Simeon Coleman

International yield curve comovements: impact of the recent financial crisis

Empirical evidence on international yield comovement is sparse and lacks consensus. Employing a dynamic correlation approach, we show that during the recent global financial crisis, euro area yields have ceased to comove with the yields of the other international markets – Canada, UK and US. Some implications of our results are discussed.

Unified Knowledge Based Economy neural forecasting map

In todays troubled economies, nations are competing in many aspects, including innovation and knowledge progress. Even though there are many composite indicators to measure knowledge and innovation at both micro and macro levels, benefits to decision makers still limited due to numerous progress indicators, without any unified, easy to visualize and evaluate forecasting capabilities. This paper introduces a novel approach to forecasting and finding the aggregated position of many Knowledge-Based Economy (KBE) with a high degree of accuracy. The suggested approach is based on data mining, Neural Networks, Principle Component Analysis (PCA), and Self-Organising Map (SOM). The proposed model has the capability of forecasting and aggregating five major KBE indicators into a unified meaningful map that places any KBE in its league regardless of incomplete missing or little data. The Unified Knowledge Economy Forecast Map (UKFM) reflects the overall position of homogeneous knowledge economies, and it can be used to visualise, identify or evaluate stable, progressing or accelerating KBEs.

Unified knowledge economy competitiveness index using fuzzy clustering model

The aim of this paper is to design a unified knowledge economy competitiveness index using fuzzy clustering, to aggregate four of the most reputable and famous knowledge economy indicators into a unified index that reflects the overall rate of knowledge in an economy, to serve many purposes for the decision makers and foreign investors interested in such economy. The four selected indices are: Knowledge Economy Index (KEI) from World Bank, Information and Communication Technologies Development Index (IDI) from United Nations agency for information and communication technology issues (ITU), Global Competitiveness Index (GCI) from the World Economic Forum, and World Competitiveness Yearbook (WCY) from Institute for Management Development (IMD). To achieve this unified index, a four steps framework is proposed. The first step utilizes a Correlation analysis, the second step is to carry a Principle Component Analysis (PCA) analysis and the third step employs training an Adaptive Neural Fuzzy Inference Systems (ANFIS) and the forth step is to create a unified index based on all existing indices. The purpose of the first step was to test the relationship between the selected indices and how strong it is. The PCA is employed to test the similarity amongst existing indices and whether they can be reduced in any form. ANFIS was used to generate rules to create trained submodel that determine which of the input indices make efficient contribution to the new unified knowledge indicator. Then, the fuzzy c-means clustering technique is used to construct the new Unified Knowledge Competitiveness and Progress Indicator (UKPI) which combines the four selected aggregate indices into a new single meaningful index that reflects the overall rate of Knowledge competitiveness and progress in a nation.

An investigation of regime shifts in UK commercial property returns: a time series analysis

The random-walk hypothesis, vis-à-vis asset price, suggests that prices traded in a market cannot be predicted based on historical information. Employing unsecuritized UK commercial property returns, we analyse this hypothesis by investigating regime shifts or multiple changes in persistence in the series. Our results uncover regime shifts in both the aggregate and sector-specific data. Specifically, the shifts are less frequent in the Industrial sector, compared to the Office, Retail and Aggregate returns data. We highlight some implications for academics, practitioners and regulators.

Intelligent synthetic composite indicators with application

This article is proposing an alternative approach to develop Intelligent Synthetic Composite Indicators (ISCI). The suggested approach utilizes Fuzzy Proximity Knowledge Mining technique to build the qualitative taxonomy initially, and then Fuzzy c-means is employed to form the new composite indicators. A fully worked application is presented. The application uses Information and Communication Technology real variables to form a new unified ICT index, illustrating the method of construction for ISCI. The weighting and aggregation results obtained were compared against Principal Component Analysis, Factor Analysis and the Geometric mean to weight and aggregate synthetic composite indicators. This study also compares and contrasts two special Fuzzy c-means techniques that is, the Optimal Completion Strategy and the Nearest Prototype Strategy to impute missing values. The results are compared against statistical imputation techniques. The validity and robustness of all techniques are evaluated using Monte Carlo simulation. The results obtained suggest a novel, intelligent and non-biased method of building future composite indicators.

Towards developing 3rd Generation Intelligent Synthetic Composite Indicators

Synthetic Composite Indicators (SCIs) are assessment tools, usually constructed to evaluate and contrast entities performance, by aggregating abstract issues in many areas such as economy, education, technology and innovation. Most SCIs are built using statistical measures, but the jury is still out regarding their accuracy and effectiveness. This paper is proposing an alternative approach to build a new breed or 3rd Generation Intelligent Synthetic Composite Indicators (3G iSCi) based on computational intelligent methods. The suggested approach utilizes Fuzzy Proximity Knowledge Mining to build the qualitative taxonomy, and Fuzzy C-Means will be used to form the 3G iSCi. It is suggested to cluster related dataset for every single country and detect a cluster centre to act as the aggregated index for that country, which would identify natural homogeneously grouped data, allowing for concise representation of the relationships embedded within the variables. To illustrate, this research uses real variables and data to build a new Unified Intelligent ICT Index (U3I) based on the suggested soft computing methods. The results obtained so far are compared against Principal Components/Factor Analysis (PC/FA), and the Geometric Aggregation (GME), which are widely used statistical methods to weight and aggregate SCIs. The robustness of both techniques are evaluated using Monte Carlo simulation. The results obtained from this case study suggest a novel and intelligent way to build future synthetic composite indicators to better serve international organizations, public officials, decision makers and business leaders.