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The aim of this paper is to access performance of existing computational techniques to model strongly non‐linear field diffusion problems.

Multidimensional application of a finite volume front‐fixing method to various front‐type problems with moving boundaries and non‐linear material properties is discussed. Advantages and implementation problems of the technique are highlighted by comparing the front‐fixing method with computations using fixed grids. Particular attention is focused on conservation properties of the algorithm and accurate solutions close to the moving boundaries. The algorithm is tested using analytical solutions of diffusion problems with cylindrical symmetry with both spatial and temporal accuracy analysed.

Several advantages are identified in using a front‐fixing method for modelling of impulse phenomena in high‐temperature superconductors (HTS), namely high accuracy can be obtained with a small number of grid points, and standard numerical methods for convection problems with diffusion can be utilised. Approximately, first order of spatial accuracy is found for all methods (stationary or mobile grids) for 2D problems with impulse events. Nevertheless, errors resulting from a front‐fixing technique are much smaller in comparison with fixed grids. Fractional steps method is proved to be an effective algorithm for solving the equations obtained. A symmetrisation procedure has to be introduced to eliminate a directional bias for a standard asymmetric split in diffusion processes.

This paper for the first time compares in detail advantages and implementation complications of a front‐fixing method when applied to the front‐type field diffusion problems common to HTS. Particular attention is paid to accurate solutions in the region close to the moving front where rapid changes in material properties are responsible for large computational errors.

The purpose of this paper is to investigate a reliable evaluator of arc re-ignition and to develop a numerical tool for accurate prediction of arc behaviour of low-voltage switching devices (LVSDs) prior to empirical laboratory testing of real products.

Two types of interruption tests have been carried out in the investigation of re-ignition evaluators. Arc modelling tool coupled with the load circuit has been developed to predict arc characteristics based on conventional magnetohydrodynamics theory, with special attention given to Lorentz force acting on the arc column and surface phenomena on the splitter plate. The model assumptions have been validated by experimental observation of arc motion and current and voltage waveforms.

It is found that the exit-voltage across the switching device and the ratio of system to exit-voltage at the current zero point are reliable evaluators for prediction of re-ignition. Where the voltage ratio is positive, instantaneous re-ignition does not occur. Further, the probability of re-ignition is very low if the voltage ratio is in the rage of −1.3 to 0.

It is observed that the voltage ratio can be considered as a reliable global evaluator of re-ignition, which can be used for various types of LVSD test conditions. In addition, it is shown that arc modelling allows a good prediction of the current and voltage waveforms, arc motion as well as the exit-voltage, which can be used to obtain the evaluator of re-ignition.

The purpose of this paper is to access performance of existing computational techniques to model strongly non‐linear coupled thermo‐electric problems.

A thermistor is studied as an example of a strongly non‐linear diffusion problem. The temperature field and the current flow in the device are mutually coupled via ohmic heating and very rapid variations of electric conductivity with temperature and applied electric field, which makes the problem an ideal test case for the computational techniques. The finite volume fully coupled and fractional steps (splitting) approaches on a fixed computational grid are compared with a fully coupled front‐fixing method. The algorithms' input parameters are verified by comparison with published experiments.

It was found that fully coupled methods are more effective for non‐linear diffusion problems. The front fixing provides additional improvements in terms of accuracy and computational cost.

This paper for the first time compares in detail advantages and implementation complications of each method being applied to the coupled thermo‐electric problems. Particular attention is paid to conservation properties of the algorithms and accurate solutions in the transition region with rapid changes in material properties.

The purpose of this paper is twofold: to improve the traditional conditional autoregressive Wishart (CAW) and heterogeneous autoregressive (HAR)-CAW model to account for heterogeneous leverage effect and to adjust the high-frequency volatility. The other is to confirm whether CAW-type models that have statistical advantages have economic advantages.

Based on the high-frequency data, this study proposed a new model to describe the volatility process according to the heterogeneous market hypothesis. Thus, the authors acquire needed and credible high-frequency data.

By designing two mean-variance frameworks and considering several economic performance measures, the authors find that compared with five other models based on daily data, CAW-type models, especially LHAR-CAW and HAR-CAW, indeed generate the substantial economic values, and matrix adjustment method significantly improves the three CAW-type performances.

The findings in this study suggest that from the aspect of economics, LHAR-CAW model can more accurately built the dynamic process of return rates and covariance matrix, respectively, and the matrix adjustment can reduce bias of realized volatility as covariance matrix estimator of return rates, and greatly improves the performance of unadjusted CAW-type models.

