose that one asset is riskier than another, i.e., the variance of one asset is greater than that of the other. In this case, should we abandon the riskier asset altogether? How do we choose between two risk assets? For simplicit, consider the case with two risk assets. Assume that the return on the two risk assets are independentl distributed or uncorrelated, i.e., their covariance is zero. Not if it has a higher epected return. Epected utilit of a portfolio with two risk assets can then be written as: EU = ER kα σ = αx + (1 α) Y ( ασ βσ ) k +, (9) where σ i is the variance of the risk asset i, and α and β are the fractions of the portfolio held in the risk assets, X and Y, respectivel. Thus, α + β = 1. I know how to do this. The Lagrangian function associated with this problem is: L= αx + βy ( α σ β σ ) k + + λ[1 α β]. (10) Partiall differentiating (10) with respect to α and β gives the FOCs: X kασ λ = (1) 0, Y kβσ λ = (13) 0, 6
7 1 α β = 0. I regret that I asked the question. But from (1) and (13), here is the solution: kσ + X Y α* =. k( ) σ + σ Note that if σ = 0, that is, if Y is a riskless asset, then (14) reduces to (6). If the two assets have the same return ( X = Y ), then optimal portfolio reduces to: σ α* =, (15) ( σ ) + σ which is independent of risk aversion. It simpl depends on the variances of the two risk assets. For all risk averters, investment in the riskier asset is smaller. Question: If Y has a higher variance than X, one s investment in X asset is greater than that of Y. Shouldn t he get rid of the riskier asset altogether? If the two risk assets have the same variances and same epected rates of return, then α = 1. What if the variances of the two risk assets were also No. The solution does not sa that α = 1. (This proves the wisdom of the proverb Don t put all eggs in one basket, even if one asset is riskier than the other) Just because one asset has less variance and hence less risk, it does not follow that one should get rid of the riskier asset at all. If two assets have identical rates of return and risks, then half-and-half is an optimal solution. Such a portfolio will ield the same epected return as that when all eggs are in one basket but will minimize the variance of the return of the total portfolio. Then the variance of the portfolio is: 7
8 equal? σ = α σ + (1 α) σ = α σ + (1 α + α ) σ (1.17) = σ (1 + α α). If α = 0, this reduces to σ. If α = 1, it also reduces to σ. But somewhere between both ends, variance of the portfolio is smaller. I can see that. For instance, if α = 1/3, it reduces to We can see how the variance changes as α increases as in the diagram below. σ = σ (1 + / 9 6 / 9) σ σ = (5 / 9) <. The optimal solution is 1 α * =. About the Emerging Markets If the financial assets (e.g., stocks) of two countries have the same epected rates of return and variances, shouldn t a shrewd investor should hold an even share of both assets? Yes and No. American investors generall hold more than 50% of their portfolio in domestic assets, and onl recentl began to bu stocks in emerging markets. Foreign securities ma have higher rates of return, but the are also considered riskier. 8
Finance 400 A. Penati - G. Pennacchi Market Micro-Structure: Notes on the Kyle Model
Finance 400 A. Penati - G. Pennacchi Market Micro-Structure: Notes on the Kyle Model These notes consider the single-period model in Kyle (1985) Continuous Auctions and Insider Trading, Econometrica 15,
More informationChapter 5 Risk and Return ANSWERS TO SELECTED END-OF-CHAPTER QUESTIONS
Chapter 5 Risk and Return ANSWERS TO SELECTED END-OF-CHAPTER QUESTIONS 5-1 a. Stand-alone risk is only a part of total risk and pertains to the risk an investor takes by holding only one asset. Risk is
More informationRisk Budgeting: Concept, Interpretation and Applications
Risk Budgeting: Concept, Interpretation and Applications Northfield Research Conference 005 Eddie Qian, PhD, CFA Senior Portfolio Manager 60 Franklin Street Boston, MA 00 (67) 439-637 7538 8//005 The Concept
