Economics 2301 Lecture 25 Risk Aversion. Figure 7.7 Utility Functions for Risk-Averse and Risk-Loving Individuals.

Slides:



Advertisements
Similar presentations
Objective : 1)Students will be able to identify linear, exponential, and quadratic equations when given an equation, graph, or table. 2)Students will be.
Advertisements

Recall Taylor’s Theorem from single variable calculus:
Converting Risk Preferences into Money Equivalents with Quadratic Programming AEC 851 – Agribusiness Operations Management Spring, 2006.
Copyright © 2006 The McGraw-Hill Companies, Inc. Permission required for reproduction or display. by Lale Yurttas, Texas A&M University Chapter 61.
1 CALCULUS Even more graphing problems
Copyright © Cengage Learning. All rights reserved.
L1: Risk and Risk Measurement1 Lecture 1: Risk and Risk Measurement We cover the following topics in this part –Risk –Risk Aversion Absolute risk aversion.
Lectures in Microeconomics-Charles W. Upton Mathematical Cost Functions C= 10+20q+4q 2.
Chapter 1 Introduction The solutions of engineering problems can be obtained using analytical methods or numerical methods. Analytical differentiation.
Lecture 38 Constrained Optimization
Economics 214 Lecture 29 Multivariate Calculus. Homogeneous Function.
Economics 214 Lecture 19 Univariate Calculus. At the Margin There is a direct correspondence between the mathematical concept of the derivative and the.
Expected Utility, Mean-Variance and Risk Aversion Lecture VII.
N 58 Graphical Solutions to Quadratic Functions Subject Content Reference: N6.7h GCSE Maths Number & Algebra.
1.3 Solving Equations Using a Graphing Utility; Solving Linear and Quadratic Equations.
Unit 1 Test Review Answers
Taylor Series & Error. Series and Iterative methods Any series ∑ x n can be turned into an iterative method by considering the sequence of partial sums.
PATTERNS. There are 4 types of patterns : 1. Geometric 2. Linear 3. n th term 4. Quadratic.
Economics 2301 Lecture 15 Differential Calculus. Difference Quotient.
Infinite Series Copyright © Cengage Learning. All rights reserved.
Chapter 2.5 Graphs of Basic Functions. Continuity Earlier in this chapter we graphed linear functions. The graph of a linear function, a straight line,
Role of Zero in Factoring
Find the local linear approximation of f(x) = e x at x = 0. Find the local quadratic approximation of f(x) = e x at x = 0.
Consider the following: Now, use the reciprocal function and tangent line to get an approximation. Lecture 31 – Approximating Functions
Do Now: Find both the local linear and the local quadratic approximations of f(x) = e x at x = 0 Aim: How do we make polynomial approximations for a given.
MA/CS 375 Fall MA/CS 375 Fall 2002 Lecture 31.
MAT 1234 Calculus I Section 2.4 Derivatives of Tri. Functions
Section 9.4 Infinite Series: “Convergence Tests”.
Warm up Construct the Taylor polynomial of degree 5 about x = 0 for the function f(x)=ex. Graph f and your approximation function for a graphical comparison.
Basic Economic Concepts Ways to organize an economic system –Free markets –Government-run Nature of decision making –Objectives –Constraints.
Section 9.7 Infinite Series: “Maclaurin and Taylor Polynomials”
MATH 6B CALCULUS II 11.3 Taylor Series. Determining the Coefficients of the Power Series Let We will determine the coefficient c k by taking derivatives.
Lecture Note 2 – Calculus and Probability Shuaiqiang Wang Department of CS & IS University of Jyväskylä
Finding the Minimum Using Newton’s Method Lecture I.
Computers in Civil Engineering 53:081 Spring 2003 Lecture #8 Roots of Equations: Systems of Equations.
Maclaurin and Taylor Polynomials Objective: Improve on the local linear approximation for higher order polynomials.
Zooming In. Objectives  To define the slope of a function at a point by zooming in on that point.  To see examples where the slope is not defined. 
PHY221 Ch21: SHM 1.Main Points: Spring force and simple harmonic motion in 1d General solution a Cos  t+  Plot x, v, a 2.Example Vertical spring with.
Chapter 10 Power Series Approximating Functions with Polynomials.
In this section, we will investigate how we can approximate any function with a polynomial.
MTH 253 Calculus (Other Topics) Chapter 11 – Infinite Sequences and Series Section 11.8 –Taylor and Maclaurin Series Copyright © 2009 by Ron Wallace, all.
Computational Fluid Dynamics Lecture II Numerical Methods and Criteria for CFD Dr. Ugur GUVEN Professor of Aerospace Engineering.
Infinite Series 9 Copyright © Cengage Learning. All rights reserved.
S ECT. 9-4 T AYLOR P OLYNOMIALS. Taylor Polynomials In this section we will be finding polynomial functions that can be used to approximate transcendental.
Linear Approximations. In this section we’re going to take a look at an application not of derivatives but of the tangent line to a function. Of course,
Theory of Capital Markets
Copyright © Cengage Learning. All rights reserved.
The Simple Linear Regression Model: Specification and Estimation
Interpolation Estimation of intermediate values between precise data points. The most common method is: Although there is one and only one nth-order.
Chapter 18.
Lecture 25 – Power Series Def: The power series centered at x = a:
6-7 Graphing and Solving Quadratic Inequalities
Section 11.3 – Power Series.
Starting problems Write a tangent line approximation for each:
Chapter 18.
Taylor Polynomials & Approximation (9.7)
Taylor and MacLaurin Series
Know to check all solutions
Section 11.3 Power Series.
11.1 – Polynomial Approximations of Functions
Numerical Integration:
y x y = x + 2 y = x + 4 y = x – 1 y = -x – 3 y = 2x y = ½x y = 3x + 1
30 m 2000 m 30 m 2000 m. 30 m 2000 m 30 m 2000 m.
THIS IS.
Quadratic Graphs.
Trig graphs and transformations
LINEAR & QUADRATIC GRAPHS
Equations, Identities and Formulae
Copyright © Cengage Learning. All rights reserved.
Lecture 38 Constrained Optimization
Presentation transcript:

Economics 2301 Lecture 25 Risk Aversion

Figure 7.7 Utility Functions for Risk-Averse and Risk-Loving Individuals

Example-Risk Adverse

Proof of Risk Adverse

Example-Risk Lover

Proof Risk Lover

Person who is both risk-lover and risk-adverse

Plot of Marginal Utility and Second Derivative

Proof of Properties of both risk-lover and risk-adverse

Linearization of Functions and Taylor Series It is often useful to be able to express a function in linear form. Helpful if we want to use matrix algebra or simple econometrics. Calculus can give us a linear approximation to a function.

Figure 7.8 Taylor Series Approximations

Linearization

Quadratic Approximation

Nth Degree Taylor Expansion

Example

Graph of our approximations

Accuracy of our approximation