Combining ethics and compliance: A systems psychodynamic inquiry into praxis and outcomes

Notwithstanding empirical evidence that combining ethics and compliance into one function or programme is likely to erode an ethical culture in organisations, the praxis to coalesce ethics and compliance conceptually and structurally remains attractive and continues in many organisations. Drawing on systems psychodynamic theory, this study enquires into unconscious complexities and drivers that may contribute to adverse consequences when combining ethics and compliance. Whereas previous research demonstrates that compliance dominates ethics because of self-regulation issues, this research provides deeper insight into why this happens. On explicating the unconscious dynamics that underlie the apparent lure of combination, it becomes clear that this praxis remains attractive because of its function to repress anxieties, maintain phantasies, and provide psychological comfort. In recognising the role of unconscious dynamics in the decision to combine ethics and compliance and the consequences thereof, cognition of this combination and its outcomes is moved beyond a conscious rational process. This elucidates the commanding opposing unconscious forces potentially at play in this decision. Insights could lessen adverse outcomes by prompting organisations to think of the combination of ethics and compliance as unconsciously motivated systemic processes that require sincere consideration before pursuing them.     

Estimation in generalized bivariate Birnbaum–Saunders models

In this paper, we propose two moment-type estimation methods for the parameters of the generalized bivariate Birnbaum–Saunders distribution by taking advantage of some properties of the distribution. The proposed moment-type estimators are easy to compute and always exist uniquely. We derive the asymptotic distributions of these estimators and carry out a simulation study to evaluate the performance of all these estimators. The probability coverages of confidence intervals are also discussed. Finally, two examples are used to illustrate the proposed methods.     

Addressing the paradox – the divergence between smallholders’ preference and actual adoption of agricultural innovations

Experiences in smallholder contexts indicate frequent mismatches between technologies introduced and needs of farmers who must make complex decisions in reallocating their limited resources under highly risky ecological and market contexts. This study proposes a cost- and time-effective, easy-to-implement approach to identify farmers’ priorities and critical intervention areas, and presents its application in guiding an agroforestry strategy in Rwanda. It was found that different tree species have distinctive enabling vs. constraining conditions under different agroecological contexts in the perspective of smallholder farmers. Tree species preferred by farmers were not necessarily widely adopted if multitudes of conditions were not enabling. The essential conditions for sustainable adoption include: quality materials/inputs are available; technologies are compatible with existing local farming systems; they are resilient to climate risks/resistant to pests-diseases; management is not complicated; and, there is guaranteed access to markets. The results show that there will not be a silver bullet national strategy to scale up agroforestry. Instead a matrix kind of strategies -to promote enabling conditions and address constraining conditions for priority species in specific agroecologies- will be required. The proposed concept should be further refined for wider agricultural technology transfer debates to break the myths of low uptakes by smallholders.     

Asset volatility with prospect theory investors

A realized volatility measure reflecting prospect theory investors' sentiments is empirically seen to improve volatility forecast accuracy     

Forecasting realized variance measures using time-varying coefficient models

This paper considers the problem of forecasting realized variance measures. These measures are highly persistent estimates of the underlying integrated variance, but are also noisy. Bollerslev, Patton and Quaedvlieg (2016), Journal of Econometrics 192(1), 1–18 exploited this so as to extend the commonly used heterogeneous autoregressive (HAR) by letting the model parameters vary over time depending on the estimated measurement error variances. We propose an alternative specification that allows the autoregressive parameters of HAR models to be driven by a latent Gaussian autoregressive process that may also depend on the estimated measurement error variance. The model parameters are estimated by maximum likelihood using the Kalman filter. Our empirical analysis considers the realized variances of 40 stocks from the S&P 500. Our model based on log variances shows the best overall performance and generates superior forecasts both in terms of a range of different loss functions and for various subsamples of the forecasting period.