# Lecture 11 Uncertainty

Size: px
Start display at page:

## Transcription

1 Lecture 11 Uncertainty 1. Contingent Claims and the State-Preference Model 1) Contingent Commodities and Contingent Claims Using the simple two-good model we have developed throughout this course, think of x 1 as consumption when state of the world 1 occurs and x 2 as consumption when state of world 2 occurs. We call a commodity that is to be delivered in only one state of the world a statecontingent commodity or just contingent commodity. Actually, if there are n different commodities and m states of the world, there will be ( n m) different contingent commodities. or now, we consider only one commodity (consumption) and two states of the world: good (1) and bad (2). Thus, in this case, there are two contingent commodities. In the state-preference model, consumers trade contingent claims, which are rights to consumption, if and only if, a particular state of the world occurs. Thus, in this framework, x 1 represents the amount of the good the consumer will receive (or purchase) if state 1 occurs and x 2 is the amount s/he will receive if state 2 occurs. Ex) Betting in a horse race. The states of the world correspond to how the various horses will place, and a claim corresponds to a bet that a horse will win. If your horse comes in, you get paid in proportion to the number of tickets you purchased. But the only way to guarantee payment in all states of the world is to bet on all the horses. *2) Risk Sharing between Consumers in a Contingent-Claims Market or example, rank Knight is a dealer in a Casino. Michael Spence wants to play the card game with \$100,000. There are 3 hidden cards, two of which have dandelions and the remaining one has a rose. If Spence picks a card with rose, he will win the amount that he bet. And if Spence picks a card with dandelion, he will lose the amount that he bet. Let C r and C d denote the two possible commodity bundles that Spence will shop after the card game (contingent commodity). Budget Line Spence will choose any amount within \$100,000 to play the game. C d 200K I 100K 70K G slope= -1 H 100K 130K 200K C r 53

2 : initial endowment, H : Budget Set (or opportunity set) How about I? Iso-Expected Value Line Now, based upon the game framework, we can calculate the expected value of the consumption that Spence will take. Since the probabilities are 1/3 and 2/3 for rose and dandelion, respectively, if Spence bet \$ 30K, then Cr = 130Kand Cd = 70 K. So the expected value if he bet \$ 30K will be: 1 2 E = 130, ,000 = 90, Is this card game fair? Absolutely not. Let s compute the expected value if Spence bet all the money he had (\$100,000). How much can he expect to make? , = 66, Now, how can we design a new rule that can make this game fair (a game that can guarantee the initial state regardless of how much money player bets. Net gain is always zero)? Simply because there is one card of rose along with two cards of dandelions, the way to make a fair game is to double the award when player picks a card of rose (we got a new game rule!) Can you draw a new budget line with the new framework? Sure, you can. C d risk-free line 150K R 100K 70K T slope= -1/2 45º J 0 100K 160K 300K C r J : iso-expected value line or fair odds line Meaning of Risk It is risk free not to play the game (45º line is called risk-free line). The more money the players bet, the higher the risk s/he needs to take. Moving farther from, which is risk free, the riskiness will rise. Point J represents the maximized level of riskiness with given condition. Preference and Choices of Decision-makers Risk neutral person will rely only upon the expected value of the game. If the game is 54

3 favorable based on the computed expected value, s/he will play. Risk averse person will not play unless the game is sufficiently favorable. So s/he will not play even if the game is fair with 0 expected value. Risk loving person will play even though the game is disadvantageous based on the expected value. Indifference Curves with Risk Averseness Convex indifference curve implies that people prefer the average consumption bundles to the extreme ones. And the slope of indifference curve on 45º line (risk-free line) will have a same slope with the fair odds line. Why? (explain with the green indifference curve I o ) C d Risk-free line Slope:-1/2 K I o 45º J C r So, we can presume that the person with risk averseness will choose the point as the optimal point if the game is fair (will play the game). Now, what if the game is advantageous to the player? C d 100K R M i 1 i 3 i 2 K 100K 400K C r Even if rank offers three times of the amount that Michael bet, Michael will bet small amount of money (100K R). 55