Compared with traditional low-frequency models, investors should allocate assets according to the LHAR-CAW model so as to get more economic values.

This study proposes LHAR-CAW model with the matrix adjustment, to account for heterogeneous leverage effect and empirically show their economic advantage. The new model and the new bias adjustment approach are pioneering and promote the evolution of financial econometrics based on high-frequency data.

The purpose of this paper is to investigate and explain the unexpected current flow patterns and twisting equipotential surfaces observed in strongly anisotropic materials.

Potential distributions and current flow paths in highly anisotropic composite materials were studied via numerical simulation and experimentally. Simplified composite panels with two plyes were analysed using a finite‐element model; the predictions were then confirmed experimentally.

The unexpected twisting equipotential surfaces and current flow patterns were found to be consistent with minimising of Joule heat release in the material. Numerical modelling suggests that the twisted profiles of the potential are highly sensitive to the anisotropic electrical conductivity.

This paper discusses the reverse current flows witnessed in a two‐layer anisotropic system. Such behaviour has never been predicted or observed experimentally before. The reported results will be of interest to anyone who is considering using anisotropic materials such as carbon fibre composites which might experience applied potential difference, such as lightning strikes.

The purpose of this paper is to examine the effect of GST announcements (pre and post) on Malaysian stock market index. This study also utilised intraday data to look into intraday market volatility post-GST announcement.

Both daily closing prices and intraday data of different frequencies are used to capture the extent of stock market volatility as well as the subsided period of the volatility. The period of study ranges from June 2009 to November 2016 and empirical estimation is based on the GARCH (1, 1) model for the pre- and post-GST announcements.

Persistent market volatility in the post-GST announcement is empirically recorded and the volatility is higher in the post-GST announcement than the pre-GST announcement. This demonstrates the unwillingness and reaction of the market towards the tax policy implementation. Market expectation on GST implementation towards the increase in the cost of living following the increase in the prices of goods and services in Malaysia is empirically supported in the post-GST announcement.

The finding on this study is consistent with the expectation of the market that GST implementation will increase the price of the goods and services and hence increase the cost of living. This is supported by a noticeable increase in the stock market volatility in the post-GST announcement. Although GST announcement could be classified as a scheduled announcement, unwillingness to accept the policy prevails as shown by the increase in the stock market volatility.

The effects of Asian and global financial crisis are the major focus of past studies on stock market volatility, whereas this study examines and highlights the effect of the GST announcement on stock market volatility and the use of intraday data to further examine the nature of the volatility.

This work investigates the volatility spillovers across stock markets and the nature of such spillovers through different periods of crises and tranquility.

Using daily stock return volatility data from June 2003 to June 2021, the generalized forecast error variance decomposition method (based on Diebold and Yilmaz, 2012 approach) is employed to measure the degree of volatility spillovers/connectedness among stock markets of 24 Asia–Pacific and 12 European Union (EU) economies.

The empirical results from static analysis suggested that about 28.1% (63.7%) of forecast error variance in return volatility for Asia–Pacific (EU) markets is due to spillovers. The evidence from dynamic analysis suggested that during mid of the global financial crisis, European debt crisis (EDC) and Covid-19, the gross volatility spillovers for Asia–Pacific (EU) was around 67% (80%), 65% (80%) and 73% (67%), respectively. The degree of net volatility transmission from Singapore (Denmark) to other Asia–Pacific (EU) markets was found to be highest.

The findings have crucial implications for the investors and portfolio managers in assessment of risk and optimum allocation of assets and investment decisions.

This study adds to the literature on risk management by systematically examining the impact of global financial crises, EDC and Covid-19 on the market interactions by capturing the magnitude, duration and pattern of the shock-specific market volatilities for a large sample of Asian and European markets using recent and large data set.

With growing interdependence between financial markets, the goal of this paper is to examine the dynamic interdependence between corporate equity and public real estate markets for the USA and a select group of seven European developed economies under a cross-country framework in crisis and boom market conditions. Dynamic interdependence is related to four measures of market linkages of “correlation, spillover, connectedness and causality”.

This study adopts a four-step investigation. The authors first estimate “time-varying variance–covariance spillovers and implied correlations” modeled with the bivariate BEKK-MGARCH methods. Second, the methods of Diebold and Yilmaz (2012, 2014) measure the conditional volatility spillover-connectedness effects across the corporate equity and public real estate markets based on a decomposition of the forecast error variance. Third, the authors implement nonlinear bivariate and multivariate causality tests to understand the lead-lag dynamics of the two asset markets' returns, volatilities and net directional volatility connectedness across different sample periods. Finally, the authors conclude the study by providing a portfolio hedging analysis.

