Some Applications in Economy for Utility Functions Involving Risk Theory

Total Page:16

File Type:pdf, Size:1020Kb

Some Applications in Economy for Utility Functions Involving Risk Theory Available online at www.sciencedirect.com ScienceDirect Procedia Economics and Finance 22 ( 2015 ) 595 – 600 2nd International Conference ‘Economic Scientific Research - Theoretical, Empirical and Practical Approaches’, ESPERA 2014, 13-14 November 2014, Bucharest, Romania Some Applications in Economy for Utility Functions Involving Risk Theory Corina Cipua, Carmen Gheorgheb* aUniversity Politehnica of Bucharest, Romania, Str. Polizu, no. 1-7, Sector 1, Bucharest, 011061, Romania bNational Institute for Economic Research “Costin C. Kiriţescu”, Casa Academiei Române, Calea 13 Septembrie, no. 13, Sector 5, Bucharest, Romania Abstract We present in the first part of the article types of utility functions that can describe the behavior of the investor and their applications to optimize portfolio. The second part of the paper refers to applications in calculating insurance premiums aggregated risk in zero utility principle. © 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (©http://creativecommons.org/licenses/by-nc-nd/4.0/ 2015 The Authors. Published by Elsevier B.V.). Selection and/orand/or peer peer-review-review under under responsibility responsibility of ofthe the Scientific Scientific Committee Committee of ESPERA of ESPERA 2014 2014. Keywords: utility function, risk theory, zero utility principle 1. Maximize the value of the business for Risk Averse Investor There are three important principles of investments in order to maximize the value business. These are: the investment decision, the financing decision and the dividend decision. The investment decision can be taken by investing in assets that gain a higher return than the smallest acceptable hurdle rate. The hurdle rate should reflect risk degree of the investment and the mixture of debt and equity employed to finance it, and the return should reflect the magnitude and phasing of cash payments. * Corresponding author. E-mail address: [email protected], [email protected]; 2212-5671 © 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/or peer-review under responsibility of the Scientific Committee of ESPERA 2014 doi: 10.1016/S2212-5671(15)00268-3 596 Corina Cipu and Carmen Gheorghe / Procedia Economics and Finance 22 ( 2015 ) 595 – 600 In order to find the most suitable type of debt and the correct blend of debt and equity to finance our business operations, we have to apply the financing decision. It is very important to match the correct type of tenor for your assets to maximize the firm value. With the dividend decision can be stated the sum to return to the business investors in cash or buybacks. The utility functions allow the measurement of the preferences of an investor in the desire to increase wealth in view of its risk aversion. We present here several criteria for the selection of a utility function that evolved over time. Among them are: [1] u() is an increasing function for xf(0, ) . This is true when the first derivative (marginal utility) is strictly positive; [2] u() is concave; m. It is important to isolate the large values thatsuch that ux() ܴא݉ u() is bounded: there exists [3] occur rarely from main preferences; [4] as wealth increases, the absolute risk aversion A() decreases. A fifth criterion is occasionally advanced: [5] utility is a constant function for negative values of wealth xux0, '( ) 0 . The utility functions we use in this paper are continuous and differentiable at zero, and they have the properties of i. normalization uu'(0) 1, (0) 0 (empty financial budget has no utility); ݕ൑ݖ՜ݑሺݕሻ൑ݑሺݖሻandܴאii. monotonicity ݕǡݖ ǡͲ<m>1, u(my+(1-m)z) ൒ ݉ݑሺݕሻ ൅ሺͳെ݉ሻݑሺݖሻ (relative increase of the utility getsܴאconcavity ݕǡ ݖ smaller when y grows). Functions that do not meet the above criteria are ux() x (not concave); ux() 1 eOx ,O ! 0 (not bounded); ux() xO ,0O 1 (fails [4]). Utility functions obtained from Weibull and Pareto distribution functions that do a meet the above criteria for proper parameters ux() 1 eOx , a 1,O ! 0, ux() 1 (OO x 1),,a a ! 0. Remark: The normalization conditions could always be fulfilled for utility functions with u '0! if we §·u 0 consider a change function xuxo . ¨¸ ©¹u '0 If u is twice differentiable we can write the [1]-[3] properties uu'0,''0,(0)0td u and u'(0) 1 . 