Investors and investment managers also use index futures to hedge their positions against losses. In this study, we look at different metrics that are proposed in the literature that can be used to evaluate the registration agreement. We then test and compare their performance on synthetic datasets, pointing out some of their inadequacies. We explain why a permutation index initially proposed by Mielke7 may be considered the most appropriate after a small change, as it fulfills all the desired characteristics for such an index, including that of being interpretable in terms of correlation coefficient r. We also propose a more refined approach to separately examining non-systematic and systematic contributions to differences of opinion in the data set. Finally, we apply the available metrics and the proposed index to two cases of actual comparative studies: one refers to the time series of the standardized difference vegetation index (NDVI), which was acquired during the same period by two different satellite missions, and the other refers to two chronological series of gross primary production (GPP) estimated by different modeling approaches. Stock index futures are settled in cash, i.e. there is no delivery of the underlying at the end of the contract. If the price of the index is higher after the agreed price expires, the buyer made a profit, and the seller – the future author – suffered a loss.
If the opposite is true, the buyer suffers a loss and the seller makes a profit. The first case of study is satellite measurements of the Standardized Difference Vegetation Index (NDVI) obtained from October 1, 2013 to May 31, 2014 on Northwest Africa. The spatial resolution is 1 km and the temporal resolution is a decade (a decade is a period that results from the division of each calendar month into 3 parts, which can take values of 8, 9, 10 or 11 days). The data are obtained from two different instruments on two different satellite platforms: SPOT-VEGETATION and PROBA-V (these are called VT and PV for simplicity). PV data is available through the copernicus Global Land Service Portal24, while VT archive data is provided courtesy of the GFC MARSOP25 project. Although the geometric and spectral characteristics of satellites and data processing chains have been as close as possible, differences between products are still expected because the instruments are not identical. The aim here is to quantify where the time series do not coincide in the region. Since there is no reason to argue that one should be a better reference than the other, a symmetrical match index should be applied to each pair of time series, resulting in values that can be attributed geographically.
It`s Dimensionless. It is therefore independent of the unit of measurement. It makes it easier to compare the match between the different pairs of dataset (if each. B pair has different units) or within different parameter areas (for example. B for multi-variable datasets). Index symmetry is an important feature of the evaluation of the dataset agreement. Unlike validation or calibration exercises, where some model estimates are compared to reference values considered error-free (usually observations of the amount of interest), comparative studies may not be related. Since two sets of data compared have uncertainty, often unknown or poorly characterized, there is no “better” dataset than the other.