Economics 1011a: Intermediate Microeconomics


 Ellen Jackson
 3 years ago
 Views:
Transcription
1 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 this topic a bit more formally. We will introduce a crucial concept: risk aversion. 1011a Lecture a Lecture 12 2 Some More Formal Notation Assume there are s states of the world. The probability of state 1 is p 1, the probability of state 2 is p 2, etc. Lotteries Any risk that you take in this world can be represented as a lottery. A lottery is defined by its payoff in each state. We sometimes represent these probability as a vector: For example, it might have payoff x 1 in state 1, x 2 in state 2, etc. Hence we can also represent this lottery as a vector: 1011a Lecture a Lecture 12 4
2 Expected Value The expected value of this lottery is its mean; the amount that it pays you on average. Expected Utility (I) The expected utility of this lottery is the amount of utility that it gives you on average. This is just its payoff in each state times the probability of that state occurring: Again we use the expectations operator: We use the expectations operator E to denote this sum: 1011a Lecture a Lecture 12 6 Expected Utility (II) Risk Aversion (I) Suppose that someone offers you a lottery with an expected value of zero. Note that the expected utility of a lottery depends on your initial wealth y. This means that rich and poor people may judge the same risk differently. This is called a fair bet. For example, I ll flip a coin. Heads you give me $10, tails I give you $ a Lecture 12 7 Would you take this bet? 1011a Lecture 12 8
3 Risk Aversion (II) What is the expected utility of this bet? How does this compare to not taking the bet? Risk Aversion and Concavity (I) Because of concavity, the utility gain when you win is smaller than the utility loss when you lose. u(y) 1011a Lecture 12 9 y10 y y a Lecture Risk Aversion and Concavity (II) Hence by taking the bet you lose utility in expectation, even though in $s you don t. u(y) Jensen s Inequality (I) There is nothing special about this bet. In fact, Jensen s Inequality says that u(y) is strictly concave if and only if you would turn down every fair bet: y10 y y a Lecture a Lecture 12 12
4 Jensen s Inequality (II) Certainty Equivalence (I) We can also write Jensen s Inequality as follows: What if you someone offers you a choice: 1)! You can play a lottery or This means you would prefer someone gives you the expected value of any bet upfront, rather than to actually take the bet. 2)! You can accept (or pay) a certain fixed amount of money c. Note that it does not matter if the bet has positive or negative expected value. 1011a Lecture Which do you take? 1011a Lecture Certainty Equivalence (II) Certainty Equivalence (III) Just compare the expected utilities of the two offers. This value c is called the certainty equivalent of the lottery. Remember that with the second there is no risk. For some value of c, these two are equal. Note that c depends on and y. Formally, we write is defined by: 1011a Lecture a Lecture 12 16
5 Certainty Equivalence & Fair Bets The certainty equivalent of a fair bet is always negative. You will pay to avoid it. u(y) Wealth and Risk (I) We keep mentioning that your attitude towards risk depends on how wealthy you are. But how does it depend on this? y10 y y+10 What might some reasonable assumptions be? 1011a Lecture a Lecture A Bet to Consider Risk Aversion Suppose I offer you the following bet: We flip a fair coin.! Heads I give you $1,000,000! Tails you give me $500,000 Would you take this bet? Would Bill Gates take this bet? It seems sensible that the rich will take bigger bets than the poor. This means that their risk aversion is decreasing. Formally you can define risk aversion as the amount you would pay to avoid small risks. It also turns out to be the curvature of the utility function. 1011a Lecture a Lecture 12 20
6 Measuring Risk Aversion (I) The coefficient of absolute risk aversion (named by John Pratt) is defined as Measuring Risk Aversion (II) The coefficient of relative risk aversion is defined as This measures your how much you would pay to avoid bets that are small in dollars. This measures how much you would pay to avoid bets that are small as a percentage of your wealth. Both of these ideas are used extensively in finance. 1011a Lecture a Lecture Constant Absolute Risk Aversion What if you have constant absolute risk aversion (CARA)? Constant Relative Risk Aversion What if you have constant relative risk aversion (CRRA)? Solving this differential equation we have: Solving this differential equation we have: Does CARA seem reasonable? Note that u is bounded from above by a Lecture or, for! = 1, Does CRRA seem more reasonable? 1011a Lecture 12 24
