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Differentiability of expected utility functions, 338, 350 Differentiation and integration interchangeability, 209, 344, 348, 349, 360 Disutility, 347 Disutility of regret, 579 Diversification, 83, 175, 176, 206, 208, 210, 211, 240, 277-289, 333, 334, 337; 347, 357, 360, 361, 378, 379 Dollar averaging, 442 Dollar cost averaging, 211 Dual variables, 6, 68, 69 Dynamic programming, 7-9, 43-55, 62-65, 189-191, 434, 438, 439, 442, 450, 688 Dynamic stochastic programming,see Stochastic dynamic programming E Efficiency criteria, see Stochastic dominance Efficient frontier, 135, 139, 166, 179 Efficient sets, 89, 90, 170, 334, 335, 607, 608 Efficient surface, 249, 250, 333, 335 Ergodic theorem, 444, 691-692 Expected utility theorem, 3-5, 11-21, 57, 60, 368, 415 F Farkas' lemma, 5, 71, 72 Financial intermediation, 211, 344, 345 Fisher's theory of savings, 429, 430 Foreign exchange, 84, 85, 180, 183-185 Fractional programming, 179, 245, 331 Frank-Wolfe algorithm, 6, 38, 68, 209, 210, 212, 349, 350, 357, 358 Friedman-Savage paradox, 3, 58, 61 G Generalized convex functions, 23-41, 56, 58-60, 68-70, 73 Generalized risk measure, 351 Geometric Brownian motion, 621, 622, 642, 649, 654, 657 Geometric mean criterion, 447-451, 593-598, 671-675, 696-698 Global optimality, 5, 23-41, 60 Hessian matrix, 5, 28, 70, 280, 331, 362 Homogeneous risk measures, 86, 173, 174 H Horse lotteries, 3, 12-21 Hyperbolic absolute risk aversion, 169, 370, 401-411, 422-424, 450, 451, 622, 637, 639, 640, 642, 651 I Increasing absolute risk aversion, 96, 123-127, 178, 187, 212-214, 298, 300, 302, 303, 407, 475 Increasing relative risk aversion, 85, 123-128, 183-185, 187, 188, 198, 212-214, 293-297, 301-303, 306, 433, 501-514, 617, 682 Induced utility functions, see Utility functions Inferior goods, 84, 174 Infinite horizon dynamic programming, 8,75-77 Insurance, 3, 84, 85, 174, 175, 180, 430 Insurance premium, see Risk premium Interest rates, 439, 440, 668, 669, 689, 690 Isoelastic utility function, see Constant relative risk aversion Ito process, 454-457, 622, 623 J Jensen's inequality, 4, 58, 109, 356, 677, 680 K Kelly criterion, 447-451, 599-617, 671-675, 696-698 Kuhn-Tucker conditions, 5, 23-32, 59-61, 71, 72, 173,207, 251,323,327,333,334,336, 337, 339, 356, 357, 361, 519, 671, 673 L Lagrangian, 5, 8, 60, 62, 63, 69, 333, 362 Linear complementarity problem, 86, 196 Linear programming, 8, 62-64, 368, 380, 431, 439, 440, 445, 683, 684-688 Linear utility functions, see Utility functions Linear risk tolerance, see Hyperbolic absolute risk aversion Liquidity considerations, 432, 663, 664 Local optima, 60 Local propensity to insure, 119, see also Risk premium Local risk aversion, see Risk aversion, measures of Local separation, 158, 159, 167, 168, 190, 191, see also Separation theorems 716 INDEX
Logarithmic utility functions, see Utility functions Log-normal distribution, 193, 194, 204, 212, 221, 222, 229-235, 353-356, 369, 415, 450, 451, 455, 614, 621, 632, 635, 640, 641,656,700 M Marginal propensity to consume, 429,461,466, 679, 663 Markov chains, 444, 446, 670, 671, 690-692 Markovian decision processes, 8, 75-77, 446, 683-688 Markovian regeneration property, 45, 49 Martingales, 84, 448, 449, 672 Mean-dispersion analysis, 206, 333, 334, 339, 346,347, 351, 352, see a/so Mean-variance analysis Mean-variance analysis, 5, 82, 85, 89, 95-99, 172, 178, 203-212, 215-220, 235-246, 315, 331, 337, 343-345, 351, 357, 449, 450,517, 632,635,671 Minimax strategies, 211, 347, 439, 442, 447, 577, 669, 670 Minimum variance portfolios, 335 Monotonicity assumption, 8, 9, 45, 47, 48, 54, 62,65 Multiplicative utility functions, see Utility functions Mutual fund theorems, see Separation theorems Mutual funds, 86, 179 Myopic policy, 367, 370, 371, 389, 401-411, 422-425, 437, 447 N Negative exponential utility functions, see Utility functions Nonsingular variance-covariance matrix, 86, 179 Normal distribution, 205-208, 213, 215, 287, 321, 338, 339, 343, 352, 360, 440 Normative utility theory, 601 O Optimal debt levels, 571 Optimal portfolio policies, existence of, 208-212, 267-276, 332, 344 Optimality postulate, 8, 44, 53, see also Dynamic programming INDEX Option strategies, 438-440,446, 669, 683, 684, 690 P Partial myopia, 370,401-411,422-424, see also Myopic policy Perfect information, expected value, 429, 430, 463, 465, 467, 680, 681 Perfect insurance, 469, 478 Perfect markets for risks, 459, 468-474 Piecewise concave functions, 572 Plunging in portfolio selection, 84, 196, 197 Policy improvement, 8, 76, 77, see also Dynamic programming Portfolio revision, 429,431,434,663,666-668, 671 Positive linear transformation, 3, 11, 84, 89, 370, 401, 403, 414 Power utility functions, see Utility functions Predictive utility theory, 601 Prescriptive utility theory, 601 Principle of optimality, 7, 53, 402, see also Dynamic programming Pseudo-concave functions, 243, 245, 254, 255, 260, 262, 331, 338, 360 Pseudo-convex functions, 23-41, 56, 58-60, 68-70 Put options, 553-562, see also Option strategies Q Quadratic programming, 86, 179,208,212, 331, 336, 337 Quadratic utility functions, see Utility functions Quasi-convex functions, 5, 23-41, 58-60, 68-70, 259, 261, 335, 461 R Random noise, 84, 188, 189, 191 Random walk, 440, 444, 445, 621, 692-694 Regret, 347, 579, 585, 590, 670 Regret criterion, 211, 347, 439, 442, 447, 669, 670 Reinsurance, 83, 197, 198 Relative risk aversion, 84, 85, 115-129, 174, 175, 178, 183, 185-188, 198, 213, 215, 291-311, 370, 407, 422-424, 433, 435, 506, 511, 512, 517, 525, 617, 682 Renewal process, 444, 690 717
