116,511 research outputs found
Author Co-Citation Analysis (ACA): a powerful tool for representing implicit knowledge of scholar knowledge workers
No full textIn the last decade, knowledge has emerged as one of the most important and valuable organizational assets. Gradually this importance caused to emergence of new discipline entitled ―knowledge management‖. However one of the major challenges of knowledge management is conversion implicit or tacit knowledge to explicit knowledge. Thus Making knowledge visible so that it can be better accessed, discussed, valued or generally managed is a long-standing objective in knowledge management. Accordingly in this paper author co- citation analysis (ACA) will be proposed as an efficient technique of knowledge visualization in academia (Scholar knowledge workers)
Modelling and analysing risk in precious metals
No full textThesis (Ph.D.(Statistics))--University of the Free State, 2018The prices of precious metals are volatile and financial market participants are interested in knowing the downside of holding precious metals in their portfolios. Risk management tools such as Value-at-Risk (VaR) are highly dependent on the underlying distributional assumption. Identifying a distribution that may best capture all the aspects of the given financial data can provide immense advantages to both investors and risk managers. In the analysis and modelling of financial returns, there are stylised facts that are observed. These include volatility clustering, heavy-tails, asymmetry, conditional heavy tails and long range dependence (long memory). In this study, we investigated the stylised facts of gold, platinum and silver returns. We thus propose models that are able to capture their empirical features. The models capture extreme tails of profit and loss distributions and improve the estimation of Value-at-Risk (VaR) of precious metal prices returns. Firstly, we evaluate the performance of existing heavy-tailed and flexible distributions in modelling extreme risk for precious metal returns. The heavy-tailed and flexible distributions used are: Generalised Hyperbolic Distributions (GHDs), Generalised Lambda Distribution (GLD), Stable Distribution (SD), Generalised Pareto Distribution (GPD), Generalised Extreme Value Distribution (GEVD), Pearson type-IV Distribution (PIVD), Symmetrical Student-t Distribution (STD) and Skewed Studentt Distribution (SSTD). Secondly, we couple ARMA-GARCH models and ARMAAPARCH models with heavy-tailed and flexible distributions. We fit the models to precious metal returns and evaluate their relative performance in estimating Valueat-Risk (VaR) using a number of conditional assumptions. The proposed models performed favourably when compared with the APARCH models with a Student-t distribution and the APARCH models with a skewed Student-t distribution usually used in the literature. This provides financial analysts with an alternative distributional scheme to be used in economic modelling. Thirdly, because all daily precious metal price returns exhibit volatility clustering, heavy tails, asymmetry and long range dependence, we fit the long-memory GARCH models under the GHDs, the GPD, the GEVD, the SD, the STD, the SSTD, the GLD and the PIVD assumptions to our price return data. The Anderson-Darling test is used to check for model adequacy. Kupiec likelihood ratio tests and Christoffersen conditional coverage tests are also used in this study to evaluate objectively whether VaR model is adequate. The backtesting results confirm that the long-memory GARCH-heavy-tailed models are adequate for improving risk management assessments and hedging strategies in the highly volatile metal markets. ARFIMA-HYGARCH, ARFIMA-FIGARCH and ARFIMA-FIAPARCH models with PIVD, Normal-Inverse Gaussian Distribution (NIGD), full GHD, FMKL GLD and Generalised Hyperbolic Student-t Distribution (GHStD) innovations are found to be suitable for VaR estimation of precious metals, thereby providing a good alternative candidate for modelling financial returns
Exploiting tacit knowledge through knowledge management technologies
