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4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
Asset PricingChapter IV. Measuring Risk and Risk Aversion
June 20, 2006
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
Utility functionIndifference Curves
Measuring Risk Aversion
U(Y + h)
U(Y)
U[0.5(Y + h) + 0.5(Y – h)]
0.5U(Y + h) + 0.5U(Y – h)
U(Y – h)
YY – h Y + hY
tangent lines
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
Utility functionIndifference Curves
Indifference Curves
c*1 c1
c2
c*2
State 2
Consumption
State 1
Consumption
(c*2 + c2)/2
EU(c) = k2
EU(c) = k1
(c*1 + c1)/ 2
I1 I2
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
Absolute Risk Aversion and the Odds of a BetRelative Risk Aversion in Relation to the Odds of a Bet
Arrow-Pratt measures of risk aversion and theirinterpretations
(i) absolute risk aversion = −U′′(Y )U′(Y ) ≡ RA(Y )
(ii) relative risk aversion = −YU′′(Y )U′(Y ) ≡ RR(Y ).
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
Absolute Risk Aversion and the Odds of a BetRelative Risk Aversion in Relation to the Odds of a Bet
Absoluterisk aversion = −U′′(Y )
U′(Y ) ≡ RA(Y )
π(Y , h) ∼= 1/2 + (1/4)hRA(Y ), (1)
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
Absolute Risk Aversion and the Odds of a BetRelative Risk Aversion in Relation to the Odds of a Bet
Relativerisk aversion = −YU′′(Y )
U′(Y ) ≡ RR(Y ).
π(Y , θ) ∼=12
+14θRR(Y ). (2)
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
Jensen’s InequalityCertainty Equivalent
4.4 Risk Premium and Certainty Equivalence
Theorem ((4.1) Jensen’s Inequality)
Let g( ) be a concave function on the interval (a, b), and x̃ be arandom variable such that Prob {x̃ ∈ (a, b)} = 1. Suppose theexpectations E(x̃) and Eg(x̃) exist; then
E [g(x̃)] ≤ g [E(x̃)] .
Furthermore, if g( ) is strictly concave and Prob{x̃ = E(x̃)} 6= 1, then the inequality is strict.
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
Jensen’s InequalityCertainty Equivalent
EU(Y + Z̃ ) = U(Y + CE(Y , Z̃ )) (3)= U(Y + EZ̃ − Π(Y , Z̃ )) (4)
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
Jensen’s InequalityCertainty Equivalent
Certainty Equivalent and Risk Premium: An illustration
Y0 Y0 + Z1 Y0 + Z2
U(Y0 + Z2)
U(Y0 + Z1)
U(Y0 + E(Z))~
EU(Y0 + Z)~
CE(Y0 + Z)~
Y0 + E(Z)~
Y
U(Y)
CE(Z)~
P
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
4.5 Assessing an Investor’s Level of Relative Risk Aversion
(Y + CE)1−γ
1− γ=
12(Y + 50, 000)1−γ
1− γ+
12(Y + 100, 000)1−γ
1− γ(5)
Assuming zero initial wealth (Y = 0), we obtain the following sampleresults (clearly, CE > 50,000):γ = 0 CE = 75,000 (risk neutrality)γ = 1 CE = 70,711γ = 2 CE = 66,667γ = 5 CE = 58,566γ = 10 CE = 53,991γ = 20 CE = 51,858γ = 30 CE = 51,209current wealth of Y = $100,000 and a degree of risk aversion of γ = 5,the equation results in a CE= $ 66,532.
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
First Order Stochastic DominanceSecond Order Stochastic Dominance
4.6 The Concept of Stochastic Dominance
In this section we show that the postulates of ExpectedUtility lead to a definition of two alternative concepts ofdominance which are weaker and this of wider applicationthan the concept of state-by-state dominance. These areof interest because they circumscribe the situations inwhich rankings among risky prospects are preference-free,ie., can be defined independently of the specific trade-offs(between return, risk and other characteristics ofprobability distributions) represented by an agent’s utilityfunction.