4 *3) Application: Insurance Market Model Total initial assets: \$6 million Probability of fire: 25% (or ¼) Estimated losses due to fire: \$4 million. So the new assets after fire will be \$2 millions. W f (fire) and W n (no fire) are the contingent commodities. Reimbursement in case of fire: \$4 per \$1 of insurance premium. W n Slope: -1/3 6 A 5 B i 1 45º W f In reality, however, it is not reasonable that reimbursement is bigger than the actual losses. So, it makes sense that if insured pays \$1 million to be reimbursed with \$ 4 millions, which will make even between the case with and without fire damages (as long as s/he is insured). Therefore, the budget line (and fair odds line, as well) that this person is facing would be AB ( BC is not feasible at all). Generally, if the premium rate (ratio of premium to reimbursement) equals the probability of damage (fire, losses, etc), that insurance program is called fair insurance. In this case, the insurance premium amounts will equal the expected value of reimbursement amounts. In our example, the probability of fire was assumed to be 25% (or ¼). So this insurance will be fair. And the absolute value of the slope of AB is 1/3, which implies that this insurance is fair. This slope means the fair odds under the condition that probability of fire is ¼ and probability of safety is ¾. Based upon this fair insurance, the expected value of assets of the insured on AB will be always \$5 millions. Since this insured is risk averse person (because s/he wants to buy insurance), his or her best choice must be on point B. C 56

6 n { } = E U,which is linear in the probabilities ( p i ). i= 1 p U ( ) (i) i x i 3) The Axioms of Expected Utility Preferences over possible outcomes are complete, reflexive, and transitive. Compound lotteries can be reduced to simple lotteries. Continuity. or each outcome x i between x 1 and x n, the consumer can name a probability, p i, such that s/he is indifferent between getting x i with certainty and playing a lottery (which involves getting x n with probability p i and x 1 with probability (1 p i )). We say that x i is the certainty equivalent to the lottery, ~ x i, where ~ xi = ( xn with pi and x1 with ( 1 pi)). Substitutability. The lottery ~ x i can always be substituted for its certainty equivalent x i in any other lottery. Monotonicity. If two lotteries with the same two alternatives each differ only in probabilities, then the lottery that gives higher probability to the most-preferred alternative is preferred to the other lottery. If preferences over lotteries satisfy above axioms, then we can assign numbers U( x i )which is associated with the outcomes x i, such that if we compare two lotteries L and L which offer probabilities ( p 1, L, p n ) and ( p 1, L, p n ) of obtaining those outcomes, L will be preferred to L if and only if n pu( x ) > p U( x ) i i i i i= 1 i= 1 This means that the rank order by expected utilities reflects the rank order of preference over the lotteries and that the rational individual will choose among risky alternatives as if s/he is maximizing expected utility. *4) The von Neumann-Morgenstern Utility unction A lottery L will pay \$A with probability p and \$B with (1 p ). If we can assign numbers (cardinal utilities) U( A)and U( B), then the expected utility of this lottery L can be defined as follows: U( L) = pu( A) + ( 1 p) U( B) By assigning two (hypothetical) extreme numbers A, B, U( A)and U( B), respectively. Suppose that A = \$100,000 and B = \$3,000. Also U( A)= 100 and U( B)= 0 Using the continuity axiom, we can calculate the specific probability p that can make this decision-maker indifferent between buying this lottery (risky asset) and having any specific amount of money (safe asset). Let s take \$25,000 as safe asset. If this person says that his p equals 0.4, then the utility that \$25,000 will create is U(\$25, 000) = 0. 4 U(\$100, 000) + ( ) U( 3, 000) = = 40 Using this example, the expected value of the award from this lottery is calculated simply \$[ pa + ( 1 p) B]. The risk averse person will prefer the situation of having the expected value amount for sure to playing this lottery. In his viewpoint, the expected utility of lottery is smaller than the utility of expected value. n 58