No full textThe purpose of this paper is to examine the contributions and suitability of the available knowledge management (KM) technologies, including the Web 2.0 for exploiting tacit knowledge. It proposes an integrated framework for extracting tacit knowledge in organisations, which includes Web 2.0 technologies, KM tools, organisational learning (OL) and Community of Practice (CoP). It reviews a comprehensive literature covering overview of KM theories, KM technologies and OL and identifies the current state of knowledge relating to tacit knowledge exploitation. The outcomes of the paper indicate that Internet and Web 2.0 technologies have stunning prospects for creating learning communities where tacit knowledge can be extracted from people. The author recommends that organisations should design procedures and embed them in their Web 2.0 collaborative platforms persuading employees to record their ideas and share them with other members. It is also recommended that no idea should be taken for granted in a learning community where tacit knowledge exploitation is pursued. It is envisaged that future research should adopt empirical approach involving Complex Adaptive Model for Tacit Knowledge Exploitation (CAMTaKE) and the Theory of Deferred Action in examining the effectiveness of KM technologies including Web 2.0 tools for tacit knowledge exploitation
Visualizing latent domain knowledge
No full textKnowledge discovery and data mining commonly rely on finding salient patterns of association from a vast amount of data. Traditional citation analysis of scientific literature draws insights from strong citation patterns. Latent domain knowledge, in contrast to the mainstream domain knowledge, often consists of highly relevant but relatively infrequently cited scientific works. Visualizing latent domain knowledge presents a significant challenge to knowledge discovery and quantitative studies of science. We build upon a citation-based knowledge visualization procedure and develop an approach that not only captures knowledge structures from prominent and highly cited works, but also traces latent domain knowledge through low-frequency citation chains. We apply this approach to two cases: (1) identifying cross-domain applications of Pathfinder networks (PFNETs) and (2) clarifying the current status of scientific inquiry of a possible link between Bovine spongiform encephalopathy (BSE), also known as mad cow disease, and a new variant Creutzfeldt-Jakob disease (vCJD), a type of brain disease in human
Report to the Nation 2006
No full textForeword
As the National Knowledge Commission (NKC) presents its fi rst annual report to the nation, we feel a sense of excitement at the potential that India has to emerge as one of the leading knowledge societies in the world. The Commission was set up by Prime Minister Manmohan Singh to prepare a blueprint to tap into the enormous reservoir of our knowledge base so that our people can confidently face challenges of the 21st century. We are conscious that this is a daunting task, which requires not only resources and time but also a vision and a long term view. At the same time, we are happy that we have taken this fi rst important step. At the heart of the NKC’s mandate are fi ve key areas related to Access, Concepts, Creation, Application and Services. We have addressed the question of how to build a knowledge society from these perspectives with a particular focus on access to knowledge. Of the nine sets of recommendations made by the NKC in 2006, six deal directly with access. We have done so consciously in conformity with the UPA government’s philosophy of building an inclusive society. The emerging knowledge society and associated opportunities present a set of new imperatives and new challenges for our economy, polity and society. Our future prosperity depends upon the policies, programmes and people that can foster continuous generation and application of knowledge in the pursuit of learning. We have addressed a wide range of subjects including a comprehensive reform of higher education, overhaul of public libraries, creation of a Knowledge Network, setting up of national portals, transformation of vocational education, re-engineering of government processes and making E-governance citizen-friendly. The impact of what we have proposed would be felt over the next decade and beyond. We have taken particular care to keep the entire process democratic, transparent and participative. In doing so, we have consulted a wide range of stakeholders in government, parliament, politics, academia, industry, civil society and the media. Our recommendations refl ect and incorporate the concerns and aspirations of experts and persons from the concerned spheres. The Commission members have worked painstakingly on every aspect of our recommendations. I want to thank all members for the exceptional dedication they have brought to their mandate even though they all know that the impact of their work will be felt only in the long-term. We have had our agreements and disagreements on many issues on the table but their expression has always been in the highest traditions of democracy. I would also like to thank the members of various working groups and the secretariat for their contribution and support. I would like to particularly acknowledge the support and guidance of the Prime Minister’s Office and the Planning Commission.
