scholarly journals An Evaluation of Alternative Equity Indices - Part 1: Heuristic and Optimised Weighting Schemes

2013 ◽  
Author(s):  
Andrew D. Clare ◽  
Nick E. Motson ◽  
Steve H. Thomas
Keyword(s):  
Weighting Schemes ◽  
Equity Indices

A Hybrid Model for Equity Indices and Stochastic Interest Rates

2012 ◽  
Author(s):  
Jan F. Baldeaux ◽  
Man Chung Fung ◽  
Katja Ignatieva ◽  
Eckhard Platen
Keyword(s):  
Interest Rates ◽  
Hybrid Model ◽  
Stochastic Interest Rates ◽  
Stochastic Interest ◽  
Equity Indices

scholarly journals The Integration of Conventional Equity Indices with Environmental, Social, and Governance Indices: Evidence from Emerging Economies

2021 ◽  
Vol 13 (2) ◽  
pp. 676
Author(s):  
Ramiz ur Rehman ◽  
Muhammad Zain ul Abidin ◽  
Rizwan Ali ◽  
Safwan Mohd Nor ◽  
Muhammad Akram Naseem ◽  
...  
Keyword(s):  
South Africa ◽  
Function Analysis ◽  
Impulse Response Function ◽  
Decomposition Analysis ◽  
Individual Country ◽  
Brics Countries ◽  
Long Run ◽  
Integration Test ◽  
Short Run ◽  
Equity Indices

This study investigates the integration of environmental, social, and governance (ESG) equity indices with conventional indices in Brazil, Russia, India, China, and South Africa (BRICS) individually and across all BRICS countries to better understand regional economic cooperation. Accordingly, we look at daily returns from 13 July 2013 to 28 February 2018 for the Morgan Stanley Capital International (MSCI) ESG indices and MSCI composite indices of the respective countries. To analyze the integration between the ESG equity indices of the sampled countries with their regional and across regional conventional counterparts, the Johansen Co-integration test is employed in this study. Further, the vector error correction model (VECM) is applied to test the causality between the sampled time-series. The impulse response function analysis further explains the impulse responses of each country’s MSCI ESG returns to one standard deviation of innovations to MSCI composite returns of the same country and across countries. Finally, the extent of the MSCI composite returns’ impact on the MSCI ESG returns in the same country indices, and cross-regional indices is examined with variance decomposition analysis. The results suggest that all ESG equity indices are integrated with conventional indices in all BRICS countries. Furthermore, there is a short-or long-run causality between MSCI ESG and MSCI composite equity indices of China and South Africa. Moreover, the study finds only short-run causality between conventional and non-conventional equity indices of Brazil and Russia, whereas we find only long-run causality between India’s non-conventional and conventional equity indices. Finally, the study finds that the all-individual country MSCI ESG equity indices shows a long-run causality with MSCI composite equity indices of all other BRICS countries. The findings also confirm the economic and financial cooperation between the BRICS countries.

Climate Index Weighting Schemes for NWS ESP-Based Seasonal Volume Forecasts

2004 ◽  
Vol 5 (6) ◽  
pp. 1076-1090 ◽  
Author(s):  
Kevin Werner ◽  
David Brandon ◽  
Martyn Clark ◽  
Subhrendu Gangopadhyay
Keyword(s):  
Nearest Neighbor ◽  
Southern Oscillation ◽  
Climate Index ◽  
Climate Information ◽  
Streamflow Prediction ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Weighting Schemes ◽  
Temperature And Precipitation ◽  
Spring Runoff

Abstract This study compares methods to incorporate climate information into the National Weather Service River Forecast System (NWSRFS). Three small-to-medium river subbasins following roughly along a longitude in the Colorado River basin with different El Niño–Southern Oscillation signals were chosen as test basins. Historical ensemble forecasts of the spring runoff for each basin were generated using modeled hydrologic states and historical precipitation and temperature observations using the Ensemble Streamflow Prediction (ESP) component of the NWSRFS. Two general methods for using a climate index (e.g., Niño-3.4) are presented. The first method, post-ESP, uses the climate index to weight ensemble members from ESP. Four different post-ESP weighting schemes are presented. The second method, preadjustment, uses the climate index to modify the temperature and precipitation ensembles used in ESP. Two preadjustment methods are presented. This study shows the distance-sensitive nearest-neighbor post-ESP to be superior to the other post-ESP weighting schemes. Further, for the basins studied, forecasts based on post-ESP techniques outperformed those based on preadjustment techniques.

scholarly journals A new family of dissimilarity metrics for discrete character matrices that include inapplicable characters and its importance for disparity studies

2018 ◽  
Vol 285 (1892) ◽  
pp. 20181784 ◽  
Author(s):  
Melanie J. Hopkins ◽  
Katherine St John
Keyword(s):  
Phylogenetic Analysis ◽  
Missing Data ◽  
Rank Order ◽  
Software Tools ◽  
Weighting Schemes ◽  
New Family ◽  
Discrete Character ◽  
Secondary Characters

The use of discrete character data for disparity analyses has become more popular, partially due to the recognition that character data describe variation at large taxonomic scales, as well as the increasing availability of both character matrices co-opted from phylogenetic analysis and software tools. As taxonomic scope increases, the need to describe variation leads to some characters that may describe traits not found across all the taxa. In such situations, it is common practice to treat inapplicable characters as missing data when calculating dissimilarity matrices for disparity studies. For commonly used dissimilarity metrics like Wills's GED and Gower's coefficient, this can lead to the reranking of pairwise dissimilarities, resulting in taxa that share more primary character states being assigned larger dissimilarity values than taxa that share fewer. We introduce a family of metrics that proportionally weight primary characters according to the secondary characters that describe them, effectively eliminating this problem, and compare their performance to common dissimilarity metrics and previously proposed weighting schemes. When applied to empirical datasets, we confirm that choice of dissimilarity metric frequently affects the rank order of pairwise distances, differentially influencing downstream macroevolutionary inferences.

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