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STOCHASTIC OPTIMIZATION MODELS IN F
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STOCHASTIC OPTIMIZATION MODELS IN F
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Dedicated to the memory of my fathe
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PART II. QUALITATIVE ECONOMIC RESUL
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2. Risk Aversion over Time Implies
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PREFACE AND BRIEF NOTES TO THE 2006
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and shows that current research in
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1990. This area presages the CVaR l
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transaction cost band. Most such mo
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through targets and are less sensit
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ing dates, the papers by Breiman, H
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Carino, D. and W.T. Ziemba (1998).
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Kallberg, J.G. and W.T. Ziemba (198
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Stone, D. and W.T. Ziemba (1993). L
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PREFACE IN 1975 EDITION There is no
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particular results are generally st
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Part I Mathematical Tools
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wealth, then his utility function m
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cases in Exercise CR-12. The relati
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However, some extensions to infinit
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1. EXPECTED UTILITY THEORY The Anna
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SUBJECTIVE PROBABILITIES ANT EXPECT
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SUBJECTIVE PROBABILITIES AND EXPECT
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282 O. L. MANGA3ARIAN" for every X
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284 O. L. MANGASARIAN We have from
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280 O. L. MANQASARIAN For the case
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288 O. L. MANGASAMAN Proof. Conside
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290 O. L. MANGASARIAN Second Berkel
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denotes the /w-dimensional vector o
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Proof (I) We shall prove this part
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(Ill) have not, to the author's kno
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for the case (3); and for the case
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3. DYNAMIC PROGRAMMING Introduction
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INTRODUCTION TO DYNAMIC PROGRAMMING
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COMPUTATIONAL AND REVIEW EXERCISES
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(d) xxx2 on E+ 2 = {x\xt = 0, x2 S
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Show that the Kuhn-Tucker condition
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(a) Show that/, (b, c) = gt (Z>, c)
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(c) Show that the monotonicity assu
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An individual's preferences are sai
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12. (Hessians, bordered Hessians, c
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The problem in (b)-(c) hints that t
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(1) Suppose/is twice continuously d
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(f) Assume that R(i,a) g 0 and the
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(j) Show that an optimal policy Va
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INTRODUCTION In the second part of
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andom variables when the utility fu
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detailed analysis of the qualitativ
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separation in a local sense (for al
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1. STOCHASTIC DOMINANCE G. Hanoch a
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But now, EFFICIENCY ANALYSIS OF CHO
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EFFICIENCY ANALYSIS OF CHOICES INVO
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A Unified Approach to Stochastic Do
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A UNIFIED APPROACH TO STOCHASTIC DO
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RISK AVERSION 123 a given risk the
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RISK AVERSION 125 If z is actuarial
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RISK AVERSION 127 4. CONCAVITY The
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To show that (a) implies (d), note
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RISK AVERSION 131 (b') The risk pre
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9.2. Example 2. If (30) u'(x)=(x°
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RISK AVERSION 135 12. INCREASING AN
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ADDENDUM In retrospect, 1 wish foot
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14 THE REVIEW OF ECONOMICS AND STAT
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Separation in Portfolio Analysis R.