The authors find that corporate equity and public real estate are moderately interdependent to the extent that their diversification benefits increases in the longer term. Moreover, the authors find increased corporate equity-public real estate causal dependence of the market groups of the European and international portfolios during the GFC and INTERCRISIS periods. The nonlinear causality test findings indicate that the joint information of asset markets can be a useful source of prediction for future innovation of market risks. Additionally, policy makers may also be able to employ conditional volatility and volatility connectedness as two other measures to manage market stability in the cross-asset market dependence during highly volatile periods.

One major take away from this academic research is since international portfolio investors are not only concerned the long-term price relationship but also the correlation structure and volatility spillover-connectedness, the conditional BEKK modeling, generalized risk connectedness analysis and nonlinear causal dependence explorations from this multi-country study can shed fresh light on the nature of market interdependence and magnitude of volatility connectedness effects in a multi-portfolio framework.

The hedging performance analysis for portfolio diversification and risk management indicates that industrial stocks (“pure” equities) are valuable assets that can improve the hedging performance of a well-diversified corporate equity-public real estate portfolio during crisis periods. For policymakers, the findings provide important information about the nature of causal links and predictability during the crisis and asset-market boom periods. They can then equip with this information to manage and coordinate market stability in cross corporate equity-real estate relationships effectively.

Although traditional research has in general reported at least a moderate degree of relationship between the two asset markets, investors' knowledge of stock-public real estate market linkage is somewhat inadequate and confine mostly to broad stocks (i.e. stocks that are exposed to public real estate influence) in a single-country context. In this paper, the authors examine the interdependence dynamics in a multi-country (multi-portfolio) context. A clear understanding their changing market relationships in a multi-country context is of crucial importance for portfolio investors, financial institutions and policy makers. Moreover, since the authors use an orthogonal stock market index, the authors allow global investors to understand the potential diversification benefits from stock markets that are beyond the public real estate market under different market conditions.

Scenario planning has significant applications in the field of strategic management and facilitating decision making under uncertainty, and hence this study aims to integrate scenario planning and the preference ranking organisation method for enrichment evaluations (PROMETHEE) method to propose a new methodology to design a portfolio.

The methodology has been designed in two stages, the first of which identifies the investment environment in Iran and defines possible scenarios for the future based upon the opinion of experts and uncertainties established in the identified environment. In the second stage, the views of experts are elicited on business area performance within each scenario. The business areas are subsequently ranked based on their final performance scores in each scenario area using the PROMETHEE method. Through use of a linear programming model, the percentage of investment in each business area is then determined according to the net flow of the area (i.e. the priority of a certain area relative to others).

The portfolio design in an Iranian investment company has been considered as a case study. The building industry and cement industry have been selected as preferable strategic business areas based on hypothetical scenarios for investment environment in Iran over five years (2008‐2012) and the strategies of the investment company.

The integration of scenario planning and multi‐criteria decision analysis (MCDA) holds great potential for strategic decision support due to the complementary nature of these two approaches. In this article, the authors provide a methodology to apply scenario planning alongside other MCDA methods to portfolio design, focusing on an innovative use of the PROMETHEE method with scenario planning.

The purpose of this paper is to develop an approach that can detect abnormal deviations in the time series models for technology forecasting. The detected modifications provide a basis for understanding the determinants and impact of the corresponding change.

The proposed approach is based on monitoring residual values (the difference between the observation and the forecasted value) continuously using statistical control charts (SCCs). The residuals that are out of the expected limits are considered an alert indicating a remarkable change. To demonstrate the use of the proposed approach, a time series model was fitted to a number of TV-related patent counts. Subsequently, model residuals were used to determine the limits of the SCCs.

A number of patents granted in the year 2012 violated the upper control limit. A further analysis has shown that there is a linkage between the abnormal patent counts and the emergence of LCD TVs.

Change in technology may dramatically affect the accuracy of a forecasting model. The need for a parameter update indicates a significant change (emergence or death of a technology) in the technological environment. This may lead to the revision of managerial actions in R&D plans and investment decisions.

The proposed methodology brings a novel approach for abnormal data detection and provides a basis for understanding the determinants and impact of the corresponding change.