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This paper aims to quantify preferences without having to have any utility data.

We use duality theory, Taylor’s theorem and nonlinear regressions.

We presented pioneering quantitative methods in economics and business. These methods can be applied to numerous topics in empirical and theoretical economics and business. Moreover, this paper highlighted the interdisciplinary nature of economics. In doing so, it emphasized the interface between economics, marketing, management, statistics and mathematics. Furthermore, it circumvented a major obstacle in the literature: the curse of dimensionality.

The authors introduce a novel and convenient approach to utility modeling. In doing so, they present a general utility function in a simple form. Furthermore, they develop a method to measure preferences without any utility data. They also devise a method to measure the marginal utility. Then, they develop new methods of modeling and measuring the consumer utility. In so doing, they overcome a major obstacle: the curse of the dimensionality. In addition, they introduce new methods of modeling and measuring the consumer demand for the firm’s good.

The purpose of this paper is to empirically test dynamic hedging, using data from the FTSE-100 and Standard & Poor’s (S&P) 500 futures indices.

The authors introduce a dynamic continuous-time hedging model in futures markets. The authors further relax the statistical-independence assumption between the spot price and basis risk.

The authors show that the investors are, on average, quite risk averse. The authors find that a one unit increase in the price volatility reduces the hedged FTSE-100 (S&P 500) by 645.62 (777.07) units. Similarly, a one unit increase in basis risk reduces the hedged FTSE-100 (S&P 500) by 403.57 (378.54) units. The authors’ approach shows that risk-averse investors should decrease their hedge (i.e. increase their equity allocation) with an increase in index price risk.

These findings are helpful to risk managers dealing with futures markets.

The contribution of this paper is that it successfully introduces a dynamic continuous-time hedging model in futures markets.

The purpose of this paper is to generalize input‐hedging models.

Using a general utility function and general probability distributions, the paper extends the Paroush‐Wolf and Alghalith theoretical models by including two risky (hedged) inputs.

The paper finds that the inclusion of two risky inputs affects the previous theoretical results.

The paper introduces new theoretical and technical contributions. Also, it is intended to be a foundation for future empirical and theoretical studies.

The purpose of this paper is to present a realistic hedging model.

The paper uses a general utility function, general distributions, and a multiple‐input technology.

The study finds that the impact of one or both risks on the optimal output, hedge, or hedge ratio is determined by the market structure of one or both forward pieces.

This is the first paper that uses a general, complete, and realistic hedging model.

The purpose of this paper is to empirically test dominant theories and assumptions in behavioral finance, using data from the Standard & Poor's 500 index.

The empirical analysis has three parts: to test the assumption of risk aversion; to examine the dominant theory that the optimal portfolio depends on risk preferences; and to test prospect theory that decision makers prefer certain outcomes over probable outcomes. Finally, an alternative model to test prospect theory is introduced.

The proposed model is more flexible than prospect theory since it does not a priori assume what value of the portfolio induces risk aversion/seeking, while it does not a priori preclude linear preferences. Empirical results show that: investors are risk seeking; a change in the sign of preferences does not necessarily imply a change in the sign of wealth/return and vice versa; and the optimal portfolio does not depend on preferences.

These findings are helpful to risk managers dealing with models of behavioural finance.

The contribution of this paper is that it successfully tests fundamental theories and assumptions in behavioral finance by providing a better alternative to prospect theory in several ways.