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A linear and geometrically non‐linear computation of a laminated composite torispherical shell subjected to internal pressure was performed by using the layered finite element whose formulation is based on degeneration principle. Geometric non‐linearity in terms of large deformations with total Lagrangian formulation was taken into account. The effect of the lamination schemes on geometric non‐linear behaviour and stress resultant distributions was analysed. The fibre directions have not a great influence on the shape of the load‐displacement curves. In contrast to the hoop stress resultant distribution, the moment distribution is significantly influenced by the lamination schemes. The influence of the lamination schemes on bending moments is greater in non‐linear than in linear computations. Likewise, the effect of the fibre orientation is greater on the hoop than on the meridional moment distribution. In unsymmetric laminated shells the values of the hoop moments exceed those of the meridional moments which is a considerable difference from metallic isotropic shells.

This paper aims to investigate the effect of three-dimensional (3D) inlet guide vanes (IGVs) on performance of a centrifugal pump.

A design method for 3D IGVs is proposed based on the controllable velocity moment, which is determined by a fourth-order dimensionless function. Numerical simulation of the centrifugal pump with IGVs is carried out by solving the Reynolds-averaged Navier–Stokes equations. The method of frozen rotor is applied to couple the stationary and rotational domain.

The efficiency of pump with 3D IGVs is higher than that with 2D IGVs for most prewhirl angles, which validate the advancement of 3D IGVs on prewhirl regulation. The effect of prewhirl regulation at small flow rate is more significant than that at large flow rate.

A prediction model of velocity moment based on the Oseen vortex is proposed to describe the flow pattern downstream the IGVs.

This paper examines Aman Ullah’s contributions to robust inference, finite sample econometrics, nonparametrics and semiparametrics, and panel and spatial models. His early works on robust inference and finite sample theory were mostly motivated by his thesis advisor, Professor Anirudh Lal Nagar. They eventually led to his most original rethinking of many statistics and econometrics models that developed into the monograph Finite Sample Econometrics published in 2004. His desire to relax distributional and functional-form assumptions lead him in the direction of nonparametric estimation and he summarized his views in his most influential textbook Nonparametric Econometrics (with Adrian Pagan) published in 1999 that has influenced a whole generation of econometricians. His innovative contributions in the areas of seemingly unrelated regressions, parametric, semiparametric and nonparametric panel data models, and spatial models have also inspired a larger literature on nonparametric and semiparametric estimation and inference and spurred on research in robust estimation and inference in these and related areas.

The purpose of this paper is to introduce a non‐normality premium (NNP) to identify the extra return that will compensate an investor for a non‐normal return distribution. The NNP quantifies the economic significance of non‐normality to complement a statistical significance test of non‐normality, such as the Jarque‐Bera test.

The NNP is patterned after the risk premium, the amount that compensates an investor for the risk of an investment. The theoretical NNP is examined on the margins with Taylor series approximation and applied to hedge fund data.

An increase of 1 in the skewness has the same effect on an investor as an increase in the mean of 2.5 basis points per month. An increase of 1 in the kurtosis has the same effect on an investor as a decrease in the mean of 0.15 basis points per month. A sample of 716 hedge funds revealed that while 72 per cent statistically reject normality, only 29 per cent require more than a single basis point per month difference in the mean to compenscate an investor for the non‐normality.

The NNP allows for a valuation on the higher moments (skewness and kurtosis) of an investor's return distribution. The evaluation is tailored to the individual through use of a utility function. Once applied to an alternative investment vehicle, it is learned that rejecting normality is not sufficient grounds to suspect that the non‐normality is important to investors.

The purpose of this paper is to find and describe some stylized facts for foreign exchange and stock market returns, which are explored using statistical methods.

Formal statistics for testing presence of autocorrelation, asymmetry, and other deviations from normality are applied. Dynamic correlations and different kernel estimations and approximations to the empirical distributions are also under scrutiny. Furthermore, dynamic analysis of mean, standard deviation, skewness and kurtosis are also performed to evaluate time‐varying properties in return distributions.

The findings include: different types of non‐normality in both markets, fat tails, excess furtosis, return clustering and unconditional time‐varying moments. Identifiable volatility cycles in both forex and stock markets are associated to common macro financial uncertainty events.

The paper is the first work of this type in Peru.

In June of 2001, Tokyo Electric Power Company (TEPCO) and Tokyo Gas Supply Company (TGSC) made a zero‐cost risk swap contract on the average temperature of August and September of 2001 in Tokyo for their adverse situations. This is an exchange of two options on the average temperature, by which TEPCO and TGSC can, respectively, hedge against a cold summer and a hot summer. The purpose of this paper is to develop a theoretical framework to evaluate the fairness or rationality of such a zero‐cost weather risk swap, derive some conditions to check the rationality and empirically evaluate the fairness of the above temperature risk swap between the two companies.

To provide a framework for analyzing weather risk swap, the authors used a statistical approach and a basic analysis is given with data and simulation.

First, the authors define the concept of full equivalence and moment equivalence of two options on a weather index and then derive some conditions for full and moment equivalences. Third, using the stochastic volatility model in Kariya et al., it is shown that the options in the TEPCO–TGSC risk swap are neither fully equivalent nor moment‐equivalent as they stand.

This paper originates the weather risk swap valuation problem and gives a framework to analyze and value the equivalence of a temperature risk swap. This method can be applied to various possible risk swaps for risk management.

THE work described in this paper was undertaken to investigate the behaviour of a magnesium alloy beam clastically and plastically deformed by a uniform bending moment at room temperature. The object of the work was to obtain relations between stresses and strains in the beam, to afford a basis for design, in cases where it is required to submit magnesium alloy structures to bending stresses exceeding the elastic limit.

Haitun has recently shown that empirical distributions are of two types—‘Gaussian’ and ‘Zipfian’—characterized by the presence or absence of moments. Gaussian‐type distributions arise only in physical contexts: Zipfian only in social contexts. As the whole of modern statistical theory is based on Gaussian distributions, Haitun thus shows that its application to social statistics, including cognitive statistics, is ‘inadmissible’. A new statistical theory based on ‘Zipfian’ distributions is therefore needed for the social sciences. Laplace's notorious ‘law of succession’, which has evaded derivation by classical probability theory, is shown to be the ‘Zipfian’ frequency analogue of the Bradford law. It is argued that these two laws together provide the most convenient analytical instruments for the exploration of social science data. Some implications of these findings for the quantitative analysis of information systems are briefly discussed.

The curvilinear shape of a bond price‐yield curve implies that risk management based on a linear approximation using duration is only viable for very small changes in interest rates. Not accounting for convexity when there are large yield changes can result in critical errors in measuring or hedging interest rate risk. The linear approximations will under‐or overestimate the value at risk (VaR) for non‐linear financial instruments. Nonlinearity can be particularly problematic if there are large changes in market risk factors. The large changes are more likely to occur when VaR is computed for high confidence levels and/or longer time horizons. Even if the movements in risk factors are small, estimation errors in VaR would get larger as the degree of non‐linearity in financial instruments increases.