Can free-shipping hurt online retailers?

Quantitative Marketing and Economics - In this paper, we investigate optimal pricing strategies for an online grocery retailer that derives its profits from delivery fees and grocery sales. We base...     

Modeling current and emerging threats in the airport domain through adversarial risk analysis

Airports are critical infrastructures entailing intense human, commercial and economic activity. As such, they are preferred targets for criminal and terrorist groups, who are attracted by the promisingly high revenues they might get from an attack. Every year, airport authorities worldwide have to face, with limited resources, attacks arising from different adversaries. There are several sensible areas within an airport organization that are especially vulnerable to the terrorist threat, including, among others: (1) those related to human lives (of passengers or staff); (2) airport infrastructure (airport perimeter, main terminal, Air Traffic Control Tower, runways, hangars, etc.); (3) aircrafts and other ground vehicles; and (4) IT systems and services. Besides the more traditional ones, we are particularly concerned with attacks launched against the last type of targets, an emerging and increasingly worrisome threat. Specifically, we analyze the impact of cyber-attacks launched by organized groups whose main goal is to take hold of airport operations. In some cases, in order to have more chances to achieve their purpose (and take advantage of its eventual success), cyber attackers may be backed up by a terrorist group who will try to interfere with the Air Traffic Management network. In this paper, we aim at supporting airport authorities in their fight against both threats, by devising a security allocation plan. We provide an adversarial risk analysis model to address the problem, and apply it to obtain the optimal portfolio of preventive measures in an illustrative case study. The model is open to extensions, as e.g. larger and more complex technical infrastructures, new threats, or additional recovery measures deployed by different defensive agents.     

Nonlinearity and nonstationarity in international art market prices: evidence from Markov-switching ADF unit root tests

This study investigates the presence (or lack thereof) of nonlinear dynamics and nonstationarity in international art market prices using quarterly data for the period 1990–2011. We first test whether art market price indices follow stochastic trends or whether they are stationary by means of linear unit root tests. Next, we estimate the Markov regime-switching ADF model and test whether the linear or the nonlinear regime-switching model provides a better characterization of the global art market price series. We find that all art market price indices (except for Drawings) exhibit nonlinearity. To our knowledge, our study is the first one in the literature to suggest that a nonlinear (Markov regime-switching) model provides a better characterization of the behavior of price dynamics in international art markets. In particular, our findings indicate that the market for the overall global art market, paintings, old masters, sculptures, photographs, prints, and contemporary art might indeed be stationary while exhibiting nonlinear regime-switching properties. On the other hand, the market for drawings and the Nineteenth century art are found to be nonstationary. Overall, despite the common ground of a regime-switching framework, we still find that the sub-segments of the art market have their own inner regime switching dynamics and hence they can evolve differently overtime.     

An alternative consideration for the testing procedure of the S-curve hypothesis

Backus et al. (Am Econ Rev 84:84–103, 1994) introduced a new theoretical interpretation of the short-run relationship between the terms of trade and the trade balance: movements in net exports and the tendency for the trade balance to be negatively correlated with current and future movements in the terms of trade, but positively correlated with past movements. There have been several papers aimed at testing the S-curve hypothesis in the related literature. The most common empirical approach used in those papers is the cross-correlation analysis. This paper highlights several deficiencies of the cross-correlation analysis when being used directly to test for the validity of the mentioned hypothesis. The purpose of this paper is to offer an alternative approach so as to combine the causality theory in econometrics with the testing procedure of the S-curve hypothesis via the cross-correlations. The paper’s empirical results reveal that disaggregated bilateral trade data between the United States and China almost never support the S-curve hypothesis, contrarily to a previous paper employing the same data, but utilizing the conventional cross-correlation analysis. Why this difference may have appeared is also discussed.     

Data Mining Approach for Direct Marketing of Banking Products with Profit/Cost Analysis

Nowadays, many businesses, such as banks, use direct marketing methods to reach customers to minimize the campaigning cost and maximize the return rate. To achieve this, huge customer data should be analyzed to determine the most appropriate product offer for each customer and the most effective channel to reach her/him. However, since only a very small amount of responses collected from the customers are positive to the offers, the dataset is very imbalanced. This decreases sensitivity ratio of prediction results and makes it difficult to make a successful product and channel selection for the offer. In this paper, we propose a hybrid system, which first classifies the customers to decide if s/he is interested in the offered product, and then clusters them for product and channel suggestions. Experiments with real life banking data show very promising accuracy results for predicting the proper product and channel for the customers. Moreover, cost-profit analysis is also added to this problem. Our experiment results show that the proposed method decreases a fraction of the total profit, but since the decrease in the total cost is very large, there is a huge increase in the overall profit/cost ratio.     

Downside business confidence spillovers in Europe: evidence from causality-in-risk tests

This paper employs an extreme risk spillovers test to investigate the bilateral business confidence spillovers between Greece, Italy, Spain, Portugal, France, and Germany. After controlling for domestic economic developments in each country and common international factors, downside risk spillovers are detected as a causal feedback between Spain and Portugal and unilaterally from Spain to Italy. Extremely low business sentiments in France, Germany, and Greece are mostly due to the common adverse economic environment and to each country’s own domestic economic developments.     

Return and volatility spillovers among CIVETS stock markets

Coined in 2009, the CIVETS refers to Colombia, Indonesia, Vietnam, Egypt, Turkey, and South Africa as a new group of frontier emerging markets with young and growing populations and dynamic economies. We provide a first look into the return and volatility spillovers between the CIVETS countries by employing causality-in-mean and causality-in-variance tests. The empirical results indicate that the contemporaneous spillover effects are generally low. Nevertheless, CIVETS stock markets may exhibit higher degrees of co-movements at times. The structure of the causal relationships further suggests the presence of intra-regional and inter-regional return and volatility interdependence effects.     

Assignment maximization

We evaluate the goal of maximizing the number of individuals matched to acceptable outcomes. We present two mechanisms that maximize assignments. The first is Pareto efficient and undominated—in terms of the number of assignments—in equilibrium. The second is fair for unassigned agents and assigns weakly more agents than any stable mechanism in equilibrium.