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
First Order Stochastic DominanceSecond Order Stochastic Dominance
Table 4.1: Sample Investment AlternativesPayoffs 10 100 2000Prob Z1 .4 .6 0Prob Z2 .4 .4 .2
EZ1 = 64, σz1= 44EZ2 = 444, σz2= 779
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
First Order Stochastic DominanceSecond Order Stochastic Dominance
0.1
0.2
0.3
0.4
0.5
0.6
0.8
0.7
1.0
0.9
0 10 100 2000
Payoff
Probability
F1 and F2
F2
F1
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
First Order Stochastic DominanceSecond Order Stochastic Dominance
Definition 4.1: First Order Stochastic Dominance FSD LetFA(x̃) and FB(x̃), respectively, represent thecumulative distribution functions of two randomvariables (cash payoffs) that, without loss ofgenerality assume values in the interval [a, b]. Wesay that FA(x̃) first order stochasticallydominates (FSD) FB(x̃) if and only ifFA(x) ≤ FB(x) for all x ∈ [a, b]
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
First Order Stochastic DominanceSecond Order Stochastic Dominance
First Order Stochastic Dominance: A More General Representation
0
0.3
0.5
0.6
0.8
0.1
0.2
0.4
1
0.9
0.7
0 1 2 3 4 5 6 8 10 127 9 11 13 14
x
FA
FB
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
First Order Stochastic DominanceSecond Order Stochastic Dominance
Theorem (4.2)
Let FA(x̃), FB(x̃), be two cumulative probability distributions forrandom payoffs x̃ ∈ [a, b]. Then FA(x̃) FSD FB(x̃) if and only ifEAU (x̃) ≥ EBU (x̃) for all non-decreasing utility functions U( ).
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
First Order Stochastic DominanceSecond Order Stochastic Dominance
Table 4.2: Two Independent Investments
Investment 3 Investment 4Payoff Prob. Payoff Prob.4 0.25 1 0.335 0.50 6 0.339 0.25 8 0.33
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
First Order Stochastic DominanceSecond Order Stochastic Dominance
Second Order Stochastic Dominance Illustrated
0
0.3
0.5
0.6
0.8
0.1
0.2
0.4
1
0.9
0.7
0 1 2 3 4 5 6 10 138 127 119
A
B
C
Investment 3
Investment 4
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
First Order Stochastic DominanceSecond Order Stochastic Dominance
Definition 4.2: Second Order Stochastic Dominance LetFA(x̃), FB(x̃), be two cumulative probabilitydistributions for random payoffs in [a, b]. We saythat FA(x̃) second order stochasticallydominates (SSD) FB(x̃) if and only if for any x :
x∫−∞
[ FB(t)− FA(t)] dt ≥ 0.
(with strict inequality for some meaningful intervalof values of t).
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
First Order Stochastic DominanceSecond Order Stochastic Dominance
Theorem (4.3)
Let FA(x̃), FB(x̃), be two cumulative probability distributions forrandom payoffs x̃ defined on [a, b]. Then, FA(x̃) SSD FB(x̃) ifand only if EAU (x̃) ≥ EBU (x̃) for all nondecreasing andconcave U.
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
4.7 More or less risky ∼= mean preserving spread
EA(x) = xf A(x)dx = xf B(x)dx = EB(x)
f A(x)
f B(x)
x, Payoff˜
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
Theorem (4.4)
Let FA( ) and FB( ) be two distribution functions defined on thesame state space with identical means. If this is true, thefollowing statements are equivalent:(i) FA(x̃) SSD FB(x̃)(ii) FB(x̃) is a mean preserving spread of FA(x̃) in the sense ofEquation
x̃B = x̃A + z̃ (6)
Asset Pricing
4.1 Measuring Risk Aversion4.2 Interpreting the Measures of Risk Aversion
4.4 Risk Premium and Certainty Equivalence4.5 Assessing an Investor’s Level of Relative Risk Aversion
4.6 The Concept of Stochastic Dominance4.7 Mean Preserving Spreads
4.8 Key Concepts
Key Concepts
Absolute and relative measures of risk aversionCertainty equivalence and risk premiumStochastic dominance and the reason for searching for thebroadest concept of dominance
Asset Pricing