br Experimental design materials and methods br Acknowledgme

Experimental design, materials and methods
Acknowledgments We are grateful for funding from ESRC under Grant reference no ES/K004824/1. We are also grateful for the services of the Secure Data Service support team.
Specifications Table Value of the data Data The dataset consists of cross-sectional observations on 155 countries that received their initial legal tradition exogenously mainly via colonization or occupation, i.e., transplants. For this sample, I report in the excel file “OIL_W” the lawmaking and adjudication institutions at independence and in 2000, the proxies for the determinants of their evolution I discuss in [12], and both the continuous measure of legal traditions and a measure of social welfare I employ in [13]. While the lawmaking institution determines the identity of the lawmaker – i.e., the government, the legislature, or the president under statute law and appellate judges under case law, adjudication institutions modulate the discretion allowed by the legal system to lower adjudicating courts [5,10,20,22]. The drivers of the evolution of legal traditions are the extent of cultural GSK1070916 and the quality of the political process.
Experimental design, materials and methods
Acknowledgments
Data We describe in detail data sets used for calibration trials in [1]. This includes graphical depictions of option prices in Maturity/Strike plane, see Figs. 1 and 2. A crucial part of any calibration trial is the problem formulation. The following data sets are obtained for a weighted least squares problem formulation, see [1]. Hence, all the upcoming data depends on the weights which we include in separate spreadsheets, see the Supplementary materials. The data obtained from optimization routines is depicted by figures in Supplementary materials for all sets of weights and both considered models.
Experimental design, materials and methods In Figs. 1 and 2 each traded option price is represented by a circle which is centered corresponding to the strike price and maturity of the contract. A circle diameter is proportionate to the weighted option premium. Black dashed line represents 100% moneyness. Figures also depict an in-sample (blue disc) and out-of-sample set (red circle). Further data were obtained from specific calibration routines, models and problem formulation. The formulation differed only with respect to weights, all employed weight sets (for all contracts) are included in the supplementary spreadsheets. Considered models were the following: the popular Heston stochastic volatility model and the newly introduced approximative fractional volatility model (FSV). Three global optimizers were considered for the calibration task, Genetic Algorithm (GA), Simulated Annealing (SA) and Adaptive Simulated Annealing (ASA). As a local search method a trust region reflective method (LSQ) was used. The most interesting approach in [1], however, was a combination of global methods and the local one. The calibration output data corresponding to these combined methods are visually shown in Supplementary materials.
Acknowledgements This work was supported by the GACR Grant 14-11559S Analysis of Fractional Stochastic Volatility Models and their Grid Implementation. Computational resources were provided by the CESNET LM2015042 and the CERIT Scientific Cloud LM2015085, provided under the programme \"Projects of Large Research, Development, and Innovations Infrastructures\".
Experimental design, materials and methods The data is made available in PDF format by the Sri Lanka Tourism Development Authority. The data description and transformation are discussed in Kumar and Stauvermann [1]. (http://www.sltda.lk/statistics).
Data We provide weekly returns time series for assets and indexes belonging to several major stock markets across the world. Weekly returns data are computed from prices values obtained from Thomson Reuters Datastream (http://financial.thomsonreuters.com/) and from daily returns obtained from Fama & French Data Library (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html). The data are filtered to check and to correct missing or inaccurate values. The data provided can be used as input for several types of portfolio selection models to compare on both efficiency and performance (for references on portfolio selection approaches see, e.g., [3]). For the above datasets, we also include as benchmarks the portfolios obtained by using several selection strategies based on both exact and approximate Stochastic Dominance models (described in [2]).