Are equity market daily price indices and returns in the major european markets european markets cointegrated? Tests and evidence.

This paper examines the relationship between the stock market price indices and index returns in three major European equity markets, FTSE100 (U.K), DAX (Germany), and CAC40 (France). Our results, obtained using a vector autoregressive (VAR) model, indicate that while the price indices of the three markets are cointegrated, returns on the indices are not cointegrated. These findings are not in agreement with the earlier studies that reported cointegration in the returns of the three indices. The sample period for the earlier studies ranges from mid-1980s to mid-1990s. Our results, using the sample period from late 1990 to early 2002, clearly indicate that there is no long run equilibrium relationship between the index returns on the three markets, although the three price indices are cointegrated.Unit root, stationarity, co-integration

A mass-balance/photochemical assessment of DMS sea-to-air flux as inferred from NASA GTE PEM-West a and B observations

This study reports dimethyl sulfide (DMS) sea-to-air fluxes derived from a mass-balance/photochemical-modeling approach. The region investigated was the western North Pacific covering the latitude range of 0°-30°N. Two NASA airborne databases were used in this study: PEM-West A in September-October 1991 and PEM-West B in February-March 1994. A total of 35 boundary layer (BL) sampling runs were recorded between the two programs. However, after filtering these data for pollution impacts and DMS lifetime considerations, this total was reduced to 13. Input for each analysis consisted of atmospheric DMS measurements, the equivalent mixing depth (EMD) for DMS, and model estimated values for OH and NO3. The evaluation of the EMD took into account both DMS within the BL as well as that transported into the overlying atmospheric buffer layer (BuL). DMS fluxes ranged from 0.6 to 3.0 μmol m-2d-1 for PEM-West A (10 sample runs) and 1.4 to 1.9 μmol m-2d-1 for PEM-West B (3 sample runs). Sensitivity analyses showed that the photochemically evaluated DMS flux was most influenced by the DMS vertical profile and the diel profile for OH. A propagation of error analysis revealed that the uncertainty associated with individual flux determinations ranged from a factor of 1.3 to 1.5. Also assessed were potential systematic errors. The first of these relates to our noninclusion of large-scale mean vertical motion as it might appear in the form of atmospheric subsidence or as a convergence. Our estimates here would place this error in the range of O to 30%. By far the largest systematic error is that associated with stochastic events (e.g., those involving major changes in cloud coverage). In the latter case, sensitivity tests suggested that the error could be as high as a factor of 2. With improvements in such areas as BL sampling time, direct observations of OH, improved DMS vertical profiling, direct assessment of vertical velocity in the field, and preflight (24 hours) detailed meteorological data, it appears that the uncertainty in this approach could be reduced to ±25%. Copyright 1999 by the American Geophysical Union

Empirical evidence on the relationships between concentration and profitability in Latin American banking

There has been growth in globalization as a result of increased liberalization. This has also resulted in an increase in the role of financial institutions, such as banks. It is the purpose of this study to test Classen’s (2001) hypothesis that increase foreign bank presence has positive welfare implications and that the functioning of national banking markets are improved as a result. Using financial data for 2003 this paper will examine the influence of foreign bank entry on Latin American domestic markets.Latin American banking; Concentration and Profitability; Foreign Bank Influence

