The Pearson system of utility functions

LiCalzi, Marco; Sorato, Annamaria

doi:10.1016/j.ejor.2004.10.012

Cited by 25 publications

(9 citation statements)

References 30 publications

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“…General research has been carried out in trying to find a suitable class of utility functions to ease the decision‐making process in different strands of decision theory, for instance, by the authors of Ref. , who advocated the use of HARA (hyperbolic absolute risk aversion) utility functions, which are those functions

u (r)

such that

- \frac{u^{''} (r)}{u^{'} (r)} = \frac{1}{a + b r}

for

a, b \in R

, i.e., the coefficient of absolute risk aversion is the reciprocal of a linear function of r . This approach is noteworthy in its applicability both to the normative decision‐making approach of expected utility theory, and also to the descriptive decision‐making methodology of prospect theory …”

Section: Conjugate Utilitymentioning

confidence: 99%

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A Conjugate Class of Utility Functions for Sequential Decision Problems

2015

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The use of the conjugacy property for members of the exponential family of distributions is commonplace within Bayesian statistical analysis, allowing for tractable and simple solutions to problems of inference. However, despite a shared motivation, there has been little previous development of a similar property for using utility functions within a Bayesian decision analysis. As such, this article explores a class of utility functions that appear to be reasonable for modeling the preferences of a decisionmaker in many real‐life situations, but that also permit a tractable and simple analysis within sequential decision problems.

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u (r)

such that

- \frac{u^{''} (r)}{u^{'} (r)} = \frac{1}{a + b r}

for

a, b \in R

Section: Conjugate Utilitymentioning

confidence: 99%

“…etc. Again, we use Equations (13) and (14) to update beliefs about θ . For instance, there are three conditional values at the first epoch, e.g., θ |d 1 = d A , r 1 ∼ N ( 3r 1 +1 7 , 3 7 ), and seven at the second epoch.…”

Section: Examplementioning

confidence: 99%

A Conjugate Class of Utility Functions for Sequential Decision Problems

2015

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“…Based on previous studies two broad classes of shapes}fully concave or fully convex, and Sshaped (convex/concave) (e.g. Bell and Fishburn, 2000;LiCalzi and Sorato, 2006)}were used. Fully concave or convex utility functions have been widely applied in the economics literature.…”

Section: Assessing the Shape Of Decision-makers' Global Utility Functmentioning

confidence: 99%

The informational content of the shape of utility functions: financial strategic behavior

Pennings

Garcia

2008

Manage Decis Econ

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Recently, Pennings and Smidts (2003) showed a relationship between organizational behavior and the global shape of the utility function. Their results suggest that the shape of the utility function may be related to 'higher-order' decisions. This research examines the relationship between financial strategic decisions and the global shape of the utility function of real decision makers. We assess the shape of utility functions of portfolio managers and show that the global shape is related to their strategic asset allocation. The findings demonstrate the informational content of the shape of utility functions in the context of financial strategic behavior. Copyright © 2008 John Wiley & Sons, Ltd.

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“…We show these different utility functions in Table II. To generalize the creation of utility functions, we use Hyperbolic Absolute Risk Aversion (HARA) class of utility functions which are specific forms of the Arrow-Pratt risk aversion model and defined in [1,22] as (12) where 0. The HARA class of utility functions gives us most of the utility functions by varying the constants, and as shown in Table II.…”

Section: Mean Variance Framework Step 2: Computing the Optimal Allmentioning

confidence: 99%

FPGA acceleration of mean variance framework for optimal asset allocation

Irturk

Benson

Laptev

et al. 2008

2008 Workshop on High Performance Computational Finance

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Asset classes respond differently to shifts in financial markets, thus an investor can minimize the risk of loss and maximize return of his portfolio by diversification of assets. Increasing the number of diversified assets in a financial portfolio significantly improves the optimal allocation of different assets giving better investment opportunities. However, a large number of assets require a significant amount of computation that only high performance computing can currently provide. Because of the highly parallel nature of Markowitz' mean variance framework (the most popular approximation approach for optimal asset allocation) an FPGA implementation of the framework can also provide the performance necessary to compute the optimal asset allocation with a large number of assets. In this work, we propose an FPGA implementation of Markowitz' mean variance framework and show it has a potential performance ratio of over a software implementation.

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The Pearson system of utility functions

Cited by 25 publications

References 30 publications

A Conjugate Class of Utility Functions for Sequential Decision Problems

A Conjugate Class of Utility Functions for Sequential Decision Problems

The informational content of the shape of utility functions: financial strategic behavior

FPGA acceleration of mean variance framework for optimal asset allocation

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