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SEPARATION IN PORTFOLIO ANALYSIS an
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SEPARATION IN PORTFOLIO ANALYSIS II
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SEPARATION IN PORTFOLIO ANALYSIS Th
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SEPARATION IN PORTFOLIO ANALYSIS Ca
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SEPARATION IN PORTFOLIO ANALYSIS It
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SEPARATION IN PORTFOLIO ANALYSIS th
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7. Consider an investor having the
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Then eliminate p from (2) to obtain
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(a) Show that an indifference curve
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(d) Develop a similar result to (c)
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Exercise Source Notes Exercise 1 wa
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(f) Show that k > 0. [Note (Exercis
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(c) Interpret (b). (d) Illustrate s
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9. Let the partial relative risk-av
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Condition (iv) is called the "no-ea
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Then * ( 8xj \ V - s u = \-r-\ -ax,
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exhibiting decreasing absolute and
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(a) Determine necessary and suffici
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Let the random variables Og^Sl and
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Part III Static Portfolio Selection
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given parameter. Exercise ME-30 dev
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utility function. However, many con
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the characteristic exponent is 2 (i
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proportions to maximize expected ut
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investment returns are always nonne
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case to a case of one riskless and
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538 REVIEW OF ECONOMIC STUDIES Howe
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540 REVIEW OF ECONOMIC STUDIES wher
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542 REVIEW OF ECONOMIC STUDIES expa
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JAMES A. OHLSON that quadratic util
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JAMES A. OHLSON Assumptions A1-A3 a
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JAMES A. OHLSON (i) snpl/ieSi\U (3)
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JAMES A. OHLSON A number of interes
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JAMES A. OHLSON for all y,teSx'3C{Q
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Since Z"=o^( w »0 = 0 and/(«,«)
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JAMES A. OHLSON analysis here is "o
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Choosing Investment Portfolios When
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CHOOSING INVESTMENT PORTFOLIOS univ
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CHOOSING INVESTMENT PORTFOLIOS The/
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CHOOSING INVESTMENT PORTFOLIOS Tobi
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CHOOSING INVESTMENT PORTFOLIOS a-di
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CHOOSING INVESTMENT PORTFOLIOS Henc
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CHOOSING INVESTMENT PORTFOLIOS Lemm
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CHOOSING INVESTMENT PORTFOLIOS ID.
