Can we predict fine wine and alcohol prices? Yes, but it depends on the forecasting horizon. We make this point by considering the Liv-ex Fine Wine 100 and 50 Indices, the retail and wholesale alcohol prices in the United States for the period going from January 1992 to March 2022. We use rich and diverse datasets of economic, survey, and financial variables as potential price drivers and adopt several combination/dimension reduction techniques to extract the most relevant determinants. We build a comprehensive set of models and compare forecast performances across different selling levels and alcohol categories. We show that it is possible to predict fine wine prices for the 2-year horizon and retail/wholesale alcohol prices at horizons ranging from 1 month to 2 years. Our findings stress the importance of including consumer survey data and macroeconomic factors, such as international economic factors and developed markets equity risk factors, to enhance the precision of predictions of retail/wholesale (fine wine) prices.

Applying the difference-in-difference (DID) estimation procedure, this study quantifies the wheat blast (Magnaporthe oryzae pathotype Triticum) induced losses in wheat yield, quantity of wheat sold, consumed, or stored, as well as wheat grain value in Bangladesh in 2016 following a disease outbreak that affected over 15,000 ha. Estimates show that the blast-induced yield loss was 540 kg ha−1 on average for households in blast-affected districts. Estimated total wheat production loss was approximately 8,205 tons worth USD 2.1 million in during the 2016 outbreak. Based on these insights, we discuss the need for long-term assured investment and concerted research efforts in controlling transboundary diseases such as wheat blast, including the importance of weather forecast driven early warning systems and the dissemination of blast-resistant varieties.

Quantifying tail dependence is an important issue in insurance and risk management. The prevalent tail dependence coefficient (TDC), however, is known to underestimate the degree of tail dependence and it does not capture non-exchangeable tail dependence since it evaluates the limiting tail probability only along the main diagonal. To overcome these issues, two novel tail dependence measures called the maximal tail concordance measure (MTCM) and the average tail concordance measure (ATCM) are proposed. Both measures are constructed based on tail copulas and possess clear probabilistic interpretations in that the MTCM evaluates the largest limiting probability among all comparable rectangles in the tail, and the ATCM is a normalized average of these limiting probabilities. In contrast to the TDC, the proposed measures can capture non-exchangeable tail dependence. Analytical forms of the proposed measures are also derived for various copulas. A real data analysis reveals striking tail dependence and tail non-exchangeability of the return series of stock indices, particularly in periods of financial distress.

We study marital assortative mating in education and its relation to dowry in India. There are four main results and contributions of this paper. First, instrumental variable estimates using Indian Human Development Survey-II data suggest existence of positive assortative mating in education levels of husband and wife. Second, this association is weaker in dowry-prominent districts suggesting that in districts with strong patriarchal norms, high dowry transfers could substitute for lower bride's education. Third, we study the independent effect of husband's and wife's education and its interaction on dowry. Estimates suggest that dowry rises with the groom's education and falls with the bride's schooling years. However, the joint effect of husband-and-wife education on dowry is negative, implying that though dowry rises with groom's education, the rate of increase is smaller the more educated the bride is. Finally, to explain the empirical results, we propose a theoretical model of assortative mating in the presence of dowry.

Wavelet theory is known to be a powerful tool for compressing and processing time series or images. It consists in projecting a signal on an orthonormal basis of functions that are chosen in order to provide a sparse representation of the data. The first part of this article focuses on smoothing mortality curves by wavelets shrinkage. A chi-square test and a penalized likelihood approach are applied to determine the optimal degree of smoothing. The second part of this article is devoted to mortality forecasting. Wavelet coefficients exhibit clear trends for the Belgian population from 1965 to 2015, they are easy to forecast resulting in predicted future mortality rates. The wavelet-based approach is then compared with some popular actuarial models of Lee–Carter type estimated fitted to Belgian, UK, and US populations. The wavelet model outperforms all of them.

This paper aims to provide a brief and relatively non-technical overview of state-of-the-art forecasting with large data sets. We classify existing methods into four groups depending on whether data sets are used wholly or partly, whether a single model or multiple models are used and whether a small subset or the whole data set is being forecast. In particular, we provide brief descriptions of the methods and short recommendations where appropriate, without going into detailed discussions of their merits or demerits.