Estimating the parameters of stochastic volatility models using option price data

This article describes a maximum likelihood method for estimating the parameters of the standard square-root stochastic volatility model and a variant of the model that includes jumps in equity prices. The model is fitted to data on the S&P 500 Index and the prices of vanilla options written on the index, for the period 1990 to 2011. The method is able to estimate both the parameters of the physical measure (associated with the index) and the parameters of the risk-neutral measure (associated with the options), including the volatility and jump risk premia. The estimation is implemented using a particle filter whose efficacy is demonstrated under simulation. The computational load of this estimation method, which previously has been prohibitive, is managed by the effective use of parallel computing using graphics processing units (GPUs). The empirical results indicate that the parameters of the models are reliably estimated and consistent with values reported in previous work. In particular, both the volatility risk premium and the jump risk premium are found to be significant.

The case for “n«all”: Why the Big Data revolution will probably happen differently in the mining sector

Big Data and predictive analytics have received significant attention from the media and academic literature throughout the past few years, and it is likely that these emerging technologies will materially impact the mining sector. This short communication argues, however, that these technological forces will probably unfold differently in the mining industry than they have in many other sectors because of significant differences in the marginal cost of data capture and storage. To this end, we offer a brief overview of what Big Data and predictive analytics are, and explain how they are bringing about changes in a broad range of sectors. We discuss the “N=all” approach to data collection being promoted by many consultants and technology vendors in the marketplace but, by considering the economic and technical realities of data acquisition and storage, we then explain why a “n « all” data collection strategy probably makes more sense for the mining sector. Finally, towards shaping the industry’s policies with regards to technology-related investments in this area, we conclude by putting forward a conceptual model for leveraging Big Data tools and analytical techniques that is a more appropriate fit for the mining sector.

Efficient parameter estimation in longitudinal data analysis using a hybrid GEE method

The method of generalized estimating equations (GEEs) provides consistent estimates of the regression parameters in a marginal regression model for longitudinal data, even when the working correlation model is misspecified (Liang and Zeger, 1986). However, the efficiency of a GEE estimate can be seriously affected by the choice of the working correlation model. This study addresses this problem by proposing a hybrid method that combines multiple GEEs based on different working correlation models, using the empirical likelihood method (Qin and Lawless, 1994). Analyses show that this hybrid method is more efficient than a GEE using a misspecified working correlation model. Furthermore, if one of the working correlation structures correctly models the within-subject correlations, then this hybrid method provides the most efficient parameter estimates. In simulations, the hybrid method's finite-sample performance is superior to a GEE under any of the commonly used working correlation models and is almost fully efficient in all scenarios studied. The hybrid method is illustrated using data from a longitudinal study of the respiratory infection rates in 275 Indonesian children.

Multifaceted Modelling of Complex Business Enterprises

We formalise and present a new generic multifaceted complex system approach for modelling complex business enterprises. Our method has a strong focus on integrating the various data types available in an enterprise which represent the diverse perspectives of various stakeholders. We explain the challenges faced and define a novel approach to converting diverse data types into usable Bayesian probability forms. The data types that can be integrated include historic data, survey data, and management planning data, expert knowledge and incomplete data. The structural complexities of the complex system modelling process, based on various decision contexts, are also explained along with a solution. This new application of complex system models as a management tool for decision making is demonstrated using a railway transport case study. The case study demonstrates how the new approach can be utilised to develop a customised decision support model for a specific enterprise. Various decision scenarios are also provided to illustrate the versatility of the decision model at different phases of enterprise operations such as planning and control.

Emotional intelligence development in radiation therapy students: A longitudinal study

Purpose: Emotional intelligence (EI) is an increasingly important aspect of a health professional’s skill set. It is strongly associated with empathy, reflection and resilience; all key aspects of radiotherapy practice. Previous work in other disciplines has formed contradictory conclusions concerning development of EI over time. This study aimed to determine the extent to which EI can develop during a radiotherapy undergraduate course and identify factors affecting this. Methods and materials: This study used anonymous coded Likert-style surveys to gather longitudinal data from radiotherapy students relating to a range of self-perceived EI traits during their 3-year degree. Data were gathered at various points throughout the course from the whole cohort. Results: A total of 26 students provided data with 14 completing the full series of datasets. There was a 17·2% increase in self-reported EI score with a p-value<0·0001. Social awareness and relationship skills exhibited the greatest increase in scores compared with self-awareness. Variance of scores decreased over time; there was a reduced change in EI for mature students who tended to have higher initial scores. EI increase was most evident immediately after clinical placements. Conclusions: Radiotherapy students increase their EI scores during a 3-year course. Students reported higher levels of EI immediately after their clinical placement; radiotherapy curricula should seek to maximise on these learning opportunities.

