Quadratic Differential Demand Systems and the Retail Demand for Pork in Great Britain

The primary objective of this paper is to derive a general synthetic quadratic (rank 3) differential demand system which nests within it a range of testable differential demand models including the quadratic AIDS, CBS, Rotterdam and NBR systems. A model selection test procedure is also outlined. These differential systems are then applied and tested to analyse the monthly retail demand for cuts of pork in Great Britain over the period 1989–2000. The empirical results suggest that a quadratic differential AIDS model is most appropriate for the pork demand system studied, but that the need for inclusion of quadratic income/expenditure terms is not universal for every cut within the demand system. Quadratic expenditure effects were appropriate for pork chops and leg roasts, but log linear expenditure effects were adequate for bellies, shoulders and loin roasts. Roasting cuts were expenditure and own price elastic, with pork loins, chops and bellies all expenditure and own price inelastic.     

How Banks Apply Marketing Expertise to Develop New Derivatives

For banks operating in the fiercely competitive derivatives market, the difference between the leaders and the also-rans often boils down to their respective approaches to product innovation. To achieve market leadership in this highly volatile field, a bank must develop and continually refine the processes and the expertise necessary for identifying new areas of business and attracting and retaining customers. Although the development of derivatives is a complex, expensive process, such development efforts provide the means for satisfying existing clients as well as attracting new customers. F. Axel Johne and M. Panos Pavlidis examine the managerial practices of banks that are acknowledged leaders in bringing new derivatives to market ahead of their competitors. In particular, they examine how those first-mover banks apply their marketing expertise and they review the advantages those banks enjoy as a result of their success in product innovation. The banks studied fall into one of two groups: highly active innovators and less active innovators. The study reveals several significant differences between the two groups with respect to managing marketing inputs for product innovation purposes. First, compared to less active banks, the highly active innovators take a more sophisticated, market-based approach to identifying innovation opportunities. Rather than looking for innovations that offer a close fit with existing products and competencies, they analyze the benefits sought by target clients and initiate innovation efforts based on those analyses. Highly active innovators recognize the important role that internal marketing plays in encouraging functional specialists to work together for the purpose of identifying follow-on development opportunities. Internal marketing also helps to ensure that all parties understand and can support the planned innovation. The highly active innovators in this study do not take a formulaic approach to the development of new derivatives; instead, they rely on marketing expertise to identify and capitalize on business opportunities. Rather than concentrate solely on improving the core technical features of a product, the highly active innovators also recognize the importance of product augmentation innovation (to ensure the appropriate support for various market segments), process innovation (so they can reduce prices, when necessary), and market innovation (to ensure that they pursue the optimal mix of markets).     

Accounting earnings and gross domestic product

We document that aggregate accounting earnings growth is an incrementally significant leading indicator of growth in nominal Gross Domestic Product (GDP). Professional macro forecasters, however, do not fully incorporate the predictive content embedded in publicly available accounting earnings data. As a result, future nominal GDP growth forecast errors are predictable based on accounting earnings data that are available to professional macro forecasters in real time.     

Comparative issues between linear and non-linear risk measures for non-convex portfolio optimization: evidence from the S&P 500

Abstract

This article focuses on inferring critical comparative conclusions as far as the application of both linear and non-linear risk measures in non-convex portfolio optimization problems. We seek to co-assess a set of sophisticated real-world non-convex investment policy limitations, such as cardinality constraints, buy-in thresholds, transaction costs, particular normative rules, etc. within the frame of four popular portfolio selection cases: (a) the mean-variance model, (b) the mean-semi variance model, (c) the mean-MAD (mean-absolute deviation) model and (d) the mean-semi MAD model. In such circumstances, the portfolio selection process reflects to a mixed-integer bi-objective (or in general multiobjective) mathematical programme. We precisely develop all corresponding modelling procedures and then solve the underlying problem by use of a novel generalized algorithm, which was exclusively introduced to cope with the above-mentioned singularities. The validity of the attempt is verified through empirical testing on the S&P 500 universe of securities. The technical conclusions obtained not only confirm certain findings of the particular limited existing theory but also shed light on computational issues and running times. Moreover, the results derived are characterized as encouraging enough, since a sufficient number of efficient or Pareto optimal portfolios produced by the models appear to possess superior out-of-sample returns with respect to the benchmark.     

