Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold

This paper employs a VAR-GARCH model to investigate the return links and volatility transmission between the S&P 500 and commodity price indices for energy, food, gold and beverages over the turbulent period from 2000 to 2011. Understanding the price behavior of commodity prices and the volatility transmission mechanism between these markets and the stock exchanges are crucial for each participant, including governments, traders, portfolio managers, consumers, and producers. For return and volatility spillover, the results show significant transmission among the S&P 500 and commodity markets. The past shocks and volatility of the S&P 500 strongly influenced the oil and gold markets. This study finds that the highest conditional correlations are between the S&P 500 and gold index and the S&P 500 and WTI index. We also analyze the optimal weights and hedge ratios for commodities/S&P 500 portfolio holdings using the estimates for each index. Overall, our findings illustrate several important implications for portfolio hedgers for making optimal portfolio allocations, engaging in risk management and forecasting future volatility in equity and commodity markets. © 2013 Elsevier B.V.

A hard slog, not a leap frog : globalization and sustainability transitions in developing Asia

How does globalization influence transitions toward more sustainable socio-technical regimes in the developing world? This paper argues that transformations of regimes, the networks and institutions governing technological and environmental practices in an industry, can be positively influenced by globalization but it depends on how global forces interact with local socio-political landscapes-the political-economic institutions, values, and regulations broadly guiding an economy and its relationship to the environment. We evaluate these relationships through a comparison of two kinds of socio-political landscapes-the neo-liberal export-led development model commonly found in the developing world and the uniquely Asian capitalist developmental state. We first show how the neo-liberal model overemphasizes the power of market forces to facilitate upgrading and more sustainable industrialization. We then argue that capitalist developmental states in East and Southeast Asia have been better able to harness global economic forces for technological and sustainability transitions through an openness to trade and investment and effective public-private institutions able to link cleaner technologies and environmental standards to production activities in firms. We buttress this argument with firm-level evidence showing the evolution of socio-technical regimes in two industries-cement and electronics. The case studies demonstrate how interactions with OECD firms can contribute to environmental technique effects provided the socio-political landscape is amenable to changes in an industry's regime. Ultimately, we find the process of transition to be complex and contingent; a hard slog not a leap frog toward a potentially more sustainable future. We close by considering the limitations on the capitalist developmental state model and with comments about what else needs to be learned about globalization's role in sustainability transitions.

A model predictive approach for community battery energy storage system optimization

This paper presents an efficient algorithm for optimizing the operation of battery storage in a low voltage distribution network with a high penetration of PV generation. A predictive control solution is presented that uses wavelet neural networks to predict the load and PV generation at hourly intervals for twelve hours into the future. The load and generation forecast, and the previous twelve hours of load and generation history, is used to assemble load profile. A diurnal charging profile can be compactly represented by a vector of Fourier coefficients allowing a direct search optimization algorithm to be applied. The optimal profile is updated hourly allowing the state of charge profile to respond to changing forecasts in load.

Male and female cyclist and driver perceptions of crash risk

Many drivers and non-cyclists perceive cycling as an extremely risky activity with women in particular being concerned about the risk of injury. The low rates of cycling participation by women pose a threat to the achievement of government targets for cycling participation and restrict the potential transport, health and environmental benefits that increased levels of cycling could provide. This study seeks to extend earlier research in gender and cycling by comparing the risks perceived by female and male cyclists and drivers in specific on-road situations while accounting for other potentially gender-related factors such as travel patterns and experience, perceived skill, and risk taking behaviors. In an online survey, 444 regular cyclists and 151 (non-cyclist) car drivers rated the level of risk in six situations: Failing to yield; Going through a red light; Not signaling when turning; Swerving; Tailgating; and Not checking traffic. The study found that the higher levels of risk perceived by women are not completely accounted for by differences in cycling patterns or perceptions of skill. Compared to their male counterparts, female cyclists and car drivers had similarly elevated perceptions of risk suggesting that these gender differences are not specific to cycling, but reflect wider differences in risk perception. Not all of the gender differences were consistent across cyclists and drivers. Higher levels of perceived skill were evident for male cyclists but not for male car drivers. Further research is needed to explore the robustness and interpretation of this finding.

