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.     

Market impact: a systematic study of the high frequency options market

This paper deals with a fundamental subject that has seldom been addressed in recent years, that of market impact in the options market. Our analysis is based on a proprietary database of metaorders—large orders that are split into smaller pieces before being sent to the market—on one of the main Asian markets. In line with our previous work on the equity market [Said, E., Bel Hadj Ayed, A., Husson, A. and Abergel, F., Market impact: A systematic study of limit orders. Mark. Microstruct. Liq., 2018, 3(3&4), 1850008.], we propose an algorithmic approach to identify metaorders, based on some implied volatility parameters, the at the money forward volatility and at the money forward skew. In both cases, we obtain results similar to the now well-understood equity market: Square-Root Law, Fair Pricing Condition and Market Impact Dynamics.     

Brand loyalty evolution and the impact of category characteristics

A common managerial belief indicates that brand loyalty declines over the years, with consumers becoming more heterogeneous in their choices. The earlier research investigating the phenomenon of brand loyalty decline is, however, inconclusive and does not offer an answer to the reasons behind brand loyalty evolution. In this study, we investigate brand loyalty evolution and explore the impact that a number of category characteristics have on driving brand loyalty evolution. We use Danish panel data across 54 categories over a period of 6 years (2006–2011). Our findings show that at the aggregate level, brand loyalty declines, but this evolution is category-specific, with only a small number of categories showing a significant decline. We further demonstrate that an increase in category penetration results in a negative impact on brand loyalty evolution, whereas an increase in the share of private label brands has a positive impact. We discuss the implications for theory and practice.

     

Green Public Procurement: A Neglected Tool in the European Green Deal Toolbox?

Intereconomics - Public procurement amounts to around 14% of European Union GDP and, given this size, could well represent an important tool to foster the green transition. However, green public...     

Cost comparison of continued anticoagulation with rivaroxaban versus placebo based on the 1-year EINSTEIN-Extension trial efficacy and safety results

Aims: The EINSTEIN-Extension trial (EINSTEIN-EXT) found that continued treatment with rivaroxaban for an additional 6 or 12 months (vs placebo) after 6–12 months of initial anticoagulation significantly reduced the risk of recurrent venous thromboembolism (VTE) with a small non-significant increased risk of major bleeding (none fatal or in critical site). This study aimed to compare total healthcare cost between rivaroxaban and placebo, based on the EINSTEIN-EXT event rates.

Methods: Total healthcare cost was calculated as the sum of treatment and clinical event costs from a US managed care perspective. Treatment duration and event rates were obtained from the EINSTEIN-EXT study. Adjustment on treatment duration was made by assuming a 10% non-adherence rate. Drug costs were based on wholesale acquisition costs. Cost estimates for clinical events (i.e. recurrent deep vein thrombosis [DVT], recurrent pulmonary embolism, major bleeding, clinically relevant non-major bleeding) were determined from the literature. Results were examined over a ±20% range of each cost component and over 95% confidence intervals (CIs) of event rate differences in deterministic (one-way) and probabilistic sensitivity analyses (PSA).

Results: Total healthcare cost was $1,454 lower for rivaroxaban-treated (vs placebo-treated) patients in the base-case, with a lower clinical event cost fully offsetting drug cost. The cost savings of recurrent DVT alone (–$3,102) was greater than drug cost ($2,723). Total healthcare cost remained lower for rivaroxaban in the majority (73%) of PSA (cost difference [95% CI]?=?–$1,454 [–$2,396, $1,231]).

Limitations: This study was conducted over the 1-year observation period of the EINSTEIN-EXT trial, which limited “real-world” applicability and examination of long-term economic impact. Assumptions on drug and clinical event costs were US-based and, thus, not applicable to other healthcare systems.

Conclusions: Total healthcare costs were estimated to be lower for patients continuing rivaroxaban therapy compared to those receiving placebo in VTE patients who had completed 6–12 months of VTE treatment.     

Fixed-Term Contracts and Labor Market Duality in France

The French labor market is segmented between permanent and temporary workers. The second category has difficulty in getting an open-ended contract. This paper aims at depicting workers on short-term contracts and shows the consequences on their professional career are negative and significant. A large part of the wage gap between permanent and temporary workers remains unexplained by observable characteristics. They receive less on-the-job training and their likelihood of obtaining a stable job is lower than 30% after 1 year and a half. They have also a higher probability of being the adjustment variable in case of an economic negative shock. These different findings show the importance of using public policies to encourage transitions from fixed-term to permanent employment and reducing labor-market duality.     

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.     

Information and Knowledge as Antecedents of Consumer Attitudes and Intentions to Buy and Recommend Fair-Trade Products

The authors propose and empirically test a causal model to understand how the availability of fair-trade information and consumer knowledge about this issue affect consumers’ attitudes and intentions toward fair-trade products. The model is built upon the attitude-behavior paradigm and the premises of agency theory. It is tested through structural equation modeling with a sample of 292 Spanish consumers. The findings are that consumers do not have good knowledge about fair trade and that this is significantly determined by the lack of information about this in the market. It is also observed that consumers’ perceptions about the availability of fair-trade information have negative effects on their concern about this issue and that such information as is available is not effective in reducing consumer skepticism. The research represents an extension of previous fair-trade literature because the role of information and communication in improving consumer attitudes and buying intentions has rarely been explored in the case of ethical products.     

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.