This paper reports measures of preference parameters relating to risk tolerance, time preference, and intertemporal substitution. These measures are based on survey responses to hypothetical situations constructed using an economic theorist's concept of the underlying parameters. The individual measures of preference parameters display heterogeneity. Estimated risk tolerance and the elasticity of intertemporal substitution are essentially uncorrelated across individuals. Measured risk tolerance is positively related to risky behaviors, including smoking, drinking, failing to have insurance, and holding stocks rather than Treasury bills. These relationships are both statistically and quantitatively significant, although measured risk tolerance explains only a small fraction of the variation of the studied behaviors.

/pdf/preference-parameters-and-behavioral-heterogeneity-an-45i36u0mka.pdf

Preference Parameters and Behavioral Heterogeneity: An Experimental Approach in the Health and Retirement Study

Individuals' preferences underlying most economic behavior are likely to display substantial heterogeneity. This paper reports on direct measures of preference parameters relating to risk tolerance, time preference, and intertemporal substitution. These experimental measures are based on survey respondents' choices in hypothetical situations. The questions are constructed with as little departure from the theorist's concept of the underlying parameter as possible. The individual measures of preference parameters display substantial heterogeneity. The majority of respondents fall into the least risk-tolerant group, but a substantial minority display higher risk tolerance. The individual measures of intertemporal substitution and time preference also display substantial heterogeneity. The mean risk tolerance is 0.25; the mean elasticity of intertemporal substitution is 0.2. Estimated risk tolerance and the elasticity of intertemporal substitution are essentially uncorrelated across individuals. Because the risk tolerance measure is obtained as part of the main questionnaire of a large survey, it can be related to a number of economic behaviors. Measured risk tolerance is positively related to a number of risky behaviors, including smoking, drinking, failing to have insurance, and holding stocks rather than Treasury bills. Although measured risk tolerance explains only a small fraction of the variation of the studied behaviors, these estimates provide evidence about the validity and usefulness of the measures of preference parameters.

Preference Parameters and Behavioral Heterogeneity: An Experimental Approach in the Health and Retirement Survey

http://www-personal.umich.edu/~mkimball/keio/5. papers/qje-bjks-risk-aversion.pdf

Preference Parameters and Behavioral Heterogeneity: An Experimental Approach in the

This article uses life insurance data to estimate the Pratt-Arrow coefficient of relative risk aversion for each of nearly 2,400 households. Attitudinal differences toward pure risk are then examined across demographic subgroups. Additionally, differences in speculative risk-taking are examined across demographic groups based on survey responses and compared with the results on pure risk aversion. INTRODUCTION In the mid-1960s, John Pratt and Kenneth Arrow introduced the now-familiar measure of relative risk aversion, along with the hypothesis that relative risk aversion increases with wealth. Since that time, numerous researchers have attempted to estimate the magnitude of relative risk aversion for subsets of the population using a variety of techniques, and others have conducted empirical tests of the increasing relative risk aversion (IRRA) hypothesis. Most recently, attention has turned to comparing risk aversion across different demographic subgroups, particularly men and women. [1] Remarkably, these efforts have been largely independent of one another. Some of those seeking to estimate risk aversion parameters, for example, assumed a utility function exhibiting constant relative risk aversion (CRRA), effectively precluding tests of the IRRA hypothesis. On the other hand, most studies examining the relationship between risk aversion and demographic or wealth variables infer differences in risk aversion parame ters rather than calculating the parameters explicitly. Many use either hypothetical questions or experimental gambling data, and most restrict attention to forms of risk in which both gains and losses are possible. In the present study, the authors integrate and extend these three strands of research. First, the authors derive a reduced form equation for the Pratt-Arrow measure of relative risk aversion without imposing prior assumptions on the shape of the utility function. The authors then estimate the risk aversion parameter empirically for individual households using survey data on life insurance purchases. This gives us more than 2,300 numerical measurements of the Pratt-Arrow coefficient. These measurements are then used to examine differences in relative risk aversion across demographic groups based on age, gender, education, nationality, race, marital and parental status, religion, health and behavioral indicators, and employment status, income, and wealth. The availability of wealth data also allows us to test the IRRA hypothesis. Finally, the authors examine attitudes toward a second type of risk, by studying survey responses to a hypothetical question regarding employment and income risk. The first section briefly reviews the prior research. The second and third sections present the authors' theoretical model and empirical results, respectively, pertaining to relative risk aversion in the context of mortality risk. The fourth section discusses results pertaining to speculative risk, and the article ends with a brief conclusion. PREVIOUS RESEARCH For a concave utility function U defined over wealth of W, Pratt (1964) and Arrow (1965) suggested the elasticity of marginal utility with respect to wealth, or R(W) = -WU"(W)/U'(w), as an appropriate measure of relative risk aversion. Arrow showed that this measure is directly related to one's insistence on favorable odds when putting some fraction of wealth at risk, and Pratt demonstrated that R(W) is proportional to the insurance premium one is willing to pay to avoid a given risk. Both Pratt and Arrow hypothesized that R(W) increases with W; the hypothesis implies that at higher levels of wealth, individuals become less willing to subject a given percentage of wealth to risk. Subsequent empirical research has addressed three central questions: the magnitude of R(W), the IRRA hypothesis, and the relationship between risk aversion and demographic variables. Among the earliest empirical estimates were those by Friend and Blume (1975), who studied the demand for risky assets and concluded that R(W) generally exceeds unity and is probably greater than 2. …