Using internet search queries for infectious disease surveillance: screening diseases for suitability

Background Internet-based surveillance systems provide a novel approach to monitoring infectious diseases. Surveillance systems built on internet data are economically, logistically and epidemiologically appealing and have shown significant promise. The potential for these systems has increased with increased internet availability and shifts in health-related information seeking behaviour. This approach to monitoring infectious diseases has, however, only been applied to single or small groups of select diseases. This study aims to systematically investigate the potential for developing surveillance and early warning systems using internet search data, for a wide range of infectious diseases. Methods Official notifications for 64 infectious diseases in Australia were downloaded and correlated with frequencies for 164 internet search terms for the period 2009–13 using Spearman’s rank correlations. Time series cross correlations were performed to assess the potential for search terms to be used in construction of early warning systems. Results Notifications for 17 infectious diseases (26.6%) were found to be significantly correlated with a selected search term. The use of internet metrics as a means of surveillance has not previously been described for 12 (70.6%) of these diseases. The majority of diseases identified were vaccine-preventable, vector-borne or sexually transmissible; cross correlations, however, indicated that vector-borne and vaccine preventable diseases are best suited for development of early warning systems. Conclusions The findings of this study suggest that internet-based surveillance systems have broader applicability to monitoring infectious diseases than has previously been recognised. Furthermore, internet-based surveillance systems have a potential role in forecasting emerging infectious disease events, especially for vaccine-preventable and vector-borne diseases

Global environmental emissions estimate: Application of multiple imputation

A new database called the World Resource Table is constructed in this study. Missing values are known to produce complications when constructing global databases. This study provides a solution for applying multiple imputation techniques and estimates the global environmental Kuznets curve (EKC) for CO2, SO2, PM10, and BOD. Policy implications for each type of emission are derived based on the results of the EKC using WRI. Finally, we predicted the future emissions trend and regional share of CO2 emissions. We found that East Asia and South Asia will be increasing their emissions share while other major CO2 emitters will still produce large shares of the total global emissions.

Can social resilience inform SA/SIA for adaptive planning for climate change in vulnerable regions?

This book represents a landmark effort to probe and analyze the theory and empirics of designing water disaster management policies. It consists of seven chapters that examine, in-depth and comprehensively, issues that are central to crafting effective policies for water disaster management. The authors use historical surveys, institutional analysis, econometric investigations, empirical case studies, and conceptual-theoretical discussions to clarify and illuminate the complex policy process. The specific topics studied in this book include a review and analysis of key policy areas and research priority areas associated with water disaster management, community participation in disaster risk reduction, the economics and politics of ‘green’ flood control, probabilistic flood forecasting for flood risk management, polycentric governance and flood risk management, drought management with the aid of dynamic inter-generational preferences, and how social resilience can inform SA/SIA for adaptive planning for climate change in vulnerable areas. A unique feature of this book is its analysis of the causes and consequences of water disasters and efforts to address them successfully through policy-rich, cross-disciplinary and transnational papers. This book is designed to help enrich the sparse discourse on water disaster management policies and galvanize water professionals to craft creative solutions to tackle water disasters efficiently, equitably, and sustainably. This book should also be of considerable use to disaster management professionals, in general, and natural resource policy analysts.

Teaching scenario analysis – An action learning pedagogy

In this paper we provide an introduction to our teaching of scenario analysis. Scenario analysis offers an excellent instructional vehicle for investigating ‘wicked problems’; issues that are complex and ambiguous and require trans-disciplinary inquiry. We outline the pedagogical underpinning based on action learning and provide a critical approach from the intuitive logics school of scenario analysis. We use this in our programme in which student groups engage in semi-structured, but divergent and inclusive analysis of a selected focal issue. They then develop a set of scenario storylines that outline the limits of possibility and plausibility for a selected time-horizon year. The scenarios are portrayed not as narratives, but as vehicles for exploration of the causes and outcomes of the interplay between forces in the contextual environment that drive the unfolding future in the context of the focal issue. In this way, we provide internally-generated challenges to both individual pre-conceptions and group-level thinking.

