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The purpose of this paper is to highlight the profound learning associated with the Goethean methodology in the Holistic Science MSc at Schumacher College, Devon, UK. This is presented as a case study in profound pedagogy and as an exploration of the implications for workplace learning. Some comparisons are also made with reflective practice.

Background is provided on Goethe’s “way of science” and Barfield’s “participation”. Students were also interviewed about their learning and reflect on their experiences and challenges in learning the Goethean methodology, particularly regarding perceptions and participation, on their altered modes of thinking and feelings about learning, as well as on an “immediate”, corporeal and potentially co-operative mode of knowing in a “community of practice”, which can be extrapolated to the workplace.

The profoundness of the student experience and personal transformation presented in the interviews reveals that Goethean methodology has a place alongside the more specific analytical knowledge focus of universities. While the method has challenges in reconciling existing modes of knowing with the new approach, the students are able to see and intuit the wholeness and dynamism of phenomena more easily, and they gain a different perspective and learn to participate more fully in the world.

The paper asks that this template for educational practice be considered more widely relevant to today’s educational landscape in better providing skills and preparing students for the workplace in a world of “super-complexity”.

In this paper we develop a new semi-parametric model for conditional correlations, which combines parametric univariate Generalized Auto Regressive Conditional Heteroskedasticity specifications for the individual conditional volatilities with nonparametric kernel regression for the conditional correlations. This approach not only avoids the proliferation of parameters as the number of assets becomes large, which typically happens in conventional multivariate conditional volatility models, but also the rigid structure imposed by more parsimonious models, such as the dynamic conditional correlation model. An empirical application to the 30 Dow Jones stocks demonstrates that the model is able to capture interesting asymmetries in correlations and that it is competitive with standard parametric models in terms of constructing minimum variance portfolios and minimum tracking error portfolios.

Market share attraction models are useful tools for analyzing competitive structures. The models can be used to infer cross-effects of marketing-mix variables, but also the own effects can be adequately estimated while conditioning on competitive reactions. Important features of attraction models are that they incorporate that market shares sum to unity and that the market shares of individual brands ake in between 0 and 1. Next to analyzing competitive structures, attraction models are also often considered for forecasting market shares.

The econometric analysis of the market share attraction model has not received much attention. Topics as specification, diagnostics, estimation and forecasting have not been thoroughly discussed in the academic marketing literature. In this chapter we go through a range of these topics, and, along the lines, we indicate that there are ample opportunities to improve upon present-day practice. We also discuss an alternative approach to the log-centering method of linearizing the attraction model. This approach leads to easier inference and interpretation of the model.

This chapter develops a return forecasting methodology that allows for instability in the relationship between stock returns and predictor variables, model uncertainty, and parameter estimation uncertainty. The predictive regression specification that is put forward allows for occasional structural breaks of random magnitude in the regression parameters, uncertainty about the inclusion of forecasting variables, and uncertainty about parameter values by employing Bayesian model averaging. The implications of these three sources of uncertainty and their relative importance are investigated from an active investment management perspective. It is found that the economic value of incorporating all three sources of uncertainty is considerable. A typical investor would be willing to pay up to several hundreds of basis points annually to switch from a passive buy-and-hold strategy to an active strategy based on a return forecasting model that allows for model and parameter uncertainty as well as structural breaks in the regression parameters.

This study is intended to investigate the volatility patterns in Bombay Stock Exchange Limited Sensitivity Index (BSE Sensex) based on time series data collected for 10 years period of time. To reach out the predefined objectives of the study, the authors have employed generalized autoregressive conditional heteroscedastic models. The study revealed that the presence of heteroscedasticiy is found in BSE Sensex. Further, the model produced highly accurate results when the researchers compared the estimated results from actual. Furthermore, the volatility of BSE Sensex has shown the features of clustering and significant time varying. Moreover, the model has indicated that there is a positive correlation between daily stock returns and the BSE Sensex volatility.