More informationChoice under Uncertainty
Choice under Uncertainty Part 1: Expected Utility Function, Attitudes towards Risk, Demand for Insurance Slide 1 Choice under Uncertainty We ll analyze the underlying assumptions of expected utility theory
More information1 Portfolio mean and variance
Copyright c 2005 by Karl Sigman Portfolio mean and variance Here we study the performance of a one-period investment X 0 > 0 (dollars) shared among several different assets. Our criterion for measuring
More informationExamples: Joint Densities and Joint Mass Functions Example 1: X and Y are jointly continuous with joint pdf
AMS 3 Joe Mitchell Eamples: Joint Densities and Joint Mass Functions Eample : X and Y are jointl continuous with joint pdf f(,) { c 2 + 3 if, 2, otherwise. (a). Find c. (b). Find P(X + Y ). (c). Find marginal
More informationThis paper is not to be removed from the Examination Halls
~~FN3023 ZB d0 This paper is not to be removed from the Examination Halls UNIVERSITY OF LONDON FN3023 ZB BSc degrees and Diplomas for Graduates in Economics, Management, Finance and the Social Sciences,
More informationSAMPLE MID-TERM QUESTIONS
SAMPLE MID-TERM QUESTIONS William L. Silber HOW TO PREPARE FOR THE MID- TERM: 1. Study in a group 2. Review the concept questions in the Before and After book 3. When you review the questions listed below,
More informationPortfolio Allocation and Asset Demand with Mean-Variance Preferences
Portfolio Allocation and Asset Demand with Mean-Variance Preferences Thomas Eichner a and Andreas Wagener b a) Department of Economics, University of Bielefeld, Universitätsstr. 25, 33615 Bielefeld, Germany.
More informationMidterm Exam:Answer Sheet
Econ 497 Barry W. Ickes Spring 2007 Midterm Exam:Answer Sheet 1. (25%) Consider a portfolio, c, comprised of a risk-free and risky asset, with returns given by r f and E(r p ), respectively. Let y be the
More informationCHAPTER 6: RISK AVERSION AND CAPITAL ALLOCATION TO RISKY ASSETS
CHAPTER 6: RISK AVERSION AND CAPITAL ALLOCATION TO RISKY ASSETS PROBLEM SETS 1. (e). (b) A higher borrowing is a consequence of the risk of the borrowers default. In perfect markets with no additional
More informationHolding Period Return. Return, Risk, and Risk Aversion. Percentage Return or Dollar Return? An Example. Percentage Return or Dollar Return? 10% or 10?
Return, Risk, and Risk Aversion Holding Period Return Ending Price - Beginning Price + Intermediate Income Return = Beginning Price R P t+ t+ = Pt + Dt P t An Example You bought IBM stock at $40 last month.
More informationFinancial Market Microstructure Theory
The Microstructure of Financial Markets, de Jong and Rindi (2009) Financial Market Microstructure Theory Based on de Jong and Rindi, Chapters 2 5 Frank de Jong Tilburg University 1 Determinants of the
More informationSystems of Linear Equations: Solving by Substitution
8.3 Sstems of Linear Equations: Solving b Substitution 8.3 OBJECTIVES 1. Solve sstems using the substitution method 2. Solve applications of sstems of equations In Sections 8.1 and 8.2, we looked at graphing
More information.4 120 +.1 80 +.5 100 = 48 + 8 + 50 = 106.
Chapter 16. Risk and Uncertainty Part A 2009, Kwan Choi Expected Value X i = outcome i, p i = probability of X i EV = pix For instance, suppose a person has an idle fund, $100, for one month, and is considering
More informationThe foreign exchange market
The foreign exchange market con 4330 Lecture 6 Asbjørn Rødseth University of Oslo February, 22 2011 Asbjørn Rødseth (University of Oslo) The foreign exchange market February, 22 2011 1 / 16 Outline 1 Mean-variance
More informationUniversity of California, Los Angeles Department of Statistics. Random variables
University of California, Los Angeles Department of Statistics Statistics Instructor: Nicolas Christou Random variables Discrete random variables. Continuous random variables. Discrete random variables.