7 pu( A) + ( 1 p) U( B) < U[ pa+ ( 1 p) B] This characteristic is analogous to the mathematical implication of strictly concave function, which means that risk averse person will have (strictly) concave utility curve. Concave unction Suppose there is a function y = f ( x). Take any two values of x1, x2. And let x 3 be located in between x 1 and x 2. x 3 can be mathematically expressed as follows: x3 = kx1+ ( 1 k) x2, 0< k < 1 Hence, x 3 is a convex combination of x 1 and x 2. If k is close to 0, x 3 approaches to x 2. Concave function can be expressed as kf ( x1) + ( 1 k) f ( x2) < f [ kx1+ ( 1 k) x2] Likewise, convex function can be expressed as kf ( x ) + ( 1 k) f ( x ) > f [ kx + ( 1 k) x ] Determining risk premium U U(A) U[pA+(1-p)B] pu(a)+(1-p)u(b) h i j k U(B) g B M [ pa + ( 1 p) B] A Income 5) Portfolio All types of assets that people diversify. So, the important issue in portfolio is the portion of each type of asset. How to choose (or allocate) the combination of riskiness and rate of return will depend upon each person s attitude toward riskiness or uncertainty. Portfolio Selection Model Two types of assets: safe vs. risky assets r f : rate of return of safe (or risk-free) asset r k : rate of return of risky asset. We can simply imagine that r f < r k. To express the level of riskiness, we can adopt the standard deviation of the rate of return (σ ). 59

8 So, let s assume that r k has the standard deviation ofσ k. Suppose a person has \$1 worth of wealth and s/he wants to have a as a type of risky asset and ( 1 a) as safe asset. Based on this information, we can calculate the rate of return of this portfolio ( r p ) and level of riskiness (σ p ) as follows: rp = ark + ( 1 a) rf (ii) σ p = aσ k (iii) inally, the question is to get the specific value of a in this portfolio selection model. Using utility function with the insertion of two factors, r p and σ p, we can set up U = U( r p, σ p ) (iv) If this person is risk averse, s/he thinks that σ p will be a bad. rom (ii), we can get rp = rf + a( rk rf ) (v) Plugging a = σ p / σ kinto (v), we can get the final expression rk rf rp = rf + σ p σ (vi) k r p i 3 j 3 j 2 r k i 2 i 1 j 1 B E r f A O σ k σ p A: a = 0. B: a = 1 rk rf : price of risk σ k 60

### .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

### Choice 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

### Choice 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

### Economics 1011a: Intermediate Microeconomics

Lecture 11: Choice Under Uncertainty Economics 1011a: Intermediate Microeconomics Lecture 11: Choice Under Uncertainty Tuesday, October 21, 2008 Last class we wrapped up consumption over time. Today we

### Lecture 10 - Risk and Insurance

Lecture 10 - Risk and Insurance 14.03 Spring 2003 1 Risk Aversion and Insurance: Introduction To have a passably usable model of choice, we need to be able to say something about how risk affects choice

### Choice 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

### Economics 1011a: Intermediate Microeconomics

Lecture 12: More Uncertainty Economics 1011a: Intermediate Microeconomics Lecture 12: More on Uncertainty Thursday, October 23, 2008 Last class we introduced choice under uncertainty. Today we will explore

### 1 Uncertainty and Preferences

In this chapter, we present the theory of consumer preferences on risky outcomes. The theory is then applied to study the demand for insurance. Consider the following story. John wants to mail a package

### Lecture notes for Choice Under Uncertainty

Lecture notes for Choice Under Uncertainty 1. Introduction In this lecture we examine the theory of decision-making under uncertainty and its application to the demand for insurance. The undergraduate

### Decision & 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

### Introduction to Game Theory IIIii. Payoffs: Probability and Expected Utility

Introduction to Game Theory IIIii Payoffs: Probability and Expected Utility Lecture Summary 1. Introduction 2. Probability Theory 3. Expected Values and Expected Utility. 1. Introduction We continue further