7 A special case of CRRA. Very easy to work with. What is strange about quadratic utility?. 1011a Lecture a Lecture With linear utility you are risk neutral. You will take any bet with positive expected value. Firms As an aside, remember we generally assume that firms only care about profits. This means firms utility is linear in income. So when a firm faces a risk, it only cares about the expected value of the risk. This means that a firm will take on any risk as long as it has positive expected value. 1011a Lecture Is this reasonable? 1011a Lecture 12 28
8 Should You Take Good Bets? Suppose someone offers you a bet with positive expected value. Will you take it? What About Small Good Bets? However, what if you could take as small or large a piece of the bet as you wanted? This depends on the size of the bet. In other words, you could take the bet Even if, it could easily be that For any scalar k. Now will you bet (i.e. choose k > 0)? 1011a Lecture a Lecture How Big A Bet To Take? (I) How Big A Bet To Take? (II) Now you are not choosing between the binary options You are maximizing, for k " 0: Differentiate with respect to k: I claim k=0 is not a maximum. 1011a Lecture a Lecture 12 32
9 How Big A Bet To Take? (III) Now plug in k=0 What if Income Changed? Note that here we were assuming that income y was the same in all states. If this was not true, then clearly this result would not necessarily hold. Since v (0) > 0, this is not a maximum. You can improve with a positive k. 1011a Lecture For example, the bet might only pay off in a state where you already had lots of money. 1011a Lecture Critiques of Expected Utility There is a lot of experimental evidence to show that people do not do maximize expected utility. The Allais Paradox (I) You can choose between 2 lotteries: Lottery A: Lottery B: In particular, people treat p=0 very differently from p=! (a small number). $1,000,000 (p = 1) Which do you pick? $5,000,000 (p = 0.10) $1,000,000 (p = 0.89) $0 (p = 0.01) 1011a Lecture a Lecture 12 36
10 The Allais Paradox (II) What about these two: Allais and Expected Utility Theory (I) If you picked A over B, this means: Lottery C: $1,000,000 (p = 0.11) $0 (p = 0.89) Lottery D: $5,000,000 (p = 0.10) $0 (p = 0.90) But if you picked D over C, this means: Now which do you pick? 1011a Lecture What s wrong with this? 1011a Lecture Allais and Expected Utility Theory (II) The Ellsberg Paradox (I) I have two jars, each filled with 100 balls. Jar A: 50 Red Balls 50 Black Balls Jar B: x Red Balls 1x Black Balls Why do you think people prefer A to B, but D to C? I will now pick a ball at random from 1 of the jars. 1011a Lecture a Lecture 12 40
11 The Ellsberg Paradox (II) If I pick a red ball, you win $20. If I pick a black ball you get nothing. You get to choose which jar I draw from. The Ellsberg Paradox (III) OK, let s play again (same jars). Now if I pick a black ball, you win $20. If I pick a red ball you get nothing. Again you get to choose which jar I draw from. Who wants me to draw from Jar A? Now who wants me to draw from Jar A? What s going on here? 1011a Lecture a Lecture 12 42
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
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 informationLecture 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
More informationLecture 11 Uncertainty
Lecture 11 Uncertainty 1. Contingent Claims and the StatePreference Model 1) Contingent Commodities and Contingent Claims Using the simple twogood model we have developed throughout this course, think
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 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 informationLecture 15. Ranking Payoff Distributions: Stochastic Dominance. FirstOrder Stochastic Dominance: higher distribution
Lecture 15 Ranking Payoff Distributions: Stochastic Dominance FirstOrder Stochastic Dominance: higher distribution Definition 6.D.1: The distribution F( ) firstorder stochastically dominates G( ) if
More informationProbability 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
More informationAsset 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
More information36 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
More informationMoral Hazard. Itay Goldstein. Wharton School, University of Pennsylvania
Moral Hazard Itay Goldstein Wharton School, University of Pennsylvania 1 PrincipalAgent Problem Basic problem in corporate finance: separation of ownership and control: o The owners of the firm are typically