More informationThe CAPM (Capital Asset Pricing Model) NPV Dependent on Discount Rate Schedule
The CAPM (Capital Asset Pricing Model) Massachusetts Institute of Technology CAPM Slide 1 of NPV Dependent on Discount Rate Schedule Discussed NPV and time value of money Choice of discount rate influences
More informationCML is the tangent line drawn from the risk free point to the feasible region for risky assets. This line shows the relation between r P and
5. Capital Asset Pricing Model and Factor Models Capital market line (CML) CML is the tangent line drawn from the risk free point to the feasible region for risky assets. This line shows the relation between
More informationDeriving MRS from Utility Function, Budget Constraints, and Interior Solution of Optimization
Utilit Function, Deriving MRS. Principles of Microeconomics, Fall Chia-Hui Chen September, Lecture Deriving MRS from Utilit Function, Budget Constraints, and Interior Solution of Optimization Outline.
More informationCAPM, Arbitrage, and Linear Factor Models
CAPM, Arbitrage, and Linear Factor Models CAPM, Arbitrage, Linear Factor Models 1/ 41 Introduction We now assume all investors actually choose mean-variance e cient portfolios. By equating these investors
More informationLecture 1: Asset Allocation
Lecture 1: Asset Allocation Investments FIN460-Papanikolaou Asset Allocation I 1/ 62 Overview 1. Introduction 2. Investor s Risk Tolerance 3. Allocating Capital Between a Risky and riskless asset 4. Allocating
More informationSo, using the new notation, P X,Y (0,1) =.08 This is the value which the joint probability function for X and Y takes when X=0 and Y=1.
Joint probabilit is the probabilit that the RVs & Y take values &. like the PDF of the two events, and. We will denote a joint probabilit function as P,Y (,) = P(= Y=) Marginal probabilit of is the probabilit
More informationLINEAR FUNCTIONS OF 2 VARIABLES
CHAPTER 4: LINEAR FUNCTIONS OF 2 VARIABLES 4.1 RATES OF CHANGES IN DIFFERENT DIRECTIONS From Precalculus, we know that is a linear function if the rate of change of the function is constant. I.e., for
More informationCHAPTER 6. Topics in Chapter. What are investment returns? Risk, Return, and the Capital Asset Pricing Model
CHAPTER 6 Risk, Return, and the Capital Asset Pricing Model 1 Topics in Chapter Basic return concepts Basic risk concepts Stand-alone risk Portfolio (market) risk Risk and return: CAPM/SML 2 What are investment
More informationExample: Document Clustering. Clustering: Definition. Notion of a Cluster can be Ambiguous. Types of Clusterings. Hierarchical Clustering
Overview Prognostic Models and Data Mining in Medicine, part I Cluster Analsis What is Cluster Analsis? K-Means Clustering Hierarchical Clustering Cluster Validit Eample: Microarra data analsis 6 Summar
More informationZeros of Polynomial Functions. The Fundamental Theorem of Algebra. The Fundamental Theorem of Algebra. zero in the complex number system.
_.qd /7/ 9:6 AM Page 69 Section. Zeros of Polnomial Functions 69. Zeros of Polnomial Functions What ou should learn Use the Fundamental Theorem of Algebra to determine the number of zeros of polnomial
More informationISyE 6761 Fall 2012 Homework #2 Solutions
1 1. The joint p.m.f. of X and Y is (a) Find E[X Y ] for 1, 2, 3. (b) Find E[E[X Y ]]. (c) Are X and Y independent? ISE 6761 Fall 212 Homework #2 Solutions f(x, ) x 1 x 2 x 3 1 1/9 1/3 1/9 2 1/9 1/18 3
More informationChapter 5. Random variables
Random variables random variable numerical variable whose value is the outcome of some probabilistic experiment; we use uppercase letters, like X, to denote such a variable and lowercase letters, like
More information2.7 Applications of Derivatives to Business
80 CHAPTER 2 Applications of the Derivative 2.7 Applications of Derivatives to Business and Economics Cost = C() In recent ears, economic decision making has become more and more mathematicall oriented.