2. Principle of expected utility maximization For F - feasible investment alternatives, XI() - random variable giving the ending value of the investment for the time period considered, a rational investor acts to select an investment IFopt which maximizes his expected utility function u Eu(m X Iopt ax((() EX I) IF 3. Investment problem We take X as a random variable; x1 and x2 two realizations; x1 represents good outcome and x2 bad outcome. The set of feasible investment alternatives has only two elements: pXx P( 1 ) , 1P()pXx 2 . Question: Which alternative (do nothing or make investment) does the investor choose if he follows the principle of expected utility maximization? Corina Cipu and Carmen Gheorghe / Procedia Economics and Finance 22 ( 2015 ) 595 – 600 597 The Certainty Equivalent (Cert. Eq ) for X is cu 1 MuX(( )i.e. u()c) MuX( (), meaning: if his current wealth is c , he will be indifferent between undertaking the investment and doing nothing. For investment problem Cert.(1) Eq px12 p x . One can measure the level of the risk aversion of an investor, in two ways: ௨ᇲᇲሺ௫ሻ : ሺݔሻ ൌെܣa) measure of the absolute risk aversion by: ܴ ሺݔሻ ൌ ௔ ௨ᇲሺ௫ሻ ᇱሺݔሻ ൐Ͳ฻ݑᇱᇱᇱሺݔሻݑᇱሺݔሻ ൏ݑᇱᇱሺݔሻଶ then as wealthܣ :x For the increasing absolute risk aversion behaviour increases will be hold fewer Euro in risky assets. ᇱሺݔሻ ൌͲ฻ݑᇱᇱᇱሺݔሻݑᇱሺݔሻ ൌݑᇱᇱሺݔሻଶܣ x For the stationary case ᇱሺݔሻ ൏Ͳ then as wealth increases will be hold moreܣ :x For the decreasing absolute risk aversion behaviour Euro in risky assets. ᇲᇲ ᇲᇲᇲ ᇲ ᇲᇲ మ ௫௨ ሺ௫ሻ ᇱ ௨ ሺ௫ሻ௨ ሺ௫ሻି௨ ሺ௫ሻ . ሺݔሻ ൌെ మ ܣ ሺݔሻ ൌെ , withܣb) measure of the relative risk aversion: ܴ௥ሺݔሻ ൌ ݔ ௨ᇲሺ௫ሻ ൫௨ᇲሺ௫ሻ൯ 4. Application in insurances. Utility premiums for linear truncated and quadratic utility Compensations are paid by IC (insurance company) based on insurance policies owned by the insured following the conclusion of a contract. Premiums paid by insurers must cover any damages and other costs: fees, taxes, maintenance costs. The amount of damage or loss associated with a contract for a period of time is a random variable X and represents the risk assumed by IC. The zero utility principle for different scale invariant utility functions ug(())O x ux() ,O ! 0,x , (4.1) O g()O with ݑǣ ܴ ՜ ܴ a classical utility function, will be used in order to obtain the zero utility premium HO,1 that is the implicit solution of the following equation: EHY((PO ))0, (4.2) Y being the risk assured, with unit expectation. For the scaled risk XY P with P expectation the zero utility premium HOP, is also uniquely determined: HHOP,/, P OP1 (4.3) We shall compare the variation of the zero utility premium HOP, for linear truncated and quadratic utility versus O parameter for an Exponential and Pareto distribution of the risk with P expectation. The linear truncated utility uxO () min{,} xO leads to the approximate solution HOP, | P . For X exponential with unit expectation equation (4.1) is written: ()H O Eu[(O H X )] OO e , H t (4.4) and one obtain ­OOOdln , 1 HO,1 ® (4.5) ¯ 1,O t 1. which is generalized for the risk X with P expectation ­ PPO,0dd ° HOP, ® §·P (4.6) °OPtln¨¸ , PO . ¯ ©¹O 598 Corina Cipu and Carmen Gheorghe / Procedia Economics and Finance 22 ( 2015 ) 595 – 600 Also, the mean value of uHYO () for the Pareto risk Y with unit expectation and D parameter is D ­ §·H O °OO¨¸1,H t Eu[(O H Y )] ® ©¹D (4.7) ° ¯ HHd1, O leading to ­ D ­ PPO,0dd OO1,1 d ° D ° HO,1 ® O and HOP, ® P (4.8) ° °OPDPa D ,. PO t ¯ 1,O t 1. ¯ O for the Pareto risk X with P expectation and D parameter. In the same manner, for quadratic utility ­ x2 °xxd, O ° 2O uxO () ® (4.9) O ° ,,x t O ¯° 2 the approximate solution is P 2 H |P (4.10) OP, 2O for any type of risk. For the exponential risk Y with unit expectation the mean of uHYO () is ­ O 2() 2eH O ° , H t O ° 2O Eu[(O H Y )] ® (4.11) (1OO22)((1))H ° , H d O ¯° 2O with unique solution ­OOO1122 t ° H 2 (4.12) O,1 ® O ° OOdln , 2. ¯ 2 and ­OP O22 P,0 dd PO /2 ° 2 (4.13) HOP, ® 2P ° OPtln , PO / 2. ¯ O 2 for the exponential risk X with P expectation. Corina Cipu and Carmen Gheorghe / Procedia Economics and Finance 22 ( 2015 ) 595 – 600 599 Fig 1. Zero utility premiums Fig 2. Quadratic utility and a Pareto risk for exponential and