On the benefits of equicorrelation for portfolio allocation

The importance of modelling correlation has long been recognised in the field of portfolio management, with largedimensional multivariate problems increasingly becoming the focus of research. This paper provides a straightforward and commonsense approach toward investigating a number of models used to generate forecasts of the correlation matrix for large-dimensional problems.We find evidence in favour of assuming equicorrelation across various portfolio sizes, particularly during times of crisis. During periods of market calm, however, the suitability of the constant conditional correlation model cannot be discounted, especially for large portfolios. A portfolio allocation problem is used to compare forecasting methods. The global minimum variance portfolio and Model Confidence Set are used to compare methods, while portfolio weight stability and relative economic value are also considered.

Adoption dynamics of increasing-return technologies in systemic contexts

Many systemic, complex technologies have been suggested to exhibit increasing returns to adoption, whereby the initial increase in adoption leads to increasing experience with the technology, which drives technological improvements and use, subsequently leading to further adoption. In addition, in the systemic context, mimetic behavior may lend support to increasing returns as technology adoption is witnessed among other agents in the systemic context. Finally, inter-dependencies in the systemic context also sensitize the adoption behavior to fundamental changes in technology provisioning, and this may lend support for the increasing returns type of dynamics in adoption. Our empirical study examines the dynamics of organizational technology adoption when technology is provisioned by organizations in another sub-system in a systemic context. We hypothesize that innovation, imitation, and technological change effects are present in creating increasing returns in the systemic context. Our empirical setting considers 24 technologies represented by 2282 data points in the computer industry. Our results provide support for our prediction that imitation effects are present in creating increasing returns to adoption. We further discuss the managerial and research implications of our results.

The role of context and motivation variables in mobile commerce usage: A further perspective on Chong (2013)

We comment on a recent article by Chong (2013) on the roles of demographic and motivation variables in mobile commerce usage. Drawing on the recent research on the service-dominant logic, socioemotional selectivity theory, and data from a first empirical study, we argue that a broader discussion on the value relevance of mobile commerce activities for customers and the consideration of consumers' future time perspectives would provide a richer, potentially more appropriate picture of the drivers of mobile commerce usage. Furthermore, using data from a second empirical study, we highlight several validity issues of the used scales. We hope to motivate a replication and extension of Chong's study and also provide recommendations for future research on this area.

Groundwater balance research to improve the accuracy of drawdown predictions in the Surat Basin, Queensland

Groundwater modelling studies rely on an accurate determination of inputs and outputs that make up the water balance. Often there is large uncertainty associated with estimates of recharge and unmetered groundwater use. This can translate to equivalent uncertainty in the forecasting of sustainable yields, impacts of extraction, and susceptibility of groundwater dependent ecosystems. In the case of Coal Seam Gas, it is important to characterise the temporal and special distribution of depressurisation in the reservoir and how this may or may not extend to the adjacent aquifers. A regional groundwater flow model has been developed by the Queensland Government to predict drawdown impacts due to Coal Seam Gas activities in the Surat basin. This groundwater model is undergoing continued refinement and there is currently scope to address some of the key areas of uncertainty including better quantification of groundwater recharge and unmetered groundwater extractions. Research is currently underway to improve the accuracy of estimates of both of these components of the groundwater balance in order to reduce uncertainty in predicted groundwater drawdowns due to CSG activities.

Modelling interregional links in electricity price spikes

Abnormally high price spikes in spot electricity markets represent a significant risk to market participants. As such, a literature has developed that focuses on forecasting the probability of such spike events, moving beyond simply forecasting the level of price. Many univariate time series models have been proposed to dealwith spikes within an individual market region. This paper is the first to develop a multivariate self-exciting point process model for dealing with price spikes across connected regions in the Australian National Electricity Market. The importance of the physical infrastructure connecting the regions on the transmission of spikes is examined. It is found that spikes are transmitted between the regions, and the size of spikes is influenced by the available transmission capacity. It is also found that improved risk estimates are obtained when inter-regional linkages are taken into account.