More informationTHE POWER RULES. Raising an Exponential Expression to a Power
8 (5-) Chapter 5 Eponents and Polnomials 5. THE POWER RULES In this section Raising an Eponential Epression to a Power Raising a Product to a Power Raising a Quotient to a Power Variable Eponents Summar
More informationSection 7.2 Linear Programming: The Graphical Method
Section 7.2 Linear Programming: The Graphical Method Man problems in business, science, and economics involve finding the optimal value of a function (for instance, the maimum value of the profit function
More information5.2 Inverse Functions
78 Further Topics in Functions. Inverse Functions Thinking of a function as a process like we did in Section., in this section we seek another function which might reverse that process. As in real life,
More information1. a. (iv) b. (ii) [6.75/(1.34) = 10.2] c. (i) Writing a call entails unlimited potential losses as the stock price rises.
1. Solutions to PS 1: 1. a. (iv) b. (ii) [6.75/(1.34) = 10.2] c. (i) Writing a call entails unlimited potential losses as the stock price rises. 7. The bill has a maturity of one-half year, and an annualized
More informationf x a 0 n 1 a 0 a 1 cos x a 2 cos 2x a 3 cos 3x b 1 sin x b 2 sin 2x b 3 sin 3x a n cos nx b n sin nx n 1 f x dx y
Fourier Series When the French mathematician Joseph Fourier (768 83) was tring to solve a problem in heat conduction, he needed to epress a function f as an infinite series of sine and cosine functions:
More informationDistinction Between Interest Rates and Returns
Distinction Between Interest Rates and Returns Rate of Return RET = C + P t+1 P t =i c + g P t C where: i c = = current yield P t g = P t+1 P t P t = capital gain Key Facts about Relationship Between Interest
More informationThe Big Picture. Correlation. Scatter Plots. Data
The Big Picture Correlation Bret Hanlon and Bret Larget Department of Statistics Universit of Wisconsin Madison December 6, We have just completed a length series of lectures on ANOVA where we considered
More informationChapter 13 Introduction to Linear Regression and Correlation Analysis
Chapter 3 Student Lecture Notes 3- Chapter 3 Introduction to Linear Regression and Correlation Analsis Fall 2006 Fundamentals of Business Statistics Chapter Goals To understand the methods for displaing
More informationα α λ α = = λ λ α ψ = = α α α λ λ ψ α = + β = > θ θ β > β β θ θ θ β θ β γ θ β = γ θ > β > γ θ β γ = θ β = θ β = θ β = β θ = β β θ = = = β β θ = + α α α α α = = λ λ λ λ λ λ λ = λ λ α α α α λ ψ + α =
More informationPolynomials. Jackie Nicholas Jacquie Hargreaves Janet Hunter
Mathematics Learning Centre Polnomials Jackie Nicholas Jacquie Hargreaves Janet Hunter c 26 Universit of Sdne Mathematics Learning Centre, Universit of Sdne 1 1 Polnomials Man of the functions we will
More informationXII. RISK-SPREADING VIA FINANCIAL INTERMEDIATION: LIFE INSURANCE
XII. RIS-SPREADIG VIA FIACIAL ITERMEDIATIO: LIFE ISURACE As discussed briefly at the end of Section V, financial assets can be traded directly in the capital markets or indirectly through financial intermediaries.
More informationCh5: Discrete Probability Distributions Section 5-1: Probability Distribution
Recall: Ch5: Discrete Probability Distributions Section 5-1: Probability Distribution A variable is a characteristic or attribute that can assume different values. o Various letters of the alphabet (e.g.
