Online banking services: a meta-analytic review and assessment of the impact of antecedents and consequents on satisfaction

This paper provides a meta-analysis of the generalizations in the relationships between the antecedents and consequents of satisfaction with online banking services. In total, 118 observations were analysed, with a sample of 49,607 respondents in 39 published articles from studies indexed in ten databases (Jstor, Emerald, PsycINFO, Taylor & Francis, Elsevier Science Direct, Scopus, ProQuest, SciELO, Google Scholar and EBSCO). Specifically, for the data analysis, we used the correlation coefficient r (plus χ², f test, t test, z test and β values). The results showed that constructs related to uncertainty, as evoked by online devices, system performance, quality of device content and online banking device structures, are significant and positive antecedents of consumer satisfaction. We also found that satisfaction with online banking services promotes trust and loyalty. Finally, we also detected that the relationship between reliability, satisfaction and service quality is stronger among Western banking consumers.     

A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries

In this paper we propose a flexible model to describe nonlinearities and long-range dependence in time series dynamics. The new model is a multiple regime smooth transition extension of the Heterogeneous Autoregressive (HAR) model, which is specifically designed to model the behavior of the volatility inherent in financial time series. The model is able to simultaneously approximate long memory behavior, as well as describe sign and size asymmetries. A sequence of tests is developed to determine the number of regimes, and an estimation and testing procedure is presented. Monte Carlo simulations evaluate the finite-sample properties of the proposed tests and estimation procedures. We apply the model to several Dow Jones Industrial Average index stocks using transaction level data from the Trades and Quotes database that covers ten years of data. We find strong support for long memory and both sign and size asymmetries. Furthermore, the new model, when combined with the linear HAR model, is viable and flexible for purposes of forecasting volatility.     

An alternative approach to estimating demand: Neural network regression with conditional volatility for high frequency air passenger arrivals

In this paper we provide an alternative approach to analyze the demand for international tourism in the Balearic Islands, Spain, by using a neural network model that incorporates time-varying conditional volatility. We consider daily air passenger arrivals to Palma de Mallorca, Ibiza and Mahon, which are located in the islands of Mallorca, Ibiza and Menorca, respectively, as a proxy for international tourism demand for the Balearic Islands. Spain is a world leader in terms of total international tourist arrivals and receipts, and Mallorca is one of the most popular destinations in Spain. For tourism management and marketing, it is essential to forecast high frequency international tourist demand accurately. As it is important to provide sensible international tourism demand forecast intervals, it is also necessary to model their variances accurately. Moreover, time-varying variances provide useful information regarding the risks associated with variations in international tourist arrivals.     

Poisson–geometric INAR(1) process for modeling count time series with overdispersion

In this paper, we propose a new first‐order non‐negative integer‐valued autoregressive [INAR(1)] process with Poisson–geometric marginals based on binomial thinning for modeling integer‐valued time series with overdispersion. Also, the new process has, as a particular case, the Poisson INAR(1) and geometric INAR(1) processes. The main properties of the model are derived, such as probability generating function, moments, conditional distribution, higher‐order moments, and jumps. Estimators for the parameters of process are proposed, and their asymptotic properties are established. Some numerical results of the estimators are presented with a discussion of the obtained results. Applications to two real data sets are given to show the potentiality of the new process.     

International market links and volatility transmission

This paper gauges volatility transmission between stock markets by testing conditional independence of their volatility measures. In particular, we check whether the conditional density of the volatility changes if we further condition on the volatility of another market. We employ nonparametric methods to estimate the conditional densities and model-free realized measures of volatility, allowing for both microstructure noise and jumps. We establish the asymptotic normality of the test statistic as well as the first-order validity of the bootstrap analog. Finally, we uncover significant volatility spillovers between the stock markets in China, Japan, UK and US.     

Reparameterized inverse gamma regression models with varying precision

In this article, we propose a mean linear regression model where the response variable is inverse gamma distributed using a new parameterization of this distribution that is indexed by mean and precision parameters. The main advantage of our new parametrization is the straightforward interpretation of the regression coefficients in terms of the expectation of the positive response variable, as usual in the context of generalized linear models. The variance function of the proposed model has a quadratic form. The inverse gamma distribution is a member of the exponential family of distributions and has some distributions commonly used for parametric models in survival analysis as special cases. We compare the proposed model to several alternatives and illustrate its advantages and usefulness. With a generalized linear model approach that takes advantage of exponential family properties, we discuss model estimation (by maximum likelihood), black further inferential quantities and diagnostic tools. A Monte Carlo experiment is conducted to evaluate the performances of these estimators in finite samples with a discussion of the obtained results. A real application using minerals data set collected by Department of Mines of the University of Atacama, Chile, is considered to demonstrate the practical potential of the proposed model.     

AGOA and Apparel: Who Captures the Tariff Rent in the Presence of Preferential Market Access?

The United States grants preferential (tariff‐ and quota‐free) market access to a list of products from eligible countries in sub‐Saharan Africa through the African Growth and Opportunity Act (AGOA). We analyse the increase in prices received by apparel exporters who benefited from AGOA preferences. In the presence of competitive markets, export prices should increase as much as the tariff which was previously collected by the US government. We refer to this price increase as the ‘tariff preference rent’ since exporters receive this income as the rent for their preferential status. The results show that exporters receive only one‐third of this rent and smaller exporters receive less than larger and established ones. We then provide evidence that suggests this may be due to the degree of market power enjoyed by US importers when facing African exporters.