### Risk and Uncertainty. Vani K Borooah University of Ulster

Risk and Uncertainty Vani K Borooah University of Ulster Basic Concepts Gamble: An action with more than one possible outcome, such that with each outcome there is an associated probability of that outcome

### Demand and supply of health insurance. Folland et al Chapter 8

Demand and supply of health Folland et al Chapter 8 Chris Auld Economics 317 February 9, 2011 What is insurance? From an individual s perspective, insurance transfers wealth from good states of the world

### Lecture Note 14: Uncertainty, Expected Utility Theory and the Market for Risk

Lecture Note 14: Uncertainty, Expected Utility Theory and the Market for Risk David Autor, Massachusetts Institute of Technology 14.03/14.003, Microeconomic Theory and Public Policy, Fall 2010 1 Risk Aversion

### MA 1125 Lecture 14 - Expected Values. Friday, February 28, 2014. Objectives: Introduce expected values.

MA 5 Lecture 4 - Expected Values Friday, February 2, 24. Objectives: Introduce expected values.. Means, Variances, and Standard Deviations of Probability Distributions Two classes ago, we computed the

### Intermediate Micro. Expected Utility

Intermediate Micro Expected Utility Workhorse model of intermediate micro Utility maximization problem Consumers Max U(x,y) subject to the budget constraint, I=P x x + P y y Health Economics Spring 2015

### Decision Making under Uncertainty

6.825 Techniques in Artificial Intelligence Decision Making under Uncertainty How to make one decision in the face of uncertainty Lecture 19 1 In the next two lectures, we ll look at the question of how

### Notes on Uncertainty and Expected Utility

Notes on Uncertainty and Expected Utility Ted Bergstrom, UCSB Economics 210A December 10, 2014 1 Introduction Expected utility theory has a remarkably long history, predating Adam Smith by a generation

### CHAPTER 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

### AMS 5 CHANCE VARIABILITY

AMS 5 CHANCE VARIABILITY The Law of Averages When tossing a fair coin the chances of tails and heads are the same: 50% and 50%. So if the coin is tossed a large number of times, the number of heads and

### A Simpli ed Axiomatic Approach to Ambiguity Aversion

A Simpli ed Axiomatic Approach to Ambiguity Aversion William S. Neilson Department of Economics University of Tennessee Knoxville, TN 37996-0550 wneilson@utk.edu March 2009 Abstract This paper takes the

### Name. Final Exam, Economics 210A, December 2011 Here are some remarks to help you with answering the questions.

Name Final Exam, Economics 210A, December 2011 Here are some remarks to help you with answering the questions. Question 1. A firm has a production function F (x 1, x 2 ) = ( x 1 + x 2 ) 2. It is a price

### Insurance. Michael Peters. December 27, 2013

Insurance Michael Peters December 27, 2013 1 Introduction In this chapter, we study a very simple model of insurance using the ideas and concepts developed in the chapter on risk aversion. You may recall

### Fund Manager s Portfolio Choice

Fund Manager s Portfolio Choice Zhiqing Zhang Advised by: Gu Wang September 5, 2014 Abstract Fund manager is allowed to invest the fund s assets and his personal wealth in two separate risky assets, modeled

### We rst consider the game from the player's point of view: Suppose you have picked a number and placed your bet. The probability of winning is

Roulette: On an American roulette wheel here are 38 compartments where the ball can land. They are numbered 1-36, and there are two compartments labeled 0 and 00. Half of the compartments numbered 1-36

### 1 Interest rates, and risk-free investments

Interest rates, and risk-free investments Copyright c 2005 by Karl Sigman. Interest and compounded interest Suppose that you place x 0 (\$) in an account that offers a fixed (never to change over time)

### Chapter 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

### Betting systems: how not to lose your money gambling

Betting systems: how not to lose your money gambling G. Berkolaiko Department of Mathematics Texas A&M University 28 April 2007 / Mini Fair, Math Awareness Month 2007 Gambling and Games of Chance Simple