More informationNotes on Present Value, Discounting, and Financial Transactions
Notes on Present Value, Discounting, and Financial Transactions Professor John Yinger The Maxwell School Sracuse Universit Version 2.0 Introduction These notes introduce the concepts of present value and
More informationMath 431 An Introduction to Probability. Final Exam Solutions
Math 43 An Introduction to Probability Final Eam Solutions. A continuous random variable X has cdf a for 0, F () = for 0 <
More informationModels of Risk and Return
Models of Risk and Return Aswath Damodaran Aswath Damodaran 1 First Principles Invest in projects that yield a return greater than the minimum acceptable hurdle rate. The hurdle rate should be higher for
More informationIntroduction to Risk, Return and the Historical Record
Introduction to Risk, Return and the Historical Record Rates of return Investors pay attention to the rate at which their fund have grown during the period The holding period returns (HDR) measure the
More informationChoice Under Uncertainty Insurance Diversification & Risk Sharing AIG. Uncertainty
Uncertainty Table of Contents 1 Choice Under Uncertainty Budget Constraint Preferences 2 Insurance Choice Framework Expected Utility Theory 3 Diversification & Risk Sharing 4 AIG States of Nature and Contingent
More informationDecision & Risk Analysis Lecture 6. Risk and Utility
Risk and Utility Risk - Introduction Payoff Game 1 $14.50 0.5 0.5 $30 - $1 EMV 30*0.5+(-1)*0.5= 14.5 Game 2 Which game will you play? Which game is risky? $50.00 Figure 13.1 0.5 0.5 $2,000 - $1,900 EMV
More informationSolution: The optimal position for an investor with a coefficient of risk aversion A = 5 in the risky asset is y*:
Problem 1. Consider a risky asset. Suppose the expected rate of return on the risky asset is 15%, the standard deviation of the asset return is 22%, and the risk-free rate is 6%. What is your optimal position
More informationMBA 611 STATISTICS AND QUANTITATIVE METHODS
MBA 611 STATISTICS AND QUANTITATIVE METHODS Part I. Review of Basic Statistics (Chapters 1-11) A. Introduction (Chapter 1) Uncertainty: Decisions are often based on incomplete information from uncertain
More informationMakeup Exam MØA 155 Financial Economics February 2010 Permitted Material: Calculator, Norwegian/English Dictionary
University of Stavanger (UiS) Stavanger Masters Program Makeup Exam MØA 155 Financial Economics February 2010 Permitted Material: Calculator, Norwegian/English Dictionary The number in brackets is the
More informationPrice Theory Lecture 3: Theory of the Consumer
Price Theor Lecture 3: Theor of the Consumer I. Introduction The purpose of this section is to delve deeper into the roots of the demand curve, to see eactl how it results from people s tastes, income,
More informationEcon 132 C. Health Insurance: U.S., Risk Pooling, Risk Aversion, Moral Hazard, Rand Study 7
Econ 132 C. Health Insurance: U.S., Risk Pooling, Risk Aversion, Moral Hazard, Rand Study 7 C2. Health Insurance: Risk Pooling Health insurance works by pooling individuals together to reduce the variability
More informationChapter 5. Risk and Return. Copyright 2009 Pearson Prentice Hall. All rights reserved.
Chapter 5 Risk and Return Learning Goals 1. Understand the meaning and fundamentals of risk, return, and risk aversion. 2. Describe procedures for assessing and measuring the risk of a single asset. 3.
More informationPractice Set #4 and Solutions.
FIN-469 Investments Analysis Professor Michel A. Robe Practice Set #4 and Solutions. What to do with this practice set? To help students prepare for the assignment and the exams, practice sets with solutions
More informationINVESTIGATIONS AND FUNCTIONS 1.1.1 1.1.4. Example 1
Chapter 1 INVESTIGATIONS AND FUNCTIONS 1.1.1 1.1.4 This opening section introduces the students to man of the big ideas of Algebra 2, as well as different was of thinking and various problem solving strategies.
