Bio-folio: applying portfolio theory to biodiversity

Genes, species and ecosystems are often considered to be assets. The need to ensure a sufficient diversity of this asset is being increasingly recognised today. Asset managers in banks and insurance companies face a similar challenge. They are asked to manage the assets of their investors by constructing efficient portfolios. They deliberately make use of a phenomenon observed in the formation of portfolios: returns are additive, while risks diversify. This phenomenon and its implications are at the heart of portfolio theory. Portfolio theory, like few other economic theories, has dramatically transformed the practical work of banks and insurance companies. Before portfolio theory was developed about 50 years ago, asset managers were confronted with a situation similar to the situation the research on biodiversity faces today. While the need for diversification was generally accepted, a concept that linked risk and return on a portfolio level and showed the value of diversification was missing. Portfolio theory has closed this gap. This article first explains the fundamentals of portfolio theory and transfers it to biodiversity. A large part of this article is then dedicated to some of the implications portfolio theory has for the valuation and management of biodiversity. The last section introduces three development openings for further research.

Structural Breaks in Mortality Models and their Consequences

In recent years, the issue of life expectancy has become of upmost importance to pension providers, insurance companies and the government bodies in the developed world. Significant and consistent improvements in mortality rates and, hence, life expectancy have led to unprecedented increases in the cost of providing for older ages. This has resulted in an explosion of stochastic mortality models forecasting trends in mortality data in order to anticipate future life expectancy and, hence, quantify the costs of providing for future aging populations. Many stochastic models of mortality rates identify linear trends in mortality rates by time, age and cohort, and forecast these trends into the future using standard statistical methods. The modeling approaches used failed to capture the effects of any structural change in the trend and, thus, potentially produced incorrect forecasts of future mortality rates. In this paper, we look at a range of leading stochastic models of mortality and test for structural breaks in the trend time series.

Identifying Structural Breaks in Stochastic Mortality Models

In recent years, the issue of life expectancy has become of utmost importance to pension providers, insurance companies, and government bodies in the developed world. Significant and consistent improvements in mortality rates and hence life expectancy have led to unprecedented increases in the cost of providing for older ages. This has resulted in an explosion of stochastic mortality models forecasting trends in mortality data to anticipate future life expectancy and hence quantify the costs of providing for future aging populations. Many stochastic models of mortality rates identify linear trends in mortality rates by time, age, and cohort and forecast these trends into the future by using standard statistical methods. These approaches rely on the assumption that structural breaks in the trend do not exist or do not have a significant impact on the mortality forecasts. Recent literature has started to question this assumption. In this paper, we carry out a comprehensive investigation of the presence or of structural breaks in a selection of leading mortality models. We find that structural breaks are present in the majority of cases. In particular, we find that allowing for structural break, where present, improves the forecast result significantly.

Modelling mortality: Are we heading in the right direction?

Predicting life expectancy has become of upmost importance in society. Pension providers, insurance companies, government bodies and individuals in the developed world have a vested interest in understanding how long people will live for. This desire to better understand life expectancy has resulted in an explosion of stochastic mortality models many of which identify linear trends in mortality rates by time. In making use of such models for forecasting purposes we rely on the assumption that the direction of the linear trend (determined from the data used for fitting purposes) will not change in the future, recent literature has started to question this assumption. In this paper we carry out a comprehensive investigation of these types of models using male and female data from 30 countries and using the theory of structural breaks to identify changes in the extracted trends by time. We find that structural breaks are present in a substantial number of cases, that they are more prevalent in male data than in female data, that the introduction of additional period factors into the model reduces their presence, and that allowing for changes in the trend improves the fit and forecast substantially.

Ownership Patterns and Control in Turkish Listed Companies

The purpose of this paper is to document the prevalent ownership concentration, structure and control in the top 100 companies listed on the Istanbul Stock Exchange. The results are discussed in the context of emerging corporate governance trends in Turkey. Where appropriate, comparisons with other countries are provided. The results of the study indicate that ownership of Turkish companies is highly concentrated, families being the dominant shareholders. The separation of ownership and control among Turkish companies is mainly achieved through pyramidal ownership structures and the presence of big business groups. However, the cash flow and voting rights in Turkish companies are relatively more aligned compared to other family–ownership–dominated insider–system countries.

How Does the Unemployment Insurance System Shape the Time Profile of Jobless Duration?

This paper updates a sparse literature on the effects of unemployment benefits (UB) on the time profile of escape rates from unemployment. These effects, as well as those of other regressors, are found to vary profoundly over the course of unemployment.

The Cost of Sustainability Capital and the Creation of Sustainable Value by Companies

We develop and apply a valuation methodology to calculate the cost of sustainability capital, and, eventually, sustainable value creation of companies. Sustainable development posits that decisions must take into account all forms of capital rather than just economic capital. We develop a methodology that allows calculation of the costs that are associated with the use of different forms of capital. Our methodology borrows the idea from financial economics that the return on capital has to cover the cost of capital. Capital costs are determined as opportunity costs, that is, the forgone returns that would have been created by alternative investments. We apply and extend the logic of opportunity costs to the valuation not only of economic capital but also of other forms of capital. This allows (a) integrated analysis of use of different forms of capital based on a value-based aggregation of different forms of capital, (b) determination of the opportunity cost of a bundle of different forms of capital used in a company, called cost of sustainability capital, (c) calculation of sustainability efficiency of companies, and (d) calculation of sustainable value creation, that is, the value above the cost of sustainability capital. By expanding the well-established logic of the valuation of economic capital in financial markets to cover other forms of capital, we provide a methodology that allows determination of the most efficient allocation of sustainability capital for sustainable value creation in companies. We demonstrate the practicability of the methodology by the valuation of the sustainability performance of British Petroleum (BP).

Sustainable Value Creation among Companies in the Manufacturing Sector

In this paper, we present empirical results of a study on the creation of Sustainable Value among European manufacturing companies. As sustainable development is a future oriented concept we assess the use of environmental resources in companies in the light of the EU15 performance targets for 2010. By using the Sustainable Value approach and based on publicly available company data we measure in monetary terms how individual companies perform vis-a-vis the 2010 performance targets already today. This shows the specific exposure and vulnerability of companies to more stringent policy regimes, and allows meaningful comparisons between both companies and sectors.