More informationRisk and Insurance. Vani Borooah University of Ulster
Risk and Insurance Vani Borooah University of Ulster Gambles An action with more than one possible outcome, such that with each outcome there is an associated probability of that outcome occurring. If
More informationFund 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
More informationC2922 Economics Utility Functions
C2922 Economics Utility Functions T.C. Johnson October 30, 2007 1 Introduction Utility refers to the perceived value of a good and utility theory spans mathematics, economics and psychology. For example,
More informationMA 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
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 informationLecture notes for Choice Under Uncertainty
Lecture notes for Choice Under Uncertainty 1. Introduction In this lecture we examine the theory of decisionmaking under uncertainty and its application to the demand for insurance. The undergraduate
More informationFinancial 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
More informationChapter 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
More informationECO 317 Economics of Uncertainty Fall Term 2009 Week 5 Precepts October 21 Insurance, Portfolio Choice  Questions
ECO 37 Economics of Uncertainty Fall Term 2009 Week 5 Precepts October 2 Insurance, Portfolio Choice  Questions Important Note: To get the best value out of this precept, come with your calculator or
More informationDecision 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
More informationRisk 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
More information1 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
More informationK 1 < K 2 = P (K 1 ) P (K 2 ) (6) This holds for both American and European Options.
Slope and Convexity Restrictions and How to implement Arbitrage Opportunities 1 These notes will show how to implement arbitrage opportunities when either the slope or the convexity restriction is violated.
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 informationAMS 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
More information3.2 Roulette and Markov Chains
238 CHAPTER 3. DISCRETE DYNAMICAL SYSTEMS WITH MANY VARIABLES 3.2 Roulette and Markov Chains In this section we will be discussing an application of systems of recursion equations called Markov Chains.
More informationDemand 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
More information1 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
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 informationIntroduction 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
More informationMinimax Strategies. Minimax Strategies. Zero Sum Games. Why Zero Sum Games? An Example. An Example
Everyone who has studied a game like poker knows the importance of mixing strategies With a bad hand, you often fold But you must bluff sometimes Lectures in MicroeconomicsCharles W Upton Zero Sum Games
More informationSection 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
More informationManagerial Economics
Managerial Economics Unit 9: Risk Analysis Rudolf WinterEbmer Johannes Kepler University Linz Winter Term 2012 Managerial Economics: Unit 9  Risk Analysis 1 / 1 Objectives Explain how managers should
More informationPascal 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
More informationAn Introduction to Utility Theory
An Introduction to Utility Theory John Norstad jnorstad@northwestern.edu http://www.norstad.org March 29, 1999 Updated: November 3, 2011 Abstract A gentle but reasonably rigorous introduction to utility
More informationA 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 379960550 wneilson@utk.edu March 2009 Abstract This paper takes the
More informationIntermediate 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
More informationInvestment Decision Analysis
Lecture: IV 1 Investment Decision Analysis The investment decision process: Generate cash flow forecasts for the projects, Determine the appropriate opportunity cost of capital, Use the cash flows and
More informationDiscrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 13. Random Variables: Distribution and Expectation
CS 70 Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 3 Random Variables: Distribution and Expectation Random Variables Question: The homeworks of 20 students are collected
More informationDecision 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