More informationChoice Under Uncertainty
Decision Making Under Uncertainty Choice Under Uncertainty Econ 422: Investment, Capital & Finance University of ashington Summer 2006 August 15, 2006 Course Chronology: 1. Intertemporal Choice: Exchange
More information1 Capital Allocation Between a Risky Portfolio and a Risk-Free Asset
Department of Economics Financial Economics University of California, Berkeley Economics 136 November 9, 2003 Fall 2006 Economics 136: Financial Economics Section Notes for Week 11 1 Capital Allocation
More informationCHAPTER 11: ARBITRAGE PRICING THEORY
CHAPTER 11: ARBITRAGE PRICING THEORY 1. The revised estimate of the expected rate of return on the stock would be the old estimate plus the sum of the products of the unexpected change in each factor times
More informationAn Overview of Asset Pricing Models
An Overview of Asset Pricing Models Andreas Krause University of Bath School of Management Phone: +44-1225-323771 Fax: +44-1225-323902 E-Mail: a.krause@bath.ac.uk Preliminary Version. Cross-references
More informationLecture 5: Put - Call Parity
Lecture 5: Put - Call Parity Reading: J.C.Hull, Chapter 9 Reminder: basic assumptions 1. There are no arbitrage opportunities, i.e. no party can get a riskless profit. 2. Borrowing and lending are possible
More informationAggregate Loss Models
Aggregate Loss Models Chapter 9 Stat 477 - Loss Models Chapter 9 (Stat 477) Aggregate Loss Models Brian Hartman - BYU 1 / 22 Objectives Objectives Individual risk model Collective risk model Computing
More informationGaussian Probability Density Functions: Properties and Error Characterization
Gaussian Probabilit Densit Functions: Properties and Error Characterization Maria Isabel Ribeiro Institute for Sstems and Robotics Instituto Superior Tcnico Av. Rovisco Pais, 1 149-1 Lisboa PORTUGAL {mir@isr.ist.utl.pt}
More informationCHAPTER 6: Continuous Uniform Distribution: 6.1. Definition: The density function of the continuous random variable X on the interval [A, B] is.
Some Continuous Probability Distributions CHAPTER 6: Continuous Uniform Distribution: 6. Definition: The density function of the continuous random variable X on the interval [A, B] is B A A x B f(x; A,
More informationCHAPTER 6 RISK AND RISK AVERSION
CHAPTER 6 RISK AND RISK AVERSION RISK AND RISK AVERSION Risk with Simple Prospects Risk, Speculation, and Gambling Risk Aversion and Utility Values Risk with Simple Prospects The presence of risk means
More informationLesson 5. Risky assets
Lesson 5. Risky assets Prof. Beatriz de Blas May 2006 5. Risky assets 2 Introduction How stock markets serve to allocate risk. Plan of the lesson: 8 >< >: 1. Risk and risk aversion 2. Portfolio risk 3.
More informationCHAPTER TEN. Key Concepts
CHAPTER TEN Ke Concepts linear regression: slope intercept residual error sum of squares or residual sum of squares sum of squares due to regression mean squares error mean squares regression (coefficient)
More informationLecture 12: The Black-Scholes Model Steven Skiena. http://www.cs.sunysb.edu/ skiena
Lecture 12: The Black-Scholes Model Steven Skiena Department of Computer Science State University of New York Stony Brook, NY 11794 4400 http://www.cs.sunysb.edu/ skiena The Black-Scholes-Merton Model
More informationQuestions and Answers
GNH7/GEOLGG9/GEOL2 EARTHQUAKE SEISMOLOGY AND EARTHQUAKE HAZARD TUTORIAL (6): EARTHQUAKE STATISTICS Question. Questions and Answers How many distinct 5-card hands can be dealt from a standard 52-card deck?
