Designing Electricity Auctions in the Presence of Transmission Constraints

This paper analyzes the effect that different designs in the access to fnancial transmission rights has on spot electricity auctions. In particular, I characterize the equilibrium in the spot electricity market when financial transmission rights are assigned to the grid operator and when financial transmission rights are assigned to the firm that submits the lowest bid in the spot electricity auction. When financial transmission rights are assigned to the grid operator, my model, in contrast with the models available in the literature, works out the equilibrium for any transmission capacity. Moreover, I have found that an increase in transmission capacity not only increases competition between markets but also within a single market. When financial transmission rights are assigned to the firm that submits the lowest bid in the spot electricity auction, firms compete not only for electricity demand, but also for transmission rights and the arbitrage profits derived from its hold. I have found that introduce competition for transmission rights reduces competition in spot electricity auctions.

A Multilevel Model with Time Series Components for the Analysis of Tribal Art Prices

In the present work we perform an econometric analysis of the Tribal art market. To this aim, we use a unique and original database that includes information on Tribal art market auctions worldwide from 1998 to 2011. In Literature, art prices are modelled through the hedonic regression model, a classic fixed-effect model. The main drawback of the hedonic approach is the large number of parameters, since, in general, art data include many categorical variables. In this work, we propose a multilevel model for the analysis of Tribal art prices that takes into account the influence of time on artwork prices. In fact, it is natural to assume that time exerts an influence over the price dynamics in various ways. Nevertheless, since the set of objects change at every auction date, we do not have repeated measurements of the same items over time. Hence, the dataset does not constitute a proper panel; rather, it has a two-level structure in that items, level-1 units, are grouped in time points, level-2 units. The main theoretical contribution is the extension of classical multilevel models to cope with the case described above. In particular, we introduce a model with time dependent random effects at the second level. We propose a novel specification of the model, derive the maximum likelihood estimators and implement them through the E-M algorithm. We test the finite sample properties of the estimators and the validity of the own-written R-code by means of a simulation study. Finally, we show that the new model improves considerably the fit of the Tribal art data with respect to both the hedonic regression model and the classic multilevel model.