Climate Change and Sustainable Development

This paper argues that in the future the primary focus of policy research and global agreements should be the de-carbonization of economic development. Consequently, instead of treating climate stabilization and economic development as separate and equal, the strategy should be to re-integrate the two global policy goals, in part by separating responsibility (and funding) from action. This will require an approach that goes beyond Kyoto. The paper invokes the example of the Manhattan Project to argue for a massive, globally funded public investment program for the deployment of renewable energy technologies in developing countries.carbon emissions, climate change, sustainable development, international cooperation, mitigation, adaptation

Sustainability, economic restructuring and social change

In what could perhaps be referred to as the postmodernist mother of fiction - the novel 'Haroun and the sea of stories' - Salman Rushdie at one point puts his main character Haroun in a mail coach driven by a Mr Butt, on a rocky, windy, slippery road high up in the mountains, trying to reach a certain point before sunset. Faster and faster the bus goes, no longer stopping even to collect or deposit mail, frightening the passengers, but Mr Butt disregards their howls. Then Mr Butt sees another dangerous bit of road ahead and exclaims: 'The snow line! Icy patches ahead! Crumbling road surface! Hairpin bends! Danger of avalanches! ... ' and quite contrary to what Haroun had hoped, Mr Butt then turns his observations into a possibly fatal conclusion by shouting to himself: 'Full speed ahead' (Rushdie, p.36). I tell this story - which, incidentally, has a happy ending - because I regard Mr Butt's attitude as illustrative of the archetypical mainstream economist's response to almost any problem he or she may be confronted. with: they are very likely to conclude their analysis by recommending more economic growth - to develop at full speed. In a context of trying to contribute to sustainable development from a social science perspective, I think there is much more - if not much else - to say. We should learn from the warning on the wall near the exit of the bus station where Haroun read: If from speed (read: growth - JBO) you get your thrill, Take precaution - make your will This inaugural address marks my passage from the domain of environmental economics to the wider one of development studies, and the bridge between the two is called 'sustainable development'

Understanding Financial Market Volatility

__Abstract__ Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. Loosely speaking, volatility is defined as the average magnitude of fluctuations observed in some phenomenon over time. Within the area of economics, this definition narrows to the variability of an unpredictable random component of a time series variable. Typical examples in finance are returns on assets, such as individual stocks or a stock index like the S&P 500 index. As indicated by the quote from Campbell et al. (1997), (financial market) volatility is central to financial economics. Since it is the most common measure of the risk involved in investments in traded securities, it plays a crucial role in portfolio management, risk management, and pricing of derivative securities including options and futures contracts. Volatility is therefore closely tracked by private investors, institutional investors like pension funds, central bankers and policy makers. For example, the so-called Basel accords contain regulations where banks are required to hold a certain amount of capital to cover the risks involved in their consumer loans, mortgages and other assets. An estimate of the volatility of these assets is a crucial input for determining these capital requirements. In addition, the financial crisis in 