### Applied 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,

### Financial Services [Applications]

Financial Services [Applications] Tomáš Sedliačik Institute o Finance University o Vienna tomas.sedliacik@univie.ac.at 1 Organization Overall there will be 14 units (12 regular units + 2 exams) Course

### Economics 206 Problem Set 1 Winter 2007 Vincent Crawford

Economics 206 Problem Set 1 Winter 2007 Vincent Crawford This problem set, which is optional, covers the material in the first half of the course, roughly in the order in which topics are discussed in

### Probability and Expected Value

Probability and Expected Value This handout provides an introduction to probability and expected value. Some of you may already be familiar with some of these topics. Probability and expected value are

### 3 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

### Lecture 13: Risk Aversion and Expected Utility

Lecture 13: Risk Aversion and Expected Utility Uncertainty over monetary outcomes Let x denote a monetary outcome. C is a subset of the real line, i.e. [a, b]. A lottery L is a cumulative distribution

### 36 Odds, Expected Value, and Conditional Probability

36 Odds, Expected Value, and Conditional Probability What s the difference between probabilities and odds? To answer this question, let s consider a game that involves rolling a die. If one gets the face

### Chapter 25: Exchange in Insurance Markets

Chapter 25: Exchange in Insurance Markets 25.1: Introduction In this chapter we use the techniques that we have been developing in the previous 2 chapters to discuss the trade of risk. Insurance markets

### Expected Value and the Game of Craps

Expected Value and the Game of Craps Blake Thornton Craps is a gambling game found in most casinos based on rolling two six sided dice. Most players who walk into a casino and try to play craps for the

### Asset Pricing. Chapter IV. Measuring Risk and Risk Aversion. June 20, 2006

Chapter IV. Measuring Risk and Risk Aversion June 20, 2006 Measuring Risk Aversion Utility function Indifference Curves U(Y) tangent lines U(Y + h) U[0.5(Y + h) + 0.5(Y h)] 0.5U(Y + h) + 0.5U(Y h) U(Y

### Section 7C: The Law of Large Numbers

Section 7C: The Law of Large Numbers Example. You flip a coin 00 times. Suppose the coin is fair. How many times would you expect to get heads? tails? One would expect a fair coin to come up heads half

### Statistics 100A Homework 3 Solutions

Chapter Statistics 00A Homework Solutions Ryan Rosario. Two balls are chosen randomly from an urn containing 8 white, black, and orange balls. Suppose that we win \$ for each black ball selected and we

### V. RANDOM VARIABLES, PROBABILITY DISTRIBUTIONS, EXPECTED VALUE

V. RANDOM VARIABLES, PROBABILITY DISTRIBUTIONS, EXPETED VALUE A game of chance featured at an amusement park is played as follows: You pay \$ to play. A penny and a nickel are flipped. You win \$ if either

### An 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

### Preferences. M. Utku Ünver Micro Theory. Boston College. M. Utku Ünver Micro Theory (BC) Preferences 1 / 20

Preferences M. Utku Ünver Micro Theory Boston College M. Utku Ünver Micro Theory (BC) Preferences 1 / 20 Preference Relations Given any two consumption bundles x = (x 1, x 2 ) and y = (y 1, y 2 ), the

### Betting with the Kelly Criterion

Betting with the Kelly Criterion Jane June 2, 2010 Contents 1 Introduction 2 2 Kelly Criterion 2 3 The Stock Market 3 4 Simulations 5 5 Conclusion 8 1 Page 2 of 9 1 Introduction Gambling in all forms,

### Intermediate Microeconomics (22014)

Intermediate Microeconomics (22014) I. Consumer Instructor: Marc Teignier-Baqué First Semester, 2011 Outline Part I. Consumer 1. umer 1.1 Budget Constraints 1.2 Preferences 1.3 Utility Function 1.4 1.5

### Economics 2020a / HBS 4010 / HKS API-111 Fall 2011 Practice Problems for Lectures 1 to 11

Economics 2020a / HBS 4010 / HKS API-111 Fall 2011 Practice Problems for Lectures 1 to 11 LECTURE 1: BUDGETS AND REVEALED PREFERENCE 1.1. Quantity Discounts and the Budget Constraint Suppose that a consumer

### Chapter 16. Law of averages. Chance. Example 1: rolling two dice Sum of draws. Setting up a. Example 2: American roulette. Summary.