We hope that the work we have done during our fi rst year will be of value to the government and will fi nd the enthusiasm and support of the administration in its implementation. We also hope that our recommendations will receive the attention they deserve and create necessary public discussion, debate and dialogue to shape and mobilize public opinion. We say this with a focus on the 550 million people below the age of 25 who will benefi t the most from the new knowledge initiatives. The destiny of India is in their hands. While making the recommendations we have been guided by how knowledge will impact the lives of people, ordinary people, of India. We are conscious that knowledge is about farmers having access to accurate information about water resources, land quality and fertilizers, students having access to schools and colleges of high quality relevant education and good jobs, scientists having access to well equipped modern libraries and laboratories, industry having access to a skilled workforce and people feeling empowered with good governance in a vibrant democracy. The recommendations of the National Knowledge Commission are really a call to action. It is time to act here and now
Estimating South Africa’s Growth Risk using GARCH-Type Models and Heavy-Tailed Distributions
No full textThe daily returns from financial market variables, such as stock indices, exhibit empirical distributions that are often heavy or semi-heavy or more Gaussian-like tailed. Estimating value-at-risk (VaR) and other risk measures such as conditional VaR (expected shortfall) depend highly on the distributional characteristics of the stock returns. The main objective of this study is to investigate the relative performance of the generalized hyperbolic skew Student-t and Pearson type-IV distributions governing the generalized autoregressive conditional heteroscedasticity (GARCH) innovations in estimation of the VaR for the daily returns from the FTSE/JSE growth index (J280). The results show that the ARMA(1,1)-EGARCH(1,1) model with a generalized hyperbolic skew Student-t distribution governing the innovations outperforms the competing models at estimating the VaR at a 95% level. Results also show that the ARMA(1,1)-EGARCH(1,1) model with a Pearson type-IV distribution governing the innovations outperforms the other competing models at estimating the VaR at all levels for the long position. This study recommends that the ARMA(1,1)-EGARCH(1,1) model with generalized hyperbolic skew Student-t and Pearson type-IV distributions be used in the modeling of daily returns from stock indices
Estimation of the value at risk using a long-memory GARCH application to JSE Indices.
No full textMasters Degree. University of KwaZulu-Natal, Durban.Financial data are characterized by stylized facts; this makes it difficult to model
financial assets if these stylized facts are not taken into account. Therefore, the implementation
of accurate risk management tools such as value at risk (VaR), which
is crucial in the management of market risk, becomes a futile exercise. This study
aims to compare the performance of the long-memory GARCH-type models with
heavy-tailed innovations in estimating the value at risk of the All Share Index, the
Mining Index, and the Banking Index. This was achieved by investigating the empirical
properties of the JSE Indices, fitting the FIGARCH, HYGARCH, and FIAPARCH
with the Student’s t-distribution (STD), skewed Student’s t-distribution (SSTD), and
generalized error distribution (GED). The study further estimates VaR for the short
and long-trading positions on the 95th, 99th, and 99,7th quantiles, as well as backtests
the results. The main findings indicate that the JSE All Share index returns is
best captured by the FIGARCH-SSTD model, whereas the JSE Mining Index retuns
most robust model is the FIAPARCH-STD model. For the JSE Banking Index returns,
the FIAPARCH-STD model is predominantly appropriate at most of different
VaR levels. The findings of the study provide a solution to both risk practitioners
and asset managers for better understanding the behaviour of the financial indices’
returns. Finally, this can assist the role players in fastidiously managing risks and
assets’ returns.Author's Dedication is on page iii of the thesis
Beginning teachers’ mathematical knowledge: What is needed?
No full textOver the past decade there has been growing interest in describing and measuring the kinds of mathematical knowledge needed by teachers. Such efforts are in parallel with the development of national standards for teachers, indicating levels of expectation across the years of teachers’ careers. This presentation provides an opportunity for teacher educators and teachers to consider the nature of mathematical knowledge needed by beginning teachers at all levels of schooling. Discussion will be informed by data from an ALTC funded national project that aims to improve the quality of pre-service teachers’ outcomes in mathematics and by the AAMT Standards framework
INSPEC database analysis for Knowledge Management records
No full textThe study deals with the Knowledge Management papers covered in the INSPEC, an international database on Information Science, Physical Sciences, Engineering and Computer Sciences. The papers have been analysed in terms of their content and other scientometric parameters
Measuring industry-science links through inventor-author relations: A profiling method
No full textIn this pilot study we examine the performance of text-based profiling in recovering a set of validated inventor-author links. In a first step we match patents and publications solely based on their similarity in content. Next, we compare inventor and author names on the highest ranked matches for the occurrence of name matches. Finally, we compare these candidate matches with the names listed in a validated set of inventor-author names. Our text-based profile methodology performs significantly better than a random matching of patents and publications, suggesting that text-based profiling is a valuable complementary tool to the name searches used in previous studies.innovation; industry-science links; text-based profiling;
- …