More information6.042/18.062J 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
More information1 Interest rates, and riskfree investments
Interest rates, and riskfree 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)
More informationElementary 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
More informationLINES AND PLANES CHRIS JOHNSON
LINES AND PLANES CHRIS JOHNSON Abstract. In this lecture we derive the equations for lines and planes living in 3space, as well as define the angle between two nonparallel planes, and determine the distance
More informationElementary 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
More informationarxiv:1112.0829v1 [math.pr] 5 Dec 2011
How Not to Win a Million Dollars: A Counterexample to a Conjecture of L. Breiman Thomas P. Hayes arxiv:1112.0829v1 [math.pr] 5 Dec 2011 Abstract Consider a gambling game in which we are allowed to repeatedly
More informationPROBABILITY NOTIONS. Summary. 1. Random experiment
PROBABILITY NOTIONS Summary 1. Random experiment... 1 2. Sample space... 2 3. Event... 2 4. Probability calculation... 3 4.1. Fundamental sample space... 3 4.2. Calculation of probability... 3 4.3. Non
More informationBetting 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
More informationBasic Utility Theory for Portfolio Selection
Basic Utility Theory for Portfolio Selection In economics and finance, the most popular approach to the problem of choice under uncertainty is the expected utility (EU) hypothesis. The reason for this
More informationStatistics 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
More information1/3 1/3 1/3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0 1 2 3 4 5 6 7 8 0.6 0.6 0.6 0.6 0.6 0.6 0.6
HOMEWORK 4: SOLUTIONS. 2. A Markov chain with state space {, 2, 3} has transition probability matrix /3 /3 /3 P = 0 /2 /2 0 0 Show that state 3 is absorbing and, starting from state, find the expected
More informationRegression Analysis Prof. Soumen Maity Department of Mathematics Indian Institute of Technology, Kharagpur. Lecture  2 Simple Linear Regression
Regression Analysis Prof. Soumen Maity Department of Mathematics Indian Institute of Technology, Kharagpur Lecture  2 Simple Linear Regression Hi, this is my second lecture in module one and on simple
More informationCombinatorics: The Fine Art of Counting
Combinatorics: The Fine Art of Counting Week 7 Lecture Notes Discrete Probability Continued Note Binomial coefficients are written horizontally. The symbol ~ is used to mean approximately equal. The Bernoulli
More informationWeek 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
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 realvalued function defined on the sample space of some experiment. For instance,
More informationThe overall size of these chance errors is measured by their RMS HALF THE NUMBER OF TOSSES NUMBER OF HEADS MINUS 0 400 800 1200 1600 NUMBER OF TOSSES
INTRODUCTION TO CHANCE VARIABILITY WHAT DOES THE LAW OF AVERAGES SAY? 4 coins were tossed 1600 times each, and the chance error number of heads half the number of tosses was plotted against the number
More informationA Note on Proebsting s Paradox
A Note on Proebsting s Paradox Leonid Pekelis March 8, 2012 Abstract Proebsting s Paradox is twostage bet where the naive Kelly gambler (wealth growth rate maximizing) can be manipulated in some disconcertingly
More informationMATHEMATICS FOR ENGINEERS STATISTICS TUTORIAL 4 PROBABILITY DISTRIBUTIONS
MATHEMATICS FOR ENGINEERS STATISTICS TUTORIAL 4 PROBABILITY DISTRIBUTIONS CONTENTS Sample Space Accumulative Probability Probability Distributions Binomial Distribution Normal Distribution Poisson Distribution
More informationThe degree of a polynomial function is equal to the highest exponent found on the independent variables.
DETAILED SOLUTIONS AND CONCEPTS  POLYNOMIAL FUNCTIONS Prepared by Ingrid Stewart, Ph.D., College of Southern Nevada Please Send Questions and Comments to ingrid.stewart@csn.edu. Thank you! PLEASE NOTE
More information13.0 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
More informationSlide 1 Math 1520, Lecture 23. This lecture covers mean, median, mode, odds, and expected value.
Slide 1 Math 1520, Lecture 23 This lecture covers mean, median, mode, odds, and expected value. Slide 2 Mean, Median and Mode Mean, Median and mode are 3 concepts used to get a sense of the central tendencies
More informationWhy 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
More informationQuestion: What is the probability that a fivecard poker hand contains a flush, that is, five cards of the same suit?