Pareto risks In Fig. 1 zero utility premiums for exponential and Pareto risks with P expectation were represented. One observe that between Pareto cases the Pareto for bigger D parameter is more convenient to the investor and more appropriate with the approximate solution. For quadratic utility and a Pareto risk Y with unit expectation and parameter D , the solution of equation (4.1) becomes ­ DD2 °OD , Od D 1 D 1 ° D 1 O 2 HO,1 ® 2D (4.15) ° OD2 2 ° OOtln , . ¯° 21D and for Pareto scaled risk XY P with P expectation and parameter D , the zero utility premium is ­ 2 21DP§· D °ODPDPD 1 ¨¸, Pt O ° DO12©¹ D HOP, ® (4.16) ° 22DD11 ° OP O P,0dd PO , ¯ DD12 P and was represented in Figure 2 for [0,2] and different values of parameter D {1.8;2;2.5;3}.
Recommended publications
  • Portfolio Selection Under Exponential and Quadratic Utility
    Portfolio Selection Under Exponential and Quadratic Utility Steven T. Buccola The production or marketing portfolio that is optimal under the assumption of quadratic utility may or may not be optimal under the assumption of exponential utility. In certain cases, the necessary and sufficient condition for an identical solution is that absolute risk aversion coefficients associated with the two utility functions be the same. In other cases, equality of risk aversion coefficients is a sufficient condition only. A comparison is made between use of exponential and quadratic utility in the analysis of a California farmer's marketing problem. A large number of utility functional forms difference. Under other conditions, the have been proposed for use in selecting opti- choice has a significant impact on optimal mal portfolios of risky prospects [Tsaing]. For behavior. purposes of both theoretical and applied eco- Suppose an analyst is studying a continu- nomic research, attention has primarily been ously divisible set of alternative production directed to the quadratic and exponential or marketing options offering approximately forms. After enjoying widespread use in the normally-distributed returns, and that he 1950s and 1960s, the quadratic form came wishes to select the portfolio of these options under criticism by Pratt and others and lost with highest expected utility. The analyst is much of its reputability. In contrast the expo- considering using an exponential utility func- nential form has become increasingly popu- tion U = -exp(-Xx), X > 0, or a quadratic lar, due in large measure to its mathematical utility function U = x -vx,v, > 0, x < /2v, tractability [Attanasi and Karlinger; O'Con- where U is utility and x is wealth.
    [Show full text]
  • Utility with Decreasing Risk Aversion
    144 UTILITY WITH DECREASING RISK AVERSION GARY G. VENTER Abstract Utility theory is discussed as a basis for premium calculation. Desirable features of utility functions are enumerated, including decreasing absolute risk aversion. Examples are given of functions meeting this requirement. Calculating premiums for simplified risk situations is advanced as a step towards selecting a specific utility function. An example of a more typical portfolio pricing problem is included. “The large rattling dice exhilarate me as torrents borne on a precipice flowing in a desert. To the winning player they are tipped with honey, slaying hirri in return by taking away the gambler’s all. Giving serious attention to my advice, play not with dice: pursue agriculture: delight in wealth so acquired.” KAVASHA Rig Veda X.3:5 Avoidance of risk situations has been regarded as prudent throughout history, but individuals with a preference for risk are also known. For many decision makers, the value of different potential levels of wealth is apparently not strictly proportional to the wealth level itself. A mathematical device to treat this is the utility function, which assigns a value to each wealth level. Thus, a 50-50 chance at double or nothing on your wealth level may or may not be felt equivalent to maintaining your present level; however, a 50-50 chance at nothing or the value of wealth that would double your utility (if such a value existed) would be equivalent to maintaining the present level, assuming that the utility of zero wealth is zero. This is more or less by definition, as the utility function is set up to make such comparisons possible.
