Causation, counterfactuals and competitive advantage

Causation is still poorly understood in strategy research, and confusion prevails around key concepts such as competitive advantage. In this paper, we define epistemological conditions that help to dispel some of this confusion and to provide a basis for more developed approaches. In particular, we argue that a counterfactual approach – that builds on a systematic analysis of ‘what-if’ questions – can advance our understanding of key causal mechanisms in strategy research. We offer two concrete methodologies – counterfactual history and causal modeling – as useful solutions. We also show that these methodologies open up new avenues in research on competitive advantage. Counterfactual history can add to our understanding of the context-specific construction of resource-based competitive advantage and path dependence, and causal modeling can help to reconceptualize the relationships between resources and performance. In particular, resource properties can be regarded as mediating mechanisms in these causal relationships.

Causation, counterfactuals and competitive advantage

Causation is still poorly understood in strategy research, and confusion prevails around key concepts such as competitive advantage. In this paper, we define epistemological conditions that help to dispel some of this confusion and to provide a basis for more developed approaches. In particular, we argue that a counterfactual approach – that builds on a systematic analysis of ‘what-if’ questions – can advance our understanding of key causal mechanisms in strategy research. We offer two concrete methodologies – counterfactual history and causal modeling – as useful solutions. We also show that these methodologies open up new avenues in research on competitive advantage. Counterfactual history can add to our understanding of the context-specific construction of resource-based competitive advantage and path dependence, and causal modeling can help to reconceptualize the relationships between resources and performance. In particular, resource properties can be regarded as mediating mechanisms in these causal relationships.

Essays on the Modeling and Prediction of Volatility and Higher Moments of Stock Returns (summary section only)

One of the most fundamental and widely accepted ideas in finance is that investors are compensated through higher returns for taking on non-diversifiable risk. Hence the quantification, modeling and prediction of risk have been, and still are one of the most prolific research areas in financial economics. It was recognized early on that there are predictable patterns in the variance of speculative prices. Later research has shown that there may also be systematic variation in the skewness and kurtosis of financial returns. Lacking in the literature so far, is an out-of-sample forecast evaluation of the potential benefits of these new more complicated models with time-varying higher moments. Such an evaluation is the topic of this dissertation. Essay 1 investigates the forecast performance of the GARCH (1,1) model when estimated with 9 different error distributions on Standard and Poor’s 500 Index Future returns. By utilizing the theory of realized variance to construct an appropriate ex post measure of variance from intra-day data it is shown that allowing for a leptokurtic error distribution leads to significant improvements in variance forecasts compared to using the normal distribution. This result holds for daily, weekly as well as monthly forecast horizons. It is also found that allowing for skewness and time variation in the higher moments of the distribution does not further improve forecasts. In Essay 2, by using 20 years of daily Standard and Poor 500 index returns, it is found that density forecasts are much improved by allowing for constant excess kurtosis but not improved by allowing for skewness. By allowing the kurtosis and skewness to be time varying the density forecasts are not further improved but on the contrary made slightly worse. In Essay 3 a new model incorporating conditional variance, skewness and kurtosis based on the Normal Inverse Gaussian (NIG) distribution is proposed. The new model and two previously used NIG models are evaluated by their Value at Risk (VaR) forecasts on a long series of daily Standard and Poor’s 500 returns. The results show that only the new model produces satisfactory VaR forecasts for both 1% and 5% VaR Taken together the results of the thesis show that kurtosis appears not to exhibit predictable time variation, whereas there is found some predictability in the skewness. However, the dynamic properties of the skewness are not completely captured by any of the models.