Overview Box Part V Variability The Averages Box We will look at various chance : Tossing coins, rolling, playing Sampling voters We will use something called s to analyze these. Box s help to translate

### Capital Allocation Between The Risky And The Risk- Free Asset. Chapter 7

Capital Allocation Between The Risky And The Risk- Free Asset Chapter 7 Investment Decisions capital allocation decision = choice of proportion to be invested in risk-free versus risky assets asset allocation

### Chapter 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,

### 2. Information Economics

2. Information Economics In General Equilibrium Theory all agents had full information regarding any variable of interest (prices, commodities, state of nature, cost function, preferences, etc.) In many

### Decision Theory. 36.1 Rational prospecting

36 Decision Theory Decision theory is trivial, apart from computational details (just like playing chess!). You have a choice of various actions, a. The world may be in one of many states x; which one

### In the situations that we will encounter, we may generally calculate the probability of an event

What does it mean for something to be random? An event is called random if the process which produces the outcome is sufficiently complicated that we are unable to predict the precise result and are instead

### Statistics and Random Variables. Math 425 Introduction to Probability Lecture 14. Finite valued Random Variables. Expectation defined

Expectation Statistics and Random Variables Math 425 Introduction to Probability Lecture 4 Kenneth Harris kaharri@umich.edu Department of Mathematics University of Michigan February 9, 2009 When a large

### An Introduction to Utility Theory

An Introduction to Utility Theory John Norstad j-norstad@northwestern.edu http://www.norstad.org March 29, 1999 Updated: November 3, 2011 Abstract A gentle but reasonably rigorous introduction to utility

### Part I. Gambling and Information Theory. Information Theory and Networks. Section 1. Horse Racing. Lecture 16: Gambling and Information Theory

and Networks Lecture 16: Gambling and Paul Tune http://www.maths.adelaide.edu.au/matthew.roughan/ Lecture_notes/InformationTheory/ Part I Gambling and School of Mathematical

### Econ 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

### Informatics 2D Reasoning and Agents Semester 2, 2015-16

Informatics 2D Reasoning and Agents Semester 2, 2015-16 Alex Lascarides alex@inf.ed.ac.uk Lecture 29 Decision Making Under Uncertainty 24th March 2016 Informatics UoE Informatics 2D 1 Where are we? Last

### Moral Hazard. Itay Goldstein. Wharton School, University of Pennsylvania

Moral Hazard Itay Goldstein Wharton School, University of Pennsylvania 1 Principal-Agent Problem Basic problem in corporate finance: separation of ownership and control: o The owners of the firm are typically

### Economics 100A. Final Exam

Name form number 1 Economics 100A Final Exam Fill in the bubbles on your scantron with your id number (starting from the left side of the box), your name, and the form type. Students who do this successfully

### CHAPTER 10 RISK AND RETURN: THE CAPITAL ASSET PRICING MODEL (CAPM)

CHAPTER 10 RISK AND RETURN: THE CAPITAL ASSET PRICING MODEL (CAPM) Answers to Concepts Review and Critical Thinking Questions 1. Some of the risk in holding any asset is unique to the asset in question.