Demography of risk aversion

Economic Foundations of Cost-Effectiveness Analysis

Measuring Risk Aversion: An Alternative Approach

Relative risk aversion around the world

This paper presents an algorithm that permits the search for dependencies among sets of data (univariate or multivariate time-series, or cross-sectional observations). The procedure is modeled after genetic theories and Darwinian concepts, such as natural selection and survival of the fittest. It permits the discovery of equations of the data-generating process in symbolic form. The genetic algorithm that is described here uses parts of equations as building blocks to breed ever better formulas. Apart from furnishing a deeper understanding of the dynamics of a process, the method also permits global predictions and forecasts. The algorithm is successfully tested with artificial and with economic time-series and also with cross-sectional data on the performance and salaries of NBA players during the 94–95 season.

A Search for Hidden Relationships: Data Mining with Genetic Algorithms

Preface. Cannonballs and Melons. The Puzzle of the Dozen Spheres. Fire Hydrants and Soccer Players. Thue s Two Attempts and Fejes Toth s Achievement. Twelve s Company, Thirteen s a Crowd. Nets and Knots. Twisted Boxes. No Dancing at This Congress. The Race for the Upper Bound. Right Angles for Round Spaces. Wobbly Balls and Hybrid Stars. Simplex, Cplex, and Symbolic Mathematics. But Is It Really a Proof? Beehives Again. This Is Not An Epilogue. Mathematical Appendices. Bibliography. Index.

Kepler's Conjecture: How Some of the Greatest Minds in History Helped Solve One of the Oldest Math Problems in the World

Economists have begun using methods borrowed from the physical sciences to search for non-linearities in economic and financial data. The so-called phase portrait from chaos theory, in which the values of a time-series are plotted against their delayed values, is one of the techniques employed for this purpose. It has recently been shown, however, that when returns on traded assets are plotted in this manner in two- or three-dimensional space, surprising patterns arise which seriously distort the conclusions that can be drawn about the underlying data. These patterns – which resemble a compass rose – reflect the microstructure of the market or, more specifically, the finite size of the “ticks” by which prices can change in a market. The present paper clarifies the reason for this phenomenon. It is shown that within the microstructure there exists nanostructure that becomes visible only when the computations are performed without approximation. High frequency data from a foreign exchange market are used to illustrate this phenomenon.

Tick size, the compass rose and market nanostructure

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