Is the forward bias economically small? Evidence from intra-erm rates.

For the purpose of testing uncovered interest parity (UIP), rates of European currencies against the DEM offer a distinct advantage: ERM membership or informal ERM association induces statistically significant mean-reversion in weekly rates. Thus, unlike for freely floating rates, there is an expectations signal that has nontrivial variation and is sufficiently traceable for research purposes. When running the standard regression tests of the unbiased-expectations hypothesis at the one-week horizon, we nevertheless obtain essentially zero coefficients for intra-EMS exchange rates (and the familiar negative coefficients for extra-EMS rates). Even more puzzlingly, lagged exchange rate changes remain significant when added to the regression, a feature that seems harder to explain as a missing-variable effect. The deviation from UIP is significant not just statistically but also economically: trading-rule tests reveal that for sufficiently large filters the average profit per trade exceeds transaction costs, and that cumulative gains can be quite impressive. The size of the profits and the patterns from buy versus sell decisions also allow us to reject the risk premium and the Peso hypotheses as separately sufficient explanations.Bias; Costs; Currency; Decision; EMS; ERM;

Fundamental remote sensing science research program. Part 1: Status report of the mathematical pattern recognition and image analysis project

The Mathematical Pattern Recognition and Image Analysis (MPRIA) Project is concerned with basic research problems related to the study of the Earth from remotely sensed measurement of its surface characteristics. The program goal is to better understand how to analyze the digital image that represents the spatial, spectral, and temporal arrangement of these measurements for purposing of making selected inference about the Earth

Anomaly Detection Based on Sensor Data in Petroleum Industry Applications

Anomaly detection is the problem of finding patterns in data that do not conform to an a priori expected behavior. This is related to the problem in which some samples are distant, in terms of a given metric, from the rest of the dataset, where these anomalous samples are indicated as outliers. Anomaly detection has recently attracted the attention of the research community, because of its relevance in real-world applications, like intrusion detection, fraud detection, fault detection and system health monitoring, among many others. Anomalies themselves can have a positive or negative nature, depending on their context and interpretation. However, in either case, it is important for decision makers to be able to detect them in order to take appropriate actions. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct or react to the situations associated with them. In that application context, heavy extraction machines for pumping and generation operations, like turbomachines, are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. In this paper, we propose a combination of yet another segmentation algorithm (YASA), a novel fast and high quality segmentation algorithm, with a one-class support vector machine approach for efficient anomaly detection in turbomachines. The proposal is meant for dealing with the aforementioned task and to cope with the lack of labeled training data. As a result, we perform a series of empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.This work was partially funded by the Brazilian National Council for Scientific and Technological Development projects CNPq BJT 407851/2012-7 and CNPq PVE 314017/2013-5 and projects MINECO TEC 2012-37832-C02-01, CICYT TEC 2011-28626-C02-02.Publicad