    [Show full text]
  • The Case Against Power Utility and a Suggested Alternative: Resurrecting Exponential Utility
    Munich Personal RePEc Archive The Case Against Power Utility and a Suggested Alternative: Resurrecting Exponential Utility Alpanda, Sami and Woglom, Geoffrey Amherst College, Amherst College July 2007 Online at https://mpra.ub.uni-muenchen.de/5897/ MPRA Paper No. 5897, posted 23 Nov 2007 06:09 UTC The Case against Power Utility and a Suggested Alternative: Resurrecting Exponential Utility Sami Alpanda Geoffrey Woglom Amherst College Amherst College Abstract Utility modeled as a power function is commonly used in the literature despite the fact that it is unbounded and generates asset pricing puzzles. The unboundedness property leads to St. Petersburg paradox issues and indifference to compound gambles, but these problems have largely been ignored. The asset pricing puzzles have been solved by introducing habit formation to the usual power utility. Given these issues, we believe it is time re-examine exponential utility. Exponential utility was abandoned largely because it implies increasing relative risk aversion in a cross-section of individuals and non- stationarity of the aggregate consumption to wealth ratio, contradicting macroeconomic data. We propose an alternative preference specification with exponential utility and relative habit formation. We show that this utility function is bounded, consistent with asset pricing facts, generates near-constant relative risk aversion in a cross-section of individuals and a stationary ratio of aggregate consumption to wealth. Keywords: unbounded utility, asset pricing puzzles, habit formation JEL Classification: D810, G110 1 1. Introduction Exponential utility functions have been mostly abandoned from economic theory despite their analytical convenience. On the other hand, power utility, which is also highly tractable, has become the workhorse of modern macroeconomics and asset pricing.
    [Show full text]
  • Pareto Utility
    Theory Dec. (2013) 75:43–57 DOI 10.1007/s11238-012-9293-8 Pareto utility Masako Ikefuji · Roger J. A. Laeven · Jan R. Magnus · Chris Muris Received: 2 September 2011 / Accepted: 3 January 2012 / Published online: 26 January 2012 © The Author(s) 2012. This article is published with open access at Springerlink.com Abstract In searching for an appropriate utility function in the expected utility framework, we formulate four properties that we want the utility function to satisfy. We conduct a search for such a function, and we identify Pareto utility as a func- tion satisfying all four desired properties. Pareto utility is a flexible yet simple and parsimonious two-parameter family. It exhibits decreasing absolute risk aversion and increasing but bounded relative risk aversion. It is applicable irrespective of the prob- ability distribution relevant to the prospect to be evaluated. Pareto utility is therefore particularly suited for catastrophic risk analysis. A new and related class of generalized exponential (gexpo) utility functions is also studied. This class is particularly relevant in situations where absolute risk tolerance is thought to be concave rather than linear. Keywords Parametric utility · Hyperbolic absolute risk aversion (HARA) · Exponential utility · Power utility M. Ikefuji Institute of Social and Economic Research, Osaka University, Osaka, Japan M. Ikefuji Department of Environmental and Business Economics, University of Southern Denmark, Esbjerg, Denmark e-mail: [email protected] R. J. A. Laeven (B) · J. R. Magnus Department of Econometrics & Operations Research, Tilburg University, Tilburg, The Netherlands e-mail: [email protected] J. R. Magnus e-mail: [email protected] C.