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CHOOSING INVESTMENT PORTFOLIOS Proo
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CHOOSING INVESTMENT PORTFOLIOS is c
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CHOOSING INVESTMENT PORTFOLIOS Proo
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CHOOSING INVESTMENT PORTFOLIOS 3. C
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2. EXISTENCE AND DIVERSIFICATION OF
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ON THE EXISTENCE OF OPTIMAL POLICIE
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Reprinted from Journal of Financial
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Remarks: Differentiability assumpti
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distributed from the rest. Then an
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x * "22 "12 1 " (o22- a12) + (0n -
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to decline — as if having more mo
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solved for as a function 8.(y,0"),
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FOOTNOTES 1. H. Makower and J. Mars
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264 QUARTERLY JOURNAL OF ECONOMICS
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274 QUARTERLY JOURNAL OP ECONOMICS
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280 QVABTBRLY JOURNAL OF ECONOMICS
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Reprinted from THE REVIEW OF ECONOM
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(1.1) and (1.2) give SOME EFFECTS O
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SOME EFFECTS OF TAXES ON RISK-TAKIN
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REFERENCES SOME EFFECTS OF TAXES ON
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and a gamble is offered that return
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among two risky assets and a riskle
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Consider problem (1) and suppose th
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21. Let pi,...,p„ be joint-normal
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Consider the following four utility
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4. In most stochastic optimizing mo
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(g) Discuss the use of semivariance
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(a) Suppose that the investor alloc
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The linear programming subproblems
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(f) Show that (j> is proportional t
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(e) Show that X has mean and varian
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(i) Show that for all Aru ...,km, t
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Some properties of the expected val
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(i) Suppose that V0 is positive def
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where V is the gradient operator an
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and where Z, Ylt...,Ym are independ
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INTRODUCTION This part of the book
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some qualitative characteristics of
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function of wealth. Hence the optim
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INVESTMENT ANALYSIS UNDER UNCERTAIN
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P if and only if INVESTMENT ANALYSI
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INVESTMENT ANALYSIS UNDEB UNCERTAIN
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2. RISK AVERSION OVER TIME IMPLIES
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ables at t, and the primed variable
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ehavior in choosing among timeless
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is to asume that the consumer behav
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The function t/,(C,_,, wt|a,, ft) i
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sumption, given state j3,+i at r+l,
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3. MYOPIC PORTFOLIO POLICIES Reprin
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326 THE JOURNAL OF BUSINESS this op
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328 THE JOURNAL OF BUSINESS with th
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330 THE JOURNAL OF BUSINESS In sum
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332 THE JOURNAL OF BUSINESS subject
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334 THE JOURNAL OF BUSINESS myopic
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where and k" 1 f (Mt-Ui) 2 4k {Oi 1
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MIND-EXPANDING EXERCISES 1. Conside
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(i.e., partial separability) and #,
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(c) Discuss the solution properties
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(d) By differentiating (cl) and (c2
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Let U be the utility function for t
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INTRODUCTION I. Two-Period Consumpt
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asked to develop explicit solutions
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that the property of nonincreasing
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The Hakansson paper studies a gener
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In the Samuelson-Hakansson additive
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techniques. The article by Breiman
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enously). These requirements are to
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in general, a monotonic function of
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ergodic theory, the book by Breiman
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or less than u; either do nothing o
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ME-24 the reader is invited to cons
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In discrete time, a nonnegative lin
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R. G. VICKSON This must not be inte
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This gives with R. G. VICKSON t(t)
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TWO-PERIOD CONSUMPTION MODELS AND P
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310 DRfeZE AND MODIGLIANI second or
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312 DREZE AND MODIGLIANI Had the sa
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314 DRfezE AND MODIGLIANI (— U^U^
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316 DREZE AND MODIGLIANI ure for te
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318 DREZE AND MODIGLIANI income sid
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320 DREZE AND MODIGLIANI (3.5) The
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322 DREZE AND MODIGLIANI Consumptio
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324 DREZE AND MODIGLIANI in (cj, c2
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326 DREZE AND MODIGLIANI Since d^Uj
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328 DREZE AND MODIGLIANI On the rig
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330 DRfeZE AND MODIGLIANI and its p
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332 DREZE AND M0DIGLIAN1 LEMMA C.2.
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334 DRfeZE AND MOD1GLIANI if the sm
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MANAGEMENT SCIENCE Vol. 19, No. 2,
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A DYNAMIC MODEL FOR BOND PORTFOLIO
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Maximize A DYNAMIC MODEL FOE BOND P
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MULTIPERIOD RISK PREFERENCE 41 mode
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MULTIPERIOD RISK PREFERENCE 43 The
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MULT1PERI0D RISK PREFERENCE between
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MULTIPERIOD RISK PREFERENCE 47 (ii)
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MULTIPERIOD RISK PREFERENCE 49 that
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or if s"(s' MULTIPERIOD RISK PREFER
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MULTIPERIOD RISK PREFERENCE 53 5. C
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Reprinted from The Review of Econom
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the risky asset, with 1 — w, goin
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w*t = glW,;Z,-1,...,Z0] = gT_i[W>]
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Since proof of the theorem is strai
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E C O N O M E T R I C A VOLUME 38 S
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OPTIMAL INVESTMENT 589 M: the numbe
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OPTIMAL INVESTMENT 591 For comparis
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(ii) in the case of Model III, 1 1
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Let Then OPTIMAL INVESTMENT 595 *.=
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where OPTIMAL INVESTMENT 597 (41) T
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where (54) T(x) = sup \ log c + log
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where b(v*) is the greatest lower b
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OPTIMAL INVESTMENT 603 noncapital i
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OPTIMAL INVESTMENT 605 Let us now t
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OPTIMAL INVESTMENT 607 REFERENCES [
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present value. Let ( be time, r the
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constant or decrease at a constant
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Denote the vector in the parenthese
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112 HOWARD M. TAYLOB easy to pictur
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114 HOWARD M. TAYLOR market and ign
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116 HOWARD M. TAYLOR the investor w
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118 HOWARD M. TAYLOR option which a
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120 HOWARD M. TAYLOR 3. BR.VDA, J.
