Time series copulas for heteroskedastic data

We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We suggest copulas for first‐order Markov series, and then extend them to higher orders and multivariate series. We derive the copula of a volatility proxy, based on which we propose new measures of volatility dependence, including co‐movement and spillover in multivariate series. In general, these depend upon the marginal distributions of the series. Using exchange rate returns, we show that the resulting copula models can capture their marginal distributions more accurately than univariate and multivariate generalized autoregressive conditional heteroskedasticity models, and produce more accurate value‐at‐risk forecasts.     

Advertising and brand attitudes: Evidence from 575 brands over five years

Quantitative Marketing and Economics - Little is known about how different types of advertising affect brand attitudes. We investigate the relationships between three brand attitude variables...     

Crisis leadership in economic recession: A three-barrier approach to offset external constraints

An economic recession is a type of crisis originated from external factors that may imperil an organization’s survival depending on the intensity and duration of the crisis. In peripheral European countries, such as Portugal, the recent financial crisis had devastating effects on various business activities. As a result, Portugal represents an important case study in examining how some corporate leaders have handled the economic recession successfully. I interviewed 20 corporate managers to capture their perceptions of the leadership traits and behaviors exhibited by their CEOs in guiding their companies through the recession. In economic recessions, negative constraints do not affect the most effective leaders, who instead erect barriers against the high-pressure conditions to create a supportive, positive work environment. In order to achieve maximum effectiveness, leaders must act as blocking agents against the negative social impacts of the economic crisis, including the fragility of trust in organizational life (i.e., a barrier against distrust), uncertainty of the future (i.e., a barrier against uncertainty), and toxic emotions (i.e., a barrier against toxic emotions).     

Unfolding Patterns of Unpaid Household Work in Latin America

Although Colombia, Mexico, Peru, and Uruguay show similar empirical patterns in terms of time women devote to unpaid work, they also present important variations in how unpaid work is distributed between men and women. Using time-use surveys for the 2007–10 period, this study finds a uniform pattern across the four countries regarding the main individual-level variables related to the allocation of unpaid work. When decomposing the gender gap in hours devoted to unpaid work, most of the difference cannot be attributed to variations in observable characteristics of men and women: the unexplained part of the gap is the dominant part. Results suggest that both the strength of traditional gender roles and existing welfare architecture are relevant factors in understanding variations in how unpaid work is distributed between men and women in these four countries. The results reaffirm that powerful interventions are needed to shift gender norms about unpaid work.     

Characterization of the Row Geometric Mean Ranking with a Group Consensus Axiom

An axiomatic approach is applied to the problem of extracting a ranking of the alternatives from a pairwise comparison ratio matrix. The ordering induced by row geometric mean method is proved to be uniquely determined by three independent axioms, anonymity (independence of the labelling of alternatives), responsiveness (a kind of monotonicity property) and aggregation invariance, which requires the preservation of group consensus, that is, the pairwise ranking between two alternatives should remain unchanged if unanimous individual preferences are combined by geometric mean.     

Inflation,real economic growth and unemployment expectations: an empirical analysis based on the ECB survey of professional forecasters

Expectations are at the centre of modern macroeconomic theory and policymakers. In this article, we examine the predictive ability and the consistency properties of macroeconomic expectations using data of the European Central Bank (ECB) Survey of Professional Forecasters (SPF). In particular, we provide evidence on the properties of forecasts for three key macroeconomic variables: the inflation rate, the growth rate of real gross domestic product and the unemployment rate.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

Class structure and inequality during the industrial revolution: lessons from England's social tables, 1688–1867

This article measures the size and incomes of six major social classes across the industrial revolution using social tables for England and Wales in 1688, 1759, 1798, 1846, and 1867. Lindert and Williamson famously revised these tables, and this article extends their work in three directions. First, servants are removed from middle‐ and upper‐class households in the tables of King, Massie, and Colquhoun and tallied separately. Second, estimates are made for the same tables of the number and incomes of women and children employed in the various occupations, and, third, incomes are broken down into rents, profits, and employment income. These extensions to the tables allow variables to be computed that can be checked against independent estimates as a validation exercise. The tables are retabulated in a standardized set of six social groups to highlight the changing structure of society across the industrial revolution. Gini coefficients are computed from the social tables to measure inequality. These measures confirm that Britain traversed a ‘Kuznets curve’ in this period. Changes in overall inequality are related to the changing fortunes of the major social classes.