More informationFTS Real Time System Project: Portfolio Diversification Note: this project requires use of Excel s Solver
FTS Real Time System Project: Portfolio Diversification Note: this project requires use of Excel s Solver Question: How do you create a diversified stock portfolio? Advice given by most financial advisors
More informationSolving Quadratic Equations by Graphing. Consider an equation of the form. y ax 2 bx c a 0. In an equation of the form
SECTION 11.3 Solving Quadratic Equations b Graphing 11.3 OBJECTIVES 1. Find an ais of smmetr 2. Find a verte 3. Graph a parabola 4. Solve quadratic equations b graphing 5. Solve an application involving
More informationRisk and return (1) Class 9 Financial Management, 15.414
Risk and return (1) Class 9 Financial Management, 15.414 Today Risk and return Statistics review Introduction to stock price behavior Reading Brealey and Myers, Chapter 7, p. 153 165 Road map Part 1. Valuation
More informationFINANCIAL ECONOMICS OPTION PRICING
OPTION PRICING Options are contingency contracts that specify payoffs if stock prices reach specified levels. A call option is the right to buy a stock at a specified price, X, called the strike price.
More informationChapter 5 Uncertainty and Consumer Behavior
Chapter 5 Uncertainty and Consumer Behavior Questions for Review 1. What does it mean to say that a person is risk averse? Why are some people likely to be risk averse while others are risk lovers? A risk-averse
More informationChapter 5 Discrete Probability Distribution. Learning objectives
Chapter 5 Discrete Probability Distribution Slide 1 Learning objectives 1. Understand random variables and probability distributions. 1.1. Distinguish discrete and continuous random variables. 2. Able
More informationApplied Economics For Managers Recitation 5 Tuesday July 6th 2004
Applied Economics For Managers Recitation 5 Tuesday July 6th 2004 Outline 1 Uncertainty and asset prices 2 Informational efficiency - rational expectations, random walks 3 Asymmetric information - lemons,
More informationStatistics 100A Homework 4 Solutions
Problem 1 For a discrete random variable X, Statistics 100A Homework 4 Solutions Ryan Rosario Note that all of the problems below as you to prove the statement. We are proving the properties of epectation
More information15 CAPM and portfolio management
ECG590I Asset Pricing. Lecture 15: CAPM and portfolio management 1 15 CAPM and portfolio management 15.1 Theoretical foundation for mean-variance analysis We assume that investors try to maximize the expected
More informationLINEAR INEQUALITIES. less than, < 2x + 5 x 3 less than or equal to, greater than, > 3x 2 x 6 greater than or equal to,
LINEAR INEQUALITIES When we use the equal sign in an equation we are stating that both sides of the equation are equal to each other. In an inequality, we are stating that both sides of the equation are
More informationHedging. An Undergraduate Introduction to Financial Mathematics. J. Robert Buchanan. J. Robert Buchanan Hedging
Hedging An Undergraduate Introduction to Financial Mathematics J. Robert Buchanan 2010 Introduction Definition Hedging is the practice of making a portfolio of investments less sensitive to changes in
More informationTutorial: Structural Models of the Firm
Tutorial: Structural Models of the Firm Peter Ritchken Case Western Reserve University February 16, 2015 Peter Ritchken, Case Western Reserve University Tutorial: Structural Models of the Firm 1/61 Tutorial:
More information7.3 Parabolas. 7.3 Parabolas 505
7. Parabolas 0 7. Parabolas We have alread learned that the graph of a quadratic function f() = a + b + c (a 0) is called a parabola. To our surprise and delight, we ma also define parabolas in terms of
More informationSimple Linear Regression Inference
Simple Linear Regression Inference 1 Inference requirements The Normality assumption of the stochastic term e is needed for inference even if it is not a OLS requirement. Therefore we have: Interpretation
More informationLeast Squares Estimation
Least Squares Estimation SARA A VAN DE GEER Volume 2, pp 1041 1045 in Encyclopedia of Statistics in Behavioral Science ISBN-13: 978-0-470-86080-9 ISBN-10: 0-470-86080-4 Editors Brian S Everitt & David
More informationReview of Basic Options Concepts and Terminology
Review of Basic Options Concepts and Terminology March 24, 2005 1 Introduction The purchase of an options contract gives the buyer the right to buy call options contract or sell put options contract some
More informationFinal Exam MØA 155 Financial Economics Fall 2009 Permitted Material: Calculator
University of Stavanger (UiS) Stavanger Masters Program Final Exam MØA 155 Financial Economics Fall 2009 Permitted Material: Calculator The number in brackets is the weight for each problem. The weights
More informationEnhancing the Teaching of Statistics: Portfolio Theory, an Application of Statistics in Finance
Page 1 of 11 Enhancing the Teaching of Statistics: Portfolio Theory, an Application of Statistics in Finance Nicolas Christou University of California, Los Angeles Journal of Statistics Education Volume
More informationChapter 6 The Tradeoff Between Risk and Return
Chapter 6 The Tradeoff Between Risk and Return MULTIPLE CHOICE 1. Which of the following is an example of systematic risk? a. IBM posts lower than expected earnings. b. Intel announces record earnings.