    [Show full text]
  • Estimating Exponential Utility Functions
    ,~ j,;' I) ':;l) . G rio"". 0 • 0 (? _.0 o I') Q () l% a 9(;' o Q estimatiqg "ExPC?ttenti~1 ·:U····:t·····~·I·····I:·I·+u·· ;F;";;u::·:n6',.t5"'·;~:n·'.' ·s.. ·, .' ..., .'.: :','. ..•. ".. '..'. ;. ~':,,;'V~ ..' .'. o c:::::::> ' ,;", o o (b~s. ) EconOm:ics, .Statistics'·Oftd (OOfI,ati,tS /} o i_JS~~n;'~~t'~C:'T" " o .j o i) " (> C. "French " State Un I vers" I ty, and -­ ~~ ~ from Agric~ltur;al Economic Research, Janucrry 1978" \/01. 30, No. 1 ~~'~....' .. ' .. exponential.utility fun~tion for money has 10ng att:racted attentic;m fromAtheorists~~ because it exhibits\~oni"creasing absolute, risk aversion. j\lso, under certain con- ~ ditions, it generates an expected utility functi()n that is maximizable in a quadratic 1 program. However, "this "'functiona,l form presents 'estimation problems. Logarithmic • transformation of an exponential uti 1i,t,y, function does not conform to the Von Neumann­ Horgenste~ axioms. Hence', it cannot be used as ,.a basis for best fit in statistical analysis. A criterion is described that can be used to select a best-flt exponential uti 1ity.fun.ction, and its appl.ica,ti~!!c"",L~I.J~~~er utJ1ity an~,,~J~!s. is (~en~~~.~~r.~~ed ,~-».,.....'~\ ~ ....,.,." .,-~~ '1\, ~.... jt-~ .. -~..,q,·""·"~.""""11!"~""""'"""'\.'<'..".tft4._ ... 1' ,,''-,. < ~ i\ ,c,( ~--~""..-~,."".... .... ,~. '" ~. "~:"''''.jq''K>''''~~''l;:' ", " & ' (J 11' 1 i ,~ r.~ .... ' t ' .'~ >':J ~rr 1 C >J '¥ ~r ~1 f' if! 1 t::' ~ t,n t; ~,l IG " Canputerized simulation o Computers c Economic analysis Est imates 1\, Exponential functions " ~ogarlthm functions Risk ~Statistical analysis Utility routines 1711. IdenEifier./Open-Eaded Titnu (;:::; Absolute risk aversion Compute r app,l i cat i ens' Logari thmi c traosformatian Honey (/ Von Neumann-Morgenstern axioms )) ~ 12-A, 12-8 PB .
    [Show full text]
  • Cardinal Sins: Utility Specification and the Measurement of Risk Aversion
    19 Eisenhauer, International Journal of Applied Economics, 13(2), September 2016, 19-29 Cardinal Sins: Utility Specification and the Measurement of Risk Aversion Joseph G. Eisenhauer* University of Detroit Mercy ___________________________________________________________________________ Abstract: In contrast to the ideal of allowing empirical data to reveal individual preferences, economists have frequently resorted to constructing preference functions with convenient parameters and then using data merely to estimate the magnitude of the parameters. In particular, it has become common practice for researchers seeking to measure risk aversion to select an arbitrary utility specification and assume that the same functional form applies to all individuals in a given sample. This note illustrates the prevalence of this practice and how unreliable such an approach may be. Using a recently developed data set from Thailand, empirical results for the Pratt-Arrow coefficients of risk aversion are compared across isoelastic and exponential utility functions. The latter are estimated to be, on average, more than five times larger than the former, suggesting the possibility of enormous distortions in empirical studies that assume specific cardinal utility functions. Keywords: risk aversion, utility function, isoelastic, exponential, representative agent JEL Classification: D810 ___________________________________________________________________________ 1. Introduction Although utility is canonically described as an ordinal ranking of preferences over goods and services, economists have frequently adopted specific functional forms that yield cardinal measures (Barnett, 2003). As early as the 18th century, Bernoulli (1738) used a logarithmic utility function to illustrate the concept of the diminishing marginal utility of wealth in his explanation of risk aversion. This tendency has become increasingly prevalent since Pratt (1964) and Arrow (1965) introduced quantitative measures of risk aversion.