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172 BASIL A. KALYMON more general p
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174 BASIL A. KALYMON The three case
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176 BASIL A. KALYMON an additional
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178 BASIL A. KALYMON (24) d\ = r -
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180 BASIL A. KALYMON The above resu
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182 BASIL A. KALYMON PROOF. First w
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MANAGEMENT SCIENCE Vol. 17, No. 7,
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M1NIMAX POLICIES FOB SELLING AN ASS
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MINIMAX POLICIES FOB SELLING AN ASS
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MINIMAX POLICIES FOR SELLING AN ASS
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MINIMAX POLICIES TOR SELLING AN ASS
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MINIMAX POLICIES FOB SELLING AN ASS
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648 L. BREIMAN We have discussed th
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650 L. BREIMAN Hence, it is suffici
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(a.s.) on the set lim XN < oo > u <
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EDWARD O. THORP method to this arti
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EDWARD O. THORP drastic simplificat
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EDWARD O. THORP us is that the prop
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EDWARD O. THORP Let the range of Rj
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EDWARD O. THORP is approximately on
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EDWARD O. THORP choosing P in repea
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EDWARD O. THORP return from the hed
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EDWARD O. THORP it is plausible to
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EDWARD O. THORP is maximization of
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EDWARD O. THORP 6. BREIMAN, L., "Op
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Reprinted from JOURNAL OF ECONOMIC
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CONSUMPTION AND PORTFOLIO RULES 375
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and CONSUMPTION AND PORTFOLIO RULES
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CONSUMPTION AND PORTFOLIO RULES giv
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Yin CONSUMPTION AND PORTFOLIO RULES
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Y(t) Y CONSUMPTION AND PORTFOLIO RU
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(d) Suppose n > r2. Formulate the o
Page 702 and 703: (j) Show that the absolute risk-ave
Page 704 and 705: The optimal first-period decision a
Page 706 and 707: (b) Show that the optimal policy in
Page 708 and 709: the game is stationary and favorabl
Page 710 and 711: (v) UHEV-E^CV-Af- 1 )^ w, V&0, w>0,
Page 713 and 714: MIND-EXPANDING EXERCISES 1. Conside
Page 715 and 716: Note the following distinction betw
Page 717 and 718: (e) Discuss the computational aspec
Page 719 and 720: (a) Show that U obeys the functiona
Page 721 and 722: (g) If ;ij(0) > 0 (1 g; g N), show
Page 723 and 724: Note that problem P2 has 3(N- 1)+1
Page 725 and 726: Note that (f) and (g) imply the exi
Page 727 and 728: P = (Pu), where pu = P[X„+l =j\ X
Page 729 and 730: (a) Show that {X„} is a stationar
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Page 733 and 734: (b) Interpret this assumption. Is i
Page 735 and 736: (d) Show that (c) may be written as
Page 737 and 738: Bibliography Journal Abbreviations
Page 739 and 740: BHARUCHA-REID, A. T. (1960). Elemen
Page 741 and 742: FIACCO, A. V., and MCCORMICK, G. P.
Page 743 and 744: IGLEHART, D. (1965). "Capital accum
Page 745 and 746: MANGASARIAN, O. L. (1966). "Suffici
Page 747 and 748: PORTEUS, E. L. (1972). "Equivalent
Page 749 and 750: THOMASION, A. J. (1969). The Struct
Page 751: A Absolute risk aversion, 84, 85, 9
Page 755 and 756: V Value of information, 462-467, 68
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