Global estimation of burned area using MODIS active fire observations

We present a method for estimating monthly burned area globally at 1° spatial resolution using Terra MODIS data and ancillary vegetation cover information. Using regression trees constructed for 14 different global regions, MODIS active fire observations were calibrated to burned area estimates derived from 500-m MODIS imagery based on the assumption that burned area is proportional to counts of fire pixels. Unlike earlier methods, we allow the constant of proportionality to vary as a function of tree and herbaceous vegetation cover, and the mean size of monthly cumulative fire-pixel clusters. In areas undergoing active deforestation, we implemented a subsequent correction based on tree cover information and a simple measure of fire persistence. Regions showing good agreement between predicted and observed burned area included Boreal Asia, Central Asia, Europe, and Temperate North America, where the estimates produced by the regression trees were relatively accurate and precise. Poorest agreement was found for southern-hemisphere South America, where predicted values of burned area are both inaccurate and imprecise; this is most likely a consequence of multiple factors that include extremely persistent cloud cover, and lower quality of the 500-m burned area maps used for calibration. Application of our approach to the nine remaining regions yielded comparatively accurate, but less precise, estimates of monthly burned area. We applied the regional regression trees to the entire archive of Terra MODIS fire data to produce a monthly global burned area data set spanning late 2000 through mid-2005. Annual totals derived from this approach showed good agreement with independent annual estimates available for nine Canadian provinces, the United States, and Russia. With our data set we estimate the global annual burned area for the years 2001-2004 to vary between 2.97 million and 3.74 million km², with the maximum occurring in 2001. These coarse-resolution burned area estimates may serve as a useful interim product until long-term burned area data sets from multiple sensors and retrieval approaches become available

Top-down estimate of a large source of atmospheric carbon monoxide associated with fuel combustion in Asia

We simulate the oceanic and atmospheric distribution of methyl iodide (CH3I) with a global 3-D model driven by assimilated meteorological observations from the Goddard Earth Observing System of the NASA Data Assimilation Office and coupled to an oceanic mixed layer model. A global compilation of atmospheric and oceanic observations is used to constrain and evaluate the simulation. Seawater CH3I(aq) in the model is produced photochemically from dissolved organic carbon, and is removed by reaction with Cl− and emission to the atmosphere. The net oceanic emission to the atmosphere is 214 Gg yr−1. Small terrestrial emissions from rice paddies, wetlands, and biomass burning are also included in the model. The model captures 40% of the variance in the observed seawater CH3I(aq) concentrations. Simulated concentrations at midlatitudes in summer are too high, perhaps because of a missing biological sink of CH3I(aq). We define a marine convection index (MCI) as the ratio of upper tropospheric (8–12 km) to lower tropospheric (0–2.5 km) CH3I concentrations averaged over coherent oceanic regions. The MCI in the observations ranges from 0.11 over strongly subsiding regions (southeastern subtropical Pacific) to 0.40 over strongly upwelling regions (western equatorial Pacific). The model reproduces the observed MCI with no significant global bias (offset of only +11%) but accounts for only 15% of its spatial and seasonal variance. The MCI can be used to test marine convection in global models, complementing the use of radon-222 as a test of continental convection.Engineering and Applied Science

Analysis of Next-generation Sequencing Data in Virology - Opportunities and Challenges

Viruses are the most abundant and the smallest organisms, which are relatively simple to sequence. Genome sequence data of viruses for individual species to populations outnumber that of other species. Although this offers an opportunity to study viral diversity at varying levels of taxonomic hierarchy, it also poses challenges for systematic and structured organization of data and its downstream processing. Extensive computational analyses using a number of algorithms and programs have opened exciting opportunities for virus discovery and diagnostics, apart from augmenting our understanding of the intriguing world of viruses. Unravelling evolutionary dynamics of viruses permits improved understanding of phenomena such as quasispecies diversity, role of mutations in host switching and drug resistance, which enables the tangible measurements of genotype and phenotype of viruses. Improved understanding of geno-/serotype diversity in correlation with antigenic diversity will facilitate rational design and development of efficacious vaccines against emerging and re-emerging viruses. Mathematical models developed using the genomic data could be used to predict the spread of viruses due to vector switching and the (re)emergence due to host switching and, thereby, contribute towards designing public health policies for disease management and control

Time-dependent inversion estimates of global biomass-burning CO emissions using Measurement of Pollution in the Troposphere (MOPITT) measurements