    [Show full text]
  • Chapter 2 — Risk and Risk Aversion
    Chapter 2 — Risk and Risk Aversion The previous chapter discussed risk and introduced the notion of risk aversion. This chapter examines those concepts in more detail. In particular, it answers questions like, when can we say that one prospect is riskier than another or one agent is more averse to risk than another? How is risk related to statistical measures like variance? Risk aversion is important in finance because it determines how much of a given risk a person is willing to take on. Consider an investor who might wish to commit some funds to a risk project. Suppose that each dollar invested will return x dollars. Ignoring time and any other prospects for investing, the optimal decision for an agent with wealth w is to choose the amount k to invest to maximize [uw (+− kx( 1) )]. The marginal benefit of increasing investment gives the first order condition ∂u 0 = = u′( w +− kx( 1)) ( x − 1) (1) ∂k At k = 0, the marginal benefit isuw′( ) [ x − 1] which is positive for all increasing utility functions provided the risk has a better than fair return, paying back on average more than one dollar for each dollar committed. Therefore, all risk averse agents who prefer more to less should be willing to take on some amount of the risk. How big a position they might take depends on their risk aversion. Someone who is risk neutral, with constant marginal utility, would willingly take an unlimited position, because the right-hand side of (1) remains positive at any chosen level for k. For a risk-averse agent, marginal utility declines as prospects improves so there will be a finite optimum.
    [Show full text]
  • Single-Period Portfolio Choice and Asset Pricing
    Part I Single-Period Portfolio Choice and Asset Pricing 1 Chapter 1 Expected Utility and Risk Aversion Asset prices are determined by investors’ risk preferences and by the distrib- utions of assets’ risky future payments. Economists refer to these two bases of prices as investor "tastes" and the economy’s "technologies" for generating asset returns. A satisfactory theory of asset valuation must consider how in- dividuals allocate their wealth among assets having different future payments. This chapter explores the development of expected utility theory, the standard approach for modeling investor choices over risky assets. We first analyze the conditions that an individual’spreferences must satisfy to be consistent with an expected utility function. We then consider the link between utility and risk aversion and how risk aversion leads to risk premia for particular assets. Our final topic examines how risk aversion affects an individual’s choice between a risky and a risk-free asset. Modeling investor choices with expected utility functions is widely used. However, significant empirical and experimental evidence has indicated that 3 4 CHAPTER 1. EXPECTED UTILITY AND RISK AVERSION individuals sometimes behave in ways inconsistent with standard forms of ex- pected utility. These findings have motivated a search for improved models of investor preferences. Theoretical innovations both within and outside the expected utility paradigm are being developed, and examples of such advances are presented in later chapters of this book. 1.1 Preferences when Returns Are Uncertain Economists typically analyze the price of a good or service by modeling the nature of its supply and demand. A similar approach can be taken to price an asset.
    [Show full text]
  • An Approximate Solution for the Power Utility Optimization Under Predictable Returns Arxiv:1911.06552V3 [Q-Fin.PM] 21 Jan 2021
    An approximate solution for the power utility optimization under predictable returns Dmytro Ivasiuk Department of Statistics, European University Viadrina, Frankfurt(Oder), Germany E-mail: [email protected] Abstract This work presents an approximate single period solution of the portfolio choice problem for the investor with a power utility function and the predictable returns. Assuming that gross portfolio returns approximately follow a log-normal distribu- tion, it was possible to derive the optimal weights. As it is known, the log-normal distribution seems to be a good proxy of the normal distribution in case if the stan- dard deviation of the last one is way much smaller than its mean. Thus, succeeding to model the behaviour of asset returns as a multivariate normal (conditional nor- mal) distribution (what is a popular way to proceed in academic literature) would be the case to apply the ideas discussed in this work. Besides, the paper provides a simulation study comparing the derived result to the well-know numerical solution. Keywords: CRRA, power utility, utility optimization, numerical solution, log-normal distribution. 1 Introduction The current paper discusses portfolio optimization as the utility of wealth maximization. The classical problem stands for deriving the value function given by the whole investment period with the use of the Bellman backward method (see Pennacchi, 2008; Brandt, 2009). One can find it as the typical solution for the investment strategies for different types of utility functions (see Bodnar et al., 2015a,b). However, most of the time, it is hard arXiv:1911.06552v3 [q-fin.PM] 21 Jan 2021 to succeed and to derive an analytical solution.