Hedonic Price Analysis and Selectivity Bias

Hedonic valuation of quality attributes can be misleading when theassumption that these attributes are exogenous to sample selection isviolated. This paper considers the simultaneity between hedonic valuationand sample selection in the context of a model of producer behavior andinvestigates empirically the case where land is demanded for use as an inputeither in agricultural production or in touristic development. The empiricalanalysis suggests that failing to correct for sample selection results in abiased valuation of the effect of water salinity on agricultural land.     

Multiobjective portfolio optimization with non-convex policy constraints: Evidence from the Eurostoxx 50

Our purpose in this paper is to depart from the intrinsic pathology of the typical mean–variance formalism, due to both the restriction of its assumptions and difficulty of implementation. We manage to co-assess a set of sophisticated real-world non-convex investment policy limitations, such as cardinality constraints, buy-in thresholds, transaction costs, particular normative rules, etc., within the frame of complex scenarios, which demand for simultaneous optimization of multiple investment objectives. In such a case, the portfolio selection process reflects a mixed-integer multiobjective portfolio optimization problem. On this basis, we meticulously develop all the corresponding modeling procedures and then solve the underlying problem by use of a new, fast and very effective algorithm. The value of the suggested framework is integrated with the introduction of two novel concepts in the field of multiobjective portfolio optimization, i.e. the security impact plane and the barycentric portfolio. The first represents a measure of each security's impact in the efficient surface of Pareto optimal portfolios. The second serves as the vehicle for implementing a balanced strategy of iterative portfolio tuning. Moreover, a couple of some very informative graphs provide thorough visualization of all empirical testing results. The validity of the attempt is verified through an illustrative application on the Eurostoxx 50. The results obtained are characterized as very encouraging, since a sufficient number of efficient or Pareto optimal portfolios produced by the model, appear to possess superior out-of-sample returns with respect to the underlying benchmark.     

A Markov regime switching approach for hedging energy commodities

This paper estimates constant and dynamic hedge ratios in the New York Mercantile Exchange oil futures markets and examines their hedging performance. We also introduce a Markov regime switching vector error correction model with GARCH error structure. This specification links the concept of disequilibrium with that of uncertainty (as measured by the conditional second moments) across high and low volatility regimes. Overall, in and out-of-sample tests indicate that state dependent hedge ratios are able to provide significant reduction in portfolio risk.     

On Reform Intensity under Uncertainty

We model policy reform as a way to affect the stochastic process of relative returns that firms face when switching from old to new activities. This stochastic process has an Ito process component that is noncontrollable and policy reforms result in jumps in relative returns that arrive according to a Poisson process. The intensity of policy reform depends on the arrival rate and magnitude of jumps. We use a single firm model to understand the reaction of the firm to such a stochastic process and the usual hysteresis results in switching between old and new activities. Aggregation to the level of all firms leads to an appropriate definition of the government payoff function, and we use this to obtain the optimal level of reform. The results are as follows: there exists an optimal level of radical reform that overcomes the hysteresis behavior of firms; if such a level is not desirable, then the intensity of policy reform is not at an extreme point; and this gradual level of optimal reform is lower if uncertainty is higher.J. Comp. Econom.,December 1997,25(3), pp. 297–321. Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142; Olin School of Business, Washington University, St. Louis, Missouri 63130; and IBM, Armonk, New York 10504.     

Growth determinants,intra-distribution mobility,and convergence of state-level agricultural productivity in the USA

Stochastic kernels and state-level data are used in this paper to investigate mobility and convergence of the agricultural Total Factor Productivity (TFP) in the U.S.A. and to determine the influence of certain growth-related factors on the shape of the equilibrium TFP distribution. According to the empirical results, increasing returns to scale, materials intensity, and regional differences are likely to increase the dispersion of the cross-section TFP distribution, while higher rates of catchingup work towards reducing it. (JEL: Q16, O30, C14)