We present an inverse-modeling analysis of CO emissions using column CO retrievals from the Measurement of Pollution in the Troposphere (MOPITT) instrument and a global chemical transport model (GEOS-CHEM). We first focus on the information content of MOPITT CO column retrievals in terms of constraining CO emissions associated with biomass burning and fossil fuel/biofuel use. Our analysis shows that seasonal variation of biomass-burning CO emissions in Africa, South America, and Southeast Asia can be characterized using monthly mean MOPITT CO columns. For the fossil fuel/biofuel source category the derived monthly mean emission estimates are noisy even when the error statistics are accurately known, precluding a characterization of seasonal variations of regional CO emissions for this source category. The derived estimate of CO emissions from biomass burning in southern Africa during the June-July 2000 period is significantly higher than the prior estimate (prior, 34 Tg; posterior, 13 Tg). We also estimate that emissions are higher relative to the prior estimate in northern Africa during December 2000 to January 2001 and lower relative to the prior estimate in Central America and Oceania/Indonesia during April-May and September-October 2000, respectively. While these adjustments provide better agreement of the model with MOPITT CO column fields and with independent measurements of surface CO from National Oceanic and Atmospheric Administration Climate Monitoring and Diagnostics Laboratory at background sites in the Northern Hemisphere, some systematic differences between modeled and measured CO fields persist, including model overestimation of background surface CO in the Southern Hemisphere. Characterizing and accounting for underlying biases in the measurement model system are needed to improve the robustness of the top-down estimate. Copyright 2006 by the American Geophysical Union

Estimates of fire emissions from an active deforestation region in the southern Amazon based on satellite data and biogeochemical modelling

Tropical deforestation contributes to the build-up of atmospheric carbon dioxide in the atmosphere. Within the deforestation process, fire is frequently used to eliminate biomass in preparation for agricultural use. Quantifying these deforestation-induced fire emissions represents a challenge, and current estimates are only available at coarse spatial resolution with large uncertainty. Here we developed a biogeochemical model using remote sensing observations of plant productivity, fire activity, and deforestation rates to estimate emissions for the Brazilian state of Mato Grosso during 2001–2005. Our model of DEforestation CArbon Fluxes (DECAF) runs at 250-m spatial resolution with a monthly time step to capture spatial and temporal heterogeneity in fire dynamics in our study area within the ''arc of deforestation'', the southern and eastern fringe of the Amazon tropical forest where agricultural expansion is most concentrated. Fire emissions estimates from our modelling framework were on average 90 Tg C year<sup>−1</sup>, mostly stemming from fires associated with deforestation (74%) with smaller contributions from fires from conversions of Cerrado or pastures to cropland (19%) and pasture fires (7%). In terms of carbon dynamics, about 80% of the aboveground living biomass and litter was combusted when forests were converted to pasture, and 89% when converted to cropland because of the highly mechanized nature of the deforestation process in Mato Grosso. The trajectory of land use change from forest to other land uses often takes more than one year, and part of the biomass that was not burned in the dry season following deforestation burned in consecutive years. This led to a partial decoupling of annual deforestation rates and fire emissions, and lowered interannual variability in fire emissions. Interannual variability in the region was somewhat dampened as well because annual emissions from fires following deforestation and from maintenance fires did not covary, although the effect was small due to the minor contribution of maintenance fires. Our results demonstrate how the DECAF model can be used to model deforestation fire emissions at relatively high spatial and temporal resolutions. Detailed model output is suitable for policy applications concerned with annual emissions estimates distributed among post-clearing land uses and science applications in combination with atmospheric emissions modelling to provide constrained global deforestation fire emissions estimates. DECAF currently estimates emissions from fire; future efforts can incorporate other aspects of net carbon emissions from deforestation including soil respiration and regrowth

Top-down estimates of global CO sources using MOPITT measurements

We present a synthesis inversion of CO emissions from various geographical regions and for various source categories for the year 2000 using CO retrievals from the MOPITT (Measurements of Pollution in the Troposphere) instrument. We find a large discrepancy between our top‐down estimates and recent bottom‐up estimates of CO emissions from fossil fuel/biofuel (FFBF) use in Asia. A key conclusion of this study is that CO emissions in East Asia (EAS) are about a factor of 1.8–2 higher than recent bottom‐up estimates