Copenhagen Business School

About: Copenhagen Business School is a education organization based out in Copenhagen, Hovedstaden, Denmark. It is known for research contribution in the topics: Corporate governance & Entrepreneurship. The organization has 2194 authors who have published 9649 publications receiving 341898 citations.

Knowledge management: raising the spectre of the cross-cultural dimension.

Abstract: Practitioners and theorists of knowledge management are increasingly aware of the practice as an international activity, but the topic is seldom presented in its cross-cultural dimensions. This paper argues that knowledge management in the global economy is a form of cross-cultural management, but points out that the literature is vague on how to handle culture in its wider international manifestations. Among other things it is suggested that the division of knowledge into tacit and explicit may have limited applicability when knowledge is to be leveraged cross-culturally. Researchers are cautioned about making use of still pervasive concepts of culture which are out of keeping with the workings of the knowledge economy. It is concluded that the key task of knowledge management is to foster and continually sophisticate collaborative cross-cultural learning. But the point to bear in mind is that the essence of the cross-cultural challenge is not about what to learn from each other, but how to learn. Copyright © 2001 John Wiley & Sons, Ltd.

New actors and alliances in development

Abstract: ‘New actors and alliances in development’ brings together an interdisciplinary group of scholars exploring how development financing and interventions are being shaped by a wider and more complex platform of actors than usually considered in the existing literature The contributors also trace a changing set of key relations and alliances in development – those between business and consumers; ngos and celebrities; philanthropic organisations and the state; diaspora groups and transnational advocacy networks; ruling elites and productive capitalists; and ‘new donors’ and developing country governments Despite the diversity of these actors and alliances, several commonalities arise: they are often based on hybrid transnationalism and diffuse notions of development responsibility; rather than being new per se, they are newly being studied as practices that are now coming to be understood as ‘development’; and they are limited in their ability to act as agents of development by their lack of accountability o

Models for S&P 500 Dynamics: Evidence from Realized Volatility, Daily Returns, and Option Prices

Abstract: Most recent empirical option valuation studies build on the affine square root (SQR) stochastic volatility model. The SQR model is a convenient choice, because it yields closed-form solutions for option prices. However, relatively little is known about the resulting biases. We investigate alternatives to the SQR model, by comparing its empirical performance with that of five different but equally parsimonious stochastic volatility models. We provide empirical evidence from three different sources. We first use realized volatilities to assess the properties of the SQR model and to guide us in the search for alternative specifications. We then estimate the models using maximum likelihood on S&P 500 returns. Finally, we employ nonlinear least squares on a panel of option data. In comparison with earlier studies that explicitly solve the filtering problem, we analyze a more comprehensive option data set. The scope of our analysis is feasible because of our use of the particle filter. The three sources of data we employ all point to the same conclusion: the SQR model is misspecified. Overall, the best of the alternative volatility specifications is a model with linear rather than square root diffusion for variance which we refer to as the VAR model. This model captures the stylized facts in realized volatilities, it performs well in fitting various samples of index returns, and it has the lowest option implied volatility mean squared errors in- and out-of-sample.