ECS20 Discrete Mathematics Quarter: Spring 2007 Instructor: John Steinberger Assistant: Sophie Engle (prepared by Sophie Engle) Homework 8 Hints Due Wednesday June 6 th 2007 Section 6.1 #16 What is the
More informationAdaptive Online Gradient Descent
Adaptive Online Gradient Descent Peter L Bartlett Division of Computer Science Department of Statistics UC Berkeley Berkeley, CA 94709 bartlett@csberkeleyedu Elad Hazan IBM Almaden Research Center 650
More informationLecture 13. Understanding Probability and LongTerm Expectations
Lecture 13 Understanding Probability and LongTerm Expectations Thinking Challenge What s the probability of getting a head on the toss of a single fair coin? Use a scale from 0 (no way) to 1 (sure thing).
More informationLecture 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
More informationOn the Efficiency of Competitive Stock Markets Where Traders Have Diverse Information
Finance 400 A. Penati  G. Pennacchi Notes on On the Efficiency of Competitive Stock Markets Where Traders Have Diverse Information by Sanford Grossman This model shows how the heterogeneous information
More informationV. 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
More information$2 4 40 + ( $1) = 40
THE EXPECTED VALUE FOR THE SUM OF THE DRAWS In the game of Keno there are 80 balls, numbered 1 through 80. On each play, the casino chooses 20 balls at random without replacement. Suppose you bet on the
More informationThe New Mexico Lottery
The New Mexico Lottery 26 February 2014 Lotteries 26 February 2014 1/27 Today we will discuss the various New Mexico Lottery games and look at odds of winning and the expected value of playing the various
More informationHow to build a probabilityfree casino
How to build a probabilityfree casino Adam Chalcraft CCR La Jolla dachalc@ccrwest.org Chris Freiling Cal State San Bernardino cfreilin@csusb.edu Randall Dougherty CCR La Jolla rdough@ccrwest.org Jason
More informationChapter 21: The Discounted Utility Model
Chapter 21: The Discounted Utility Model 21.1: Introduction This is an important chapter in that it introduces, and explores the implications of, an empirically relevant utility function representing intertemporal
More informationInsurance. 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
More informationHomework 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 fiveyear, 5 percent coupon bond with a face value of $,000. What happens if
More informationDiscrete Structures for Computer Science
Discrete Structures for Computer Science Adam J. Lee adamlee@cs.pitt.edu 6111 Sennott Square Lecture #20: Bayes Theorem November 5, 2013 How can we incorporate prior knowledge? Sometimes we want to know
More informationMath 141. Lecture 2: More Probability! Albyn Jones 1. jones@reed.edu www.people.reed.edu/ jones/courses/141. 1 Library 304. Albyn Jones Math 141
Math 141 Lecture 2: More Probability! Albyn Jones 1 1 Library 304 jones@reed.edu www.people.reed.edu/ jones/courses/141 Outline Law of total probability Bayes Theorem the Multiplication Rule, again Recall
More informationChapter 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
More informationYou Are What You Bet: Eliciting Risk Attitudes from Horse Races
You Are What You Bet: Eliciting Risk Attitudes from Horse Races PierreAndré Chiappori, Amit Gandhi, Bernard Salanié and Francois Salanié March 14, 2008 What Do We Know About Risk Preferences? Not that
More informationUniversity of Oslo Department of Economics
University of Oslo Department of Economics Exam: ECON3200/4200 Microeconomics and game theory Date of exam: Tuesday, November 26, 2013 Grades are given: December 17, 2013 Duration: 14:3017:30 The problem
More informationJUST THE MATHS UNIT NUMBER 1.8. ALGEBRA 8 (Polynomials) A.J.Hobson
JUST THE MATHS UNIT NUMBER 1.8 ALGEBRA 8 (Polynomials) by A.J.Hobson 1.8.1 The factor theorem 1.8.2 Application to quadratic and cubic expressions 1.8.3 Cubic equations 1.8.4 Long division of polynomials
More informationWeek 2: Conditional Probability and Bayes formula
Week 2: Conditional Probability and Bayes formula We ask the following question: suppose we know that a certain event B has occurred. How does this impact the probability of some other A. This question
More informationProbability OPRE 6301
Probability OPRE 6301 Random Experiment... Recall that our eventual goal in this course is to go from the random sample to the population. The theory that allows for this transition is the theory of probability.
