Dynamic managerial capabilities: Scale development and validation

Studies on dynamic managerial capabilities still have a rather theoretical nature, and there is great difficulty in finding a valid, reliable instrument to measure this construct. The present study contributes to solving this problem: It aims to develop and validate a scale to measure dynamic managerial capabilities. In this investigation, we develop a scale for three factors related to dynamic managerial capabilities: human capital, managerial cognition (already described in the literature), and relationship networks. The prominent finding in this research is the factor that is referred to here as relationship networks.     

New Evidence from Brazil on Trade Liberalization and Productivity Growth*

This article presents evidence on the positive effect of international trade on productivity growth using industrial level data preceding and following Brazil's trade liberalization in 1988–90. Our data reveal large and widespread productivity improvement across industries after barriers to trade were drastically reduced. Econometric results confirm the association between trade liberalization and productivity growth and show that the impact was indeed substantial: The observed tariff reduction in the period brought a 6% estimated increase in total factor productivity growth rate and a similar impact on labor productivity.     

Consumption and Portfolio Choice over the Life Cycle

This article solves a realistically calibrated life cycle modelof consumption and portfolio choice with non-tradable laborincome and borrowing constraints. Since labor income substitutesfor riskless asset holdings, the optimal share invested in equitiesis roughly decreasing over life. We compute a measure of theimportance of human capital for investment behavior. We findthat ignoring labor income generates large utility costs, whilethe cost of ignoring only its risk is an order of magnitudesmaller, except when we allow for a disastrous labor incomeshock. Moreover, we study the implications of introducing endogenousborrowing constraints in this incomplete-markets setting.     

Do economic integration agreements affect trade predictability? A group effect analysis

Recent theoretical findings in the trade literature suggest that economic integration agreements (EIAs) not only increase the level of trade but also make it more stable and predictable. This paper proposes a Hausman–Taylor quantile regression approach to identify the causal effect of EIA membership on trade predictability. The proposed methodology accounts for group effects in the gravity equation and is computationally efficient. Our results corroborate the theoretical findings that EIAs make trade more stable and predictable and this conclusion is even stronger for deeper EIAs.     

Supply chain risk analysis: a shipbuilding industry case

A lack of empirical studies on Supply Chain Risk Management (SCRM) is observed, especially in the industries of large projects. Therefore, this paper conducts a risk analysis on the Brazilian Shipbuilding Industry by generating an initial risk profile. A panel was conducted with executives from major shipyards, and the primary risks were identified, assessed, and evaluated. Findings corroborate the academic literature, reinforcing that companies recognise the importance of SCRM, but the concept is incipient and far from being successfully implemented in real life. Despite the fact that the shipyards identified important risks, the focus was on internal factors. Because the industry was exposed to risks from the external environment, no risk mitigation actions existed, resulting in the bankruptcy of many shipbuilders. This research is an initial step toward investigating SCRM techniques, offering academics a novel empirical approach that can serve as a systematic risk management tool for supply chain planning.     

Extreme Value Theory and Statistics of Univariate Extremes: A Review

Statistical issues arising in modelling univariate extremes of a random sample have been successfully used in the most diverse fields, such as biometrics, finance, insurance and risk theory. Statistics of univariate extremes (SUE), the subject to be dealt with in this review paper, has recently faced a huge development, partially because rare events can have catastrophic consequences for human activities, through their impact on the natural and constructed environments. In the last decades, there has been a shift from the area of parametric SUE, based on probabilistic asymptotic results in extreme value theory, towards semi‐parametric approaches. After a brief reference to Gumbel's block methodology and more recent improvements in the parametric framework, we present an overview of the developments on the estimation of parameters of extreme events and on the testing of extreme value conditions under a semi‐parametric framework. We further discuss a few challenging topics in the area of SUE. © 2014 The Authors. International Statistical Review © 2014 International Statistical Institute     

Antecedents of Purchase Intention in the Online Context

The understanding of the determinant factors of customer purchase intention is necessary, and it is equally important to study the online purchase context, since this context is disseminated among customers. A theoretical model has been elaborated on and tested, considering the constructs of information quality, distrust, and perceived risk as antecedents of purchase intention, and aiming to analyze the relationship among these constructs in the online purchase context. A quantitative research study has been performed by means of the application of a survey. Multivariate statistics techniques have been applied for data analyses, including structural equation modeling. This study contributes to the evolution of the empirically tested concepts by providing a greater individual understanding of each construct presented in the theoretical model, as well as the relationship among them as determinants of purchase intention; the indication is that meaningful relationships were found which may impact greater profitability and, consequently, greater competition for online retailers.     

Valuation of forward start options under affine jump-diffusion models

Under the general affine jump-diffusion framework of Duffie et al. [Econometrica, 2000, 68, 1343–1376], this paper proposes an alternative pricing methodology for European-style forward start options that does not require any parallel optimization routine to ensure square integrability. Therefore, the proposed methodology is shown to possess a better accuracy–efficiency trade-off than the usual and more general approach initiated by Hong [Forward Smile and Derivative Pricing. Working paper, UBS, 2004] that is based on the knowledge of the forward characteristic function. Explicit pricing solutions are also offered under the nested jump-diffusion setting proposed by Bakshi et al. [J. Finance, 1997, 52, 2003–2049], which accommodates stochastic volatility and stochastic interest rates, and different integration schemes are numerically tested.     

Asymptotic comparison at optimal levels of reduced‐bias extreme value index estimators

In this article we are interested in the asymptotic comparison, at optimal levels, of a set of semi‐parametric reduced‐bias extreme value (EV) index estimators, valid for a wide class of heavy‐tailed models, underlying the available data. Again, as in the classical case, there is not any estimator that can always dominate the alternatives, but interesting clear‐cut patterns are found. Consequently, and in practice, a suitable choice of a set of EV index estimators will jointly enable us to better estimate the EV index γ, the primary parameter of extreme events.