Asset Returns Under Model Uncertainty: Eveidence from the euro area, the U.K and the U.S

The goal of thes paper is to analyze predictability of future asset returns in the context of model uncertainty. Using data for the euro area, the US and the U.K., we show that one can improve the forecasts of stock returns using a Bayesian Model Averaging (BMA) approach, and there is a large amount of model uncertainty. The empirical evidence for the euro area suggests that several macroeconomic, financial and macro-financial variables are consistently among the most prominent determinants of risk premium. As for the U.S, only a few number of predictors play an important role. In the case of the UK, future stock returns are better forecasted by financial variables. These results are corroborated for both the M-open and the M-closed perspectives and in the context of "in-sample" and "out-of-sample" forescating. Finally, we highlight that the predictive ability of the BMA framework is stronger at longer periods, and clearly outperforms the constant expected returns and the autoregressive benchmark models.stock returns, model uncertainty, Bayesian Model Averaging

Monochromatic Clique Decompositions of Graphs

Let $G$ be a graph whose edges are coloured with $k$ colours, and $\mathcal H=(H_1,\dots , H_k)$ be a $k$-tuple of graphs. A monochromatic $\mathcal H$-decomposition of $G$ is a partition of the edge set of $G$ such that each part is either a single edge or forms a monochromatic copy of $H_i$ in colour $i$, for some $1\le i\le k$. Let $\phi_{k}(n,\mathcal H)$ be the smallest number $\phi$, such that, for every order-$n$ graph and every $k$-edge-colouring, there is a monochromatic $\mathcal H$-decomposition with at most $\phi$ elements. Extending the previous results of Liu and Sousa ["Monochromatic $K_r$-decompositions of graphs", Journal of Graph Theory}, 76:89--100, 2014], we solve this problem when each graph in $\mathcal H$ is a clique and $n\ge n_0(\mathcal H)$ is sufficiently large.Comment: 14 pages; to appear in J Graph Theor

Karma Theory, Determinism, Fatalism and Freedom of Will

The so-called theory of karma is one of the distinguishing aspects of Hinduism and other non-Hindu south-Asian traditions. At the same time that the theory can be seen as closely connected with the freedom of will and action that we humans supposedly have, it has many times been said to be determinist and fatalist. The purpose of this paper is to analyze in some deepness the relations that are between the theory of karma on one side and determinism, fatalism and free-will on the other side. In order to do that, I shall use what has been described as the best formal approach we have to indeterminism: branching time theory. More specifically, I shall introduce a branching time semantic framework in which, among other things, statements such as “state of affairs e is a karmic effect of agent a”, “a wills it to be the case that e” and “e is inevitable” could be properly represented