More informationFeb 7 Homework Solutions Math 151, Winter 2012. Chapter 4 Problems (pages 172179)
Feb 7 Homework Solutions Math 151, Winter 2012 Chapter Problems (pages 172179) Problem 3 Three dice are rolled. By assuming that each of the 6 3 216 possible outcomes is equally likely, find the probabilities
More informationChapter 16: law of averages
Chapter 16: law of averages Context................................................................... 2 Law of averages 3 Coin tossing experiment......................................................
More informationSecond Midterm Exam (MATH1070 Spring 2012)
Second Midterm Exam (MATH1070 Spring 2012) Instructions: This is a one hour exam. You can use a notecard. Calculators are allowed, but other electronics are prohibited. 1. [60pts] Multiple Choice Problems
More informationWe never talked directly about the next two questions, but THINK about them they are related to everything we ve talked about during the past week:
ECO 220 Intermediate Microeconomics Professor Mike Rizzo Third COLLECTED Problem Set SOLUTIONS This is an assignment that WILL be collected and graded. Please feel free to talk about the assignment with
More information1 Portfolio mean and variance
Copyright c 2005 by Karl Sigman Portfolio mean and variance Here we study the performance of a oneperiod investment X 0 > 0 (dollars) shared among several different assets. Our criterion for measuring
More informationChapter 7 Part 2. Hypothesis testing Power
Chapter 7 Part 2 Hypothesis testing Power November 6, 2008 All of the normal curves in this handout are sampling distributions Goal: To understand the process of hypothesis testing and the relationship
More information" Y. Notation and Equations for Regression Lecture 11/4. Notation:
Notation: Notation and Equations for Regression Lecture 11/4 m: The number of predictor variables in a regression Xi: One of multiple predictor variables. The subscript i represents any number from 1 through
More informationHealth 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
More informationMONEY MANAGEMENT. Guy Bower delves into a topic every trader should endeavour to master  money management.
MONEY MANAGEMENT Guy Bower delves into a topic every trader should endeavour to master  money management. Many of us have read Jack Schwager s Market Wizards books at least once. As you may recall it
More informationHONORS STATISTICS. Mrs. Garrett Block 2 & 3
HONORS STATISTICS Mrs. Garrett Block 2 & 3 Tuesday December 4, 2012 1 Daily Agenda 1. Welcome to class 2. Please find folder and take your seat. 3. Review OTL C7#1 4. Notes and practice 7.2 day 1 5. Folders
More informationWe 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 136, and there are two compartments labeled 0 and 00. Half of the compartments numbered 136
More informationMTH6120 Further Topics in Mathematical Finance Lesson 2
MTH6120 Further Topics in Mathematical Finance Lesson 2 Contents 1.2.3 Nonconstant interest rates....................... 15 1.3 Arbitrage and BlackScholes Theory....................... 16 1.3.1 Informal
More informationAnalyzing the Decision Criteria of Software Developers Based on Prospect Theory
Analyzing the Decision Criteria of Software Developers Based on Prospect Theory Kanako Kina, Masateru Tsunoda Department of Informatics Kindai University Higashiosaka, Japan tsunoda@info.kindai.ac.jp Hideaki
More informationMicroeconomics Tutoral 5: Choice under Uncertainty and Revealed Preferences October, 21th
Université Paris 1 Panthéon Sorbonne Microeconomics: PSME  ngelo Secchi 1 Master I, cademic year 20152016 Microeconomics tutorials supervised by Elisa Baku 2 and ntoine Marsaudon 3. Microeconomics Tutoral
More informationcalculating probabilities
4 calculating probabilities Taking Chances What s the probability he s remembered I m allergic to nonprecious metals? Life is full of uncertainty. Sometimes it can be impossible to say what will happen
More information