### Solution: 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

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

### Applying the Kelly criterion to lawsuits

Law, Probability and Risk (2010) 9, 139 147 Advance Access publication on April 27, 2010 doi:10.1093/lpr/mgq002 Applying the Kelly criterion to lawsuits TRISTAN BARNETT Faculty of Business and Law, Victoria

### Lecture 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

### CHAPTER 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

### Financial Markets. Itay Goldstein. Wharton School, University of Pennsylvania

Financial Markets Itay Goldstein Wharton School, University of Pennsylvania 1 Trading and Price Formation This line of the literature analyzes the formation of prices in financial markets in a setting

### Chapter 14 Risk Analysis

Chapter 14 Risk Analysis 1 Frequency definition of probability Given a situation in which a number of possible outcomes might occur, the probability of an outcome is the proportion of times that it occurs

### STOCHASTIC MODELING. Math3425 Spring 2012, HKUST. Kani Chen (Instructor)

STOCHASTIC MODELING Math3425 Spring 212, HKUST Kani Chen (Instructor) Chapters 1-2. Review of Probability Concepts Through Examples We review some basic concepts about probability space through examples,

### 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.

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

Review for Exam 2 Instructions: Please read carefully The exam will have 25 multiple choice questions and 5 work problems You are not responsible for any topics that are not covered in the lecture note

### Lesson 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.

### 6.254 : Game Theory with Engineering Applications Lecture 2: Strategic Form Games

6.254 : Game Theory with Engineering Applications Lecture 2: Strategic Form Games Asu Ozdaglar MIT February 4, 2009 1 Introduction Outline Decisions, utility maximization Strategic form games Best responses

### Economics of Insurance

Economics of Insurance In this last lecture, we cover most topics of Economics of Information within a single application. Through this, you will see how the differential informational assumptions allow

### 13.0 Central Limit Theorem

13.0 Central Limit Theorem Discuss Midterm/Answer Questions Box Models Expected Value and Standard Error Central Limit Theorem 1 13.1 Box Models A Box Model describes a process in terms of making repeated

### Exam Introduction Mathematical Finance and Insurance

Exam Introduction Mathematical Finance and Insurance Date: January 8, 2013. Duration: 3 hours. This is a closed-book exam. The exam does not use scrap cards. Simple calculators are allowed. The questions

### Geometric Series and Annuities

Geometric Series and Annuities Our goal here is to calculate annuities. For example, how much money do you need to have saved for retirement so that you can withdraw a fixed amount of money each year for

### Elementary Statistics and Inference. Elementary Statistics and Inference. 17 Expected Value and Standard Error. 22S:025 or 7P:025.

Elementary Statistics and Inference S:05 or 7P:05 Lecture Elementary Statistics and Inference S:05 or 7P:05 Chapter 7 A. The Expected Value In a chance process (probability experiment) the outcomes of

### Chapter 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

### The Values of Relative Risk Aversion Degrees

The Values of Relative Risk Aversion Degrees Johanna Etner CERSES CNRS and University of Paris Descartes 45 rue des Saints-Peres, F-75006 Paris, France johanna.etner@parisdescartes.fr Abstract This article

### Week 5: Expected value and Betting systems

Week 5: Expected value and Betting systems Random variable A random variable represents a measurement in a random experiment. We usually denote random variable with capital letter X, Y,. If S is the sample

### Elementary Statistics and Inference. Elementary Statistics and Inference. 16 The Law of Averages (cont.) 22S:025 or 7P:025.

Elementary Statistics and Inference 22S:025 or 7P:025 Lecture 20 1 Elementary Statistics and Inference 22S:025 or 7P:025 Chapter 16 (cont.) 2 D. Making a Box Model Key Questions regarding box What numbers

### Health Economics. University of Linz & Demand and supply of health insurance. Gerald J. Pruckner. Lecture Notes, Summer Term 2010

Health Economics Demand and supply of health insurance University of Linz & Gerald J. Pruckner Lecture Notes, Summer Term 2010 Gerald J. Pruckner Health insurance 1 / 25 Introduction Insurance plays a