More informationLecture 8: More Continuous Random Variables
Lecture 8: More Continuous Random Variables 26 September 2005 Last time: the eponential. Going from saying the density e λ, to f() λe λ, to the CDF F () e λ. Pictures of the pdf and CDF. Today: the Gaussian
More informationMoreover, under the risk neutral measure, it must be the case that (5) r t = µ t.
LECTURE 7: BLACK SCHOLES THEORY 1. Introduction: The Black Scholes Model In 1973 Fisher Black and Myron Scholes ushered in the modern era of derivative securities with a seminal paper 1 on the pricing
More informationFigure S9.1 Profit from long position in Problem 9.9
Problem 9.9 Suppose that a European call option to buy a share for $100.00 costs $5.00 and is held until maturity. Under what circumstances will the holder of the option make a profit? Under what circumstances
More information1 Maximizing pro ts when marginal costs are increasing
BEE12 Basic Mathematical Economics Week 1, Lecture Tuesda 12.1. Pro t maimization 1 Maimizing pro ts when marginal costs are increasing We consider in this section a rm in a perfectl competitive market
More informationCapital Market Theory: An Overview. Return Measures
Capital Market Theory: An Overview (Text reference: Chapter 9) Topics return measures measuring index returns (not in text) holding period returns return statistics risk statistics AFM 271 - Capital Market
More information1 Capital Asset Pricing Model (CAPM)
Copyright c 2005 by Karl Sigman 1 Capital Asset Pricing Model (CAPM) We now assume an idealized framework for an open market place, where all the risky assets refer to (say) all the tradeable stocks available
More information15.401 Finance Theory
Finance Theory MIT Sloan MBA Program Andrew W. Lo Harris & Harris Group Professor, MIT Sloan School Lecture 13 14 14: : Risk Analytics and Critical Concepts Motivation Measuring Risk and Reward Mean-Variance
More informationHandout 2: The Foreign Exchange Market 1
University of Bern Prof. H. Dellas International Finance Winter semester 01/02 Handout 2: The Foreign Exchange Market 1 1 Financial Instruments in the FOREX markets Forward contracts Large, non standardized
More informationChapter 4 Lecture Notes
Chapter 4 Lecture Notes Random Variables October 27, 2015 1 Section 4.1 Random Variables A random variable is typically a real-valued function defined on the sample space of some experiment. For instance,
More informationFlorida Algebra I EOC Online Practice Test
Florida Algebra I EOC Online Practice Test Directions: This practice test contains 65 multiple-choice questions. Choose the best answer for each question. Detailed answer eplanations appear at the end
More informationM1 in Economics and Economics and Statistics Applied multivariate Analysis - Big data analytics Worksheet 1 - Bootstrap
Nathalie Villa-Vialanei Année 2015/2016 M1 in Economics and Economics and Statistics Applied multivariate Analsis - Big data analtics Worksheet 1 - Bootstrap This worksheet illustrates the use of nonparametric
More information3 Introduction to Assessing Risk
3 Introduction to Assessing Risk Important Question. How do we assess the risk an investor faces when choosing among assets? In this discussion we examine how an investor would assess the risk associated
More information