    [Show full text]
  • Static and Dynamic Portfolio Allocation with Nonstandard Utility Functions
    Static and dynamic portfolio allocation with nonstandard utility functions Antonio´ A. F. Santos Faculty of Economics, Monetary and Financial Research Group (GEMF), University of Coimbra, Portugal Abstract This article builds on the mean-variance criterion and the links with the expected utility maximization to define the optimal allocation of portfolios, and extends the results in two ways, first considers tailored made utility functions, which can be non continuous and able to capture possible prefer- ences associated with some portfolio managers. Second, it presents results that relate to static (myopic) portfolio allocation decisions connected to dy- namic settings where multi-period allocations are considered and conditions are defined to rebalance the portfolio as new information arrive. The con- ditions are established for the compatibility of static and dynamic decisions associated with different utility functions. Keywords: Dynamic optimization, Expected utility maximization, Mean- variance criterion, Portfolio allocation, Step utility functions 1 1 Introduction In portfolio allocation problems, the main feature is the allocation of resources to different alternatives available, in this case, different financial assets. The main objective is to choose a specific combination that is optimal by a given criterion. This problem has been studied since the research in Markowitz (1952). This au- thor has founded what is now known as the normative portfolio theory. Due to difficulties in the implementation of the theory as a way of justifying real deci- sions and to a changing emphasis in the study of financial markets, there has been a continuous research on these matters. The normative approach supposes that decision agents can specify fully their utility function and know with certainty the distribution of returns.
    [Show full text]
  • Dynamic Asset Allocation
    Dynamic Asset Allocation Claus Munk Until August 2012: Aarhus University, e-mail: [email protected] From August 2012: Copenhagen Business School, e-mail: [email protected] this version: July 3, 2012 The document contains graphs in color, use color printer for best results. Contents Preface v 1 Introduction to asset allocation 1 1.1 Introduction . .1 1.2 Investor classes and motives for investments . .1 1.3 Typical investment advice . .3 1.4 How do individuals allocate their wealth? . .3 1.5 An overview of the theory of optimal investments . .3 1.6 The future of investment management and services . .3 1.7 Outline of the rest . .3 1.8 Notation . .3 2 Preferences 5 2.1 Introduction . .5 2.2 Consumption plans and preference relations . .6 2.3 Utility indices . .9 2.4 Expected utility representation of preferences . 10 2.5 Risk aversion . 16 2.6 Utility functions in models and in reality . 20 2.7 Preferences for multi-date consumption plans . 26 2.8 Exercises . 33 3 One-period models 37 3.1 Introduction . 37 3.2 The general one-period model . 37 3.3 Mean-variance analysis . 43 3.4 A numerical example . 49 3.5 Mean-variance analysis with constraints . 49 3.6 Estimation . 49 i ii Contents 3.7 Critique of the one-period framework . 49 3.8 Exercises . 50 4 Discrete-time multi-period models 51 4.1 Introduction . 51 4.2 A multi-period, discrete-time framework for asset allocation . 51 4.3 Dynamic programming in discrete-time models . 54 5 Introduction to continuous-time modelling 59 5.1 Introduction .
    [Show full text]
  • Univariate and Multivariate Measures of Risk Aversion and Risk Premiums with Joint Normal Distribution and Applications in Portf
    UNIVARIATE AND MULTIVARIATE MEASURES OF RISK AVERSION AND RISK PREMIUMS WITH JOINT NORMAL DISTRIBUTION AND APPLICATIONS IN PORTFOLIO SELECTION MODELS by YUMING LI B.Sc, Shanghai Jiao Tong University, 1983 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN BUSINESS ADMINISTRATION in THE FACULTY OF GRADUATE STUDIES (The Faculty of Commerce and Business Administration) We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA September 1987 ©Yuming Li, 1987 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department of COMMERCE The University of British Columbia 1956 Main Mall Vancouver, Canada V6T 1Y3 Date October 1, 1987 DE-6(3/81) Abstract This thesis gives the formal derivations of the so-called Rubinstein's measures of risk aversion and their multivariate generalizations. The applications of these measures in portfolio selection models are also presented. Assuming that a decision maker's preferences can be represented by a unidimen• sional von Neumann and Morgenstern utility function, we consider a model with an uninsurable initial random wealth and an insurable risk. Under the assumption that the two random variables have a bivariate normal distribution, the second-order co- variance operator is developed from Stein/Rubinstein first-order covariance operator and is used to derive Rubinstein's measures of risk aversion from the approxima• tions of risk premiums.
    [Show full text]