### פרויקט מסכם לתואר בוגר במדעים )B.Sc( במתמטיקה שימושית

המחלקה למתמטיקה Department of Mathematics פרויקט מסכם לתואר בוגר במדעים )B.Sc( במתמטיקה שימושית הימורים אופטימליים ע"י שימוש בקריטריון קלי אלון תושיה Optimal betting using the Kelly Criterion Alon Tushia

### 6.042/18.062J Mathematics for Computer Science. Expected Value I

6.42/8.62J Mathematics for Computer Science Srini Devadas and Eric Lehman May 3, 25 Lecture otes Expected Value I The expectation or expected value of a random variable is a single number that tells you

### Final Exam Practice Set and Solutions

FIN-469 Investments Analysis Professor Michel A. Robe Final Exam Practice Set and Solutions What to do with this practice set? To help students prepare for the final exam, three practice sets with solutions

### Mathematical Expectation

Mathematical Expectation Properties of Mathematical Expectation I The concept of mathematical expectation arose in connection with games of chance. In its simplest form, mathematical expectation is the

### Discrete Math in Computer Science Homework 7 Solutions (Max Points: 80)

Discrete Math in Computer Science Homework 7 Solutions (Max Points: 80) CS 30, Winter 2016 by Prasad Jayanti 1. (10 points) Here is the famous Monty Hall Puzzle. Suppose you are on a game show, and you

### A Simple Parrondo Paradox. Michael Stutzer, Professor of Finance. 419 UCB, University of Colorado, Boulder, CO 80309-0419

A Simple Parrondo Paradox Michael Stutzer, Professor of Finance 419 UCB, University of Colorado, Boulder, CO 80309-0419 michael.stutzer@colorado.edu Abstract The Parrondo Paradox is a counterintuitive

### Homework Assignment #2: Answer Key

Homework Assignment #2: Answer Key Chapter 4: #3 Assuming that the current interest rate is 3 percent, compute the value of a five-year, 5 percent coupon bond with a face value of \$,000. What happens if

### Lecture 15. Ranking Payoff Distributions: Stochastic Dominance. First-Order Stochastic Dominance: higher distribution

Lecture 15 Ranking Payoff Distributions: Stochastic Dominance First-Order Stochastic Dominance: higher distribution Definition 6.D.1: The distribution F( ) first-order stochastically dominates G( ) if

### Regret and Rejoicing Effects on Mixed Insurance *

Regret and Rejoicing Effects on Mixed Insurance * Yoichiro Fujii, Osaka Sangyo University Mahito Okura, Doshisha Women s College of Liberal Arts Yusuke Osaki, Osaka Sangyo University + Abstract This papers

### Why is Insurance Good? An Example Jon Bakija, Williams College (Revised October 2013)

Why is Insurance Good? An Example Jon Bakija, Williams College (Revised October 2013) Introduction The United States government is, to a rough approximation, an insurance company with an army. 1 That is

### CHAPTER 4 Consumer Choice

CHAPTER 4 Consumer Choice CHAPTER OUTLINE 4.1 Preferences Properties of Consumer Preferences Preference Maps 4.2 Utility Utility Function Ordinal Preference Utility and Indifference Curves Utility and

### 1 Introduction to Option Pricing

ESTM 60202: Financial Mathematics Alex Himonas 03 Lecture Notes 1 October 7, 2009 1 Introduction to Option Pricing We begin by defining the needed finance terms. Stock is a certificate of ownership of

### Practice 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

### Pascal is here expressing a kind of skepticism about the ability of human reason to deliver an answer to this question.

Pascal s wager So far we have discussed a number of arguments for or against the existence of God. In the reading for today, Pascal asks not Does God exist? but Should we believe in God? What is distinctive

### Holding 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.

### c 2008 Je rey A. Miron We have described the constraints that a consumer faces, i.e., discussed the budget constraint.

Lecture 2b: Utility c 2008 Je rey A. Miron Outline: 1. Introduction 2. Utility: A De nition 3. Monotonic Transformations 4. Cardinal Utility 5. Constructing a Utility Function 6. Examples of Utility Functions