Wisdom and narcissism as predictors of transformational leadership

- Purpose Although leadership and organizational scholars have suggested that the virtue of wisdom may promote outstanding leadership behavior, this proposition has rarely been empirically tested. The purpose of this paper is to investigate the relationships between transformational leadership, narcissism, and five dimensions of wisdom as conceptualized by the well-established Berlin wisdom paradigm. General mental ability and emotional intelligence were considered relevant control variables. - Design/methodology/approach Interview, test, and questionnaire data were obtained from 77 employees of a high school and from two or three colleagues of each employee. Data were analyzed using hierarchical regression analyses. - Findings After controlling for general mental ability and emotional intelligence, narcissism and the wisdom dimension relativism of values and life priorities were negatively related to transformational leadership, and the wisdom dimension recognition and management of uncertainty was positively related to transformational leadership. The other three wisdom dimensions, rich factual knowledge about life, rich procedural knowledge about life, and lifespan contextualism, were not significantly related to transformational leadership. - Research limitations/implications Limitations to be addressed in future studies include the cross-sectional design and the relatively small and specialized sample. - Practical implications Tentative implications for leadership training and development are outlined. - Originality/value This multi-method and multi-source study represents the first empirical investigation that examines links between well-established wisdom and leadership constructs in the work context.

Leaders' personal wisdom and leader-member exchange quality: The role of individualized consideration

Business scholars have recently proposed that the virtue of personal wisdom may predict leadership behaviors and the quality of leader–follower relationships. This study investigated relationships among leaders’ personal wisdom—defined as the integration of advanced cognitive, reflective, and affective personality characteristics (Ardelt, Hum Dev 47:257–285, 2004)—transformational leadership behaviors, and leader–member exchange (LMX) quality. It was hypothesized that leaders’ personal wisdom positively predicts LMX quality and that intellectual stimulation and individualized consideration, two dimensions of transformational leadership, mediate this relationship. Data came from 75 religious leaders and 1–3 employees of each leader (N = 158). Results showed that leaders’ personal wisdom had a positive indirect effect on follower ratings of LMX quality through individualized consideration, even after controlling for Big Five personality traits, emotional intelligence, and narcissism. In contrast, intellectual stimulation and the other two dimensions of transformational leadership (idealized influence and inspirational motivation) did not mediate the positive relationship between leaders’ personal wisdom and LMX quality. Implications for future research on personal wisdom and leadership are discussed, and some tentative suggestions for leadership development are outlined.

Stepping into my shoes: Generativity as a mediator of the relationship between business owners' age and family succession

The authors investigated generativity – the concern in establishing and guiding the next generation – as a mediator of the relationship between family business owners' age and succession in family businesses. Data came from 155 family business owners in Germany from different industries between the ages of 26 and 83 years. Results showed that age was positively related to generativity, and that generativity, in turn, positively influenced an objective measure of family succession. Generativity fully mediated the positive relationship between age and family succession. The findings suggest that generativity is an important psycho-social construct for understanding ageing, careers and succession in family business settings.

Focus on opportunities as a mediator of the relationship between business owners' age and venture growth

Combining upper echelons and lifespan theories, we investigated the mediating effect of focus on opportunities on the negative relationship between business owners' age and venture growth. We also expected that mental health moderates the negative relationship between business owners' age and focus on opportunities. Path analytic findings based on data from 84 business owners (mean age = 44, range 24-74) supported these hypotheses. Findings suggest that focus on opportunities is a psychological mechanism that links business owners' age with venture growth. Our findings also indicate that mental health helps maintain a high level of focus on opportunities with increasing age.

Online learning an effective means for developing small business owner-managers’ capabilities?

Purpose If owner-managers engage in management development activities then chances of success may be improved for small businesses. But small business owner-managers (SBOMs) are a difficult group to engage in management development activities. While practitioners worry about timing, content and location of development activities, the purpose of this paper is to examine what drives SBOMs to participate in an online discussion forum (ODF) as a form of management development. An ODF was run with SBOMs and the factors affecting their participation are reported from this exploratory study. Design/methodology/approach A qualitative methodology was used where data gathered from three sources, the ODF posts, in-depth interviews with participants and a focus group with non-participants. These were analysed to evaluate factors affecting participation of SBOMs in an ODF. Findings The findings point to the importance of owner-managers’ attitudes. Attitudes that positively affected SBOMs participation in the ODF included; appreciating that learning leads to business success; positive self-efficacy developed through prior online experience; and an occupational identity as a business manager. Research limitations/implications Few SBOMs participated in the ODF, which is consistent with research finding that they are a difficult group to engage in management development learning activities. Three forms of data were analysed to strengthen results. Practical implications Caution should be exercised when considering investment in e-learning to develop the managerial capabilities of SBOMs. Originality/value Evidence of the factors important for participation in an informal voluntary ODF. The findings suggest greater emphasis should be placed on changing attitudes if SBOMs are to be encouraged to participate in management development activities.

Using Big Data to manage safety-related risk in the upstream oil and gas industry: A research agenda

Despite considerable effort and a broad range of new approaches to safety management over the years, the upstream oil & gas industry has been frustrated by the sector’s stubbornly high rate of injuries and fatalities. This short communication points out, however, that the industry may be in a position to make considerable progress by applying “Big Data” analytical tools to the large volumes of safety-related data that have been collected by these organizations. Toward making this case, we examine existing safety-related information management practices in the upstream oil & gas industry, and specifically note that data in this sector often tends to be highly customized, difficult to analyze using conventional quantitative tools, and frequently ignored. We then contend that the application of new Big Data kinds of analytical techniques could potentially reveal patterns and trends that have been hidden or unknown thus far, and argue that these tools could help the upstream oil & gas sector to improve its injury and fatality statistics. Finally, we offer a research agenda toward accelerating the rate at which Big Data and new analytical capabilities could play a material role in helping the industry to improve its health and safety performance.

Changes in CO2 emissions over business cycle recessions and expansions in the United States: A decomposition analysis

This paper examines the asymmetry of changes in CO2 emissions over business cycle recessions and expansions using yearly data from 1949 and monthly data from 1973 for the United States (US). In addition, decomposition analysis is applied to investigate the relative roles of various proximate contributing factors to observed changes in total and per capita CO2 emissions and emissions intensity, over business cycle phases. The results suggest, inter alia, that aggregate emissions and emissions intensity reduce much faster in contractions than they increase in expansions. In addition, unlike the three previous expansions, in the most recent post-GFC US expansion, emissions per capita have continued to decline, and at a rate very similar to the rate of reduction in preceding contractions. This suggests the real possibility that the most recent contraction may have had an ongoing impact on the path of per capita emissions well beyond the immediate impact experienced during the contraction itself.

Cloud computing services for the small and medium enterprises: Key considerations for sourcing sustainable business value

The concept of cloud computing services (CCS) is appealing to small and medium enterprises (SMEs). However, while there is a significant push by various authorities on SMEs to adopt the CCS, knowledge of the key considerations to adopt the CCS is very limited. We use the technology-organization-environment (TOE) framework to suggest that a strategic and incremental intent, understanding the organizational structure and culture, understanding the external factors, and consideration of the human resource capacity can contribute to sustainable business value from CCS. Using survey data, we find evidence of a positive association between these considerations and the CCS-related business objectives. We also find evidence of positive association between the CCS-related business objectives and CCS-related financial objectives. The results suggest that the proposed considerations can ensure sustainable business value from the CCS. This study provides guidance to SMEs on a path to adopting the CCS with the intention of a long-term commitment and achieving sustainable business value from these services.

Unmanned Aerial Vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.

An eroding social justice agenda: The case of physical education and health edu-business in schools

In this paper we draw on current research to explore notions of a socially just Health and Physical Education (HPE), in light of claims that a neoliberal globalisation promotes markets over the states, and a new individualism that privileges self-interest over the collective good. We also invite readers to consider United Nations Educational, Scientific and Cultural Organization’s ambition for PE in light of preliminary findings from an Australian led research project exploring national and international patterns of outsourcing HPE curricula. Data were sourced from this international research project through a mixed method approach. Each external provider engaged in four phases of research activity: (a) Web-audits, (b) Interviews with external providers, (c) Network diagrams, and (d) School partner interviews and observations. Results We use these data to pose what we believe to be three emerging lines of inquiry and challenge for a socially just school HPE within neoliberal times. In particular our data indicates that the marketization of school HPE is strengthening an emphasis on individual responsibility for personal health, elevating expectations that schools and teachers will “fill the welfare gap” and finally, influencing the nature and purchase of educative HPE programs in schools. The apparent proliferation of external providers of health work, HPE resources and services reflects the rise and pervasiveness of neoliberalism in education. We conclude that this global HPE landscape warrants attention to investigate the extent to which external providers’ resources are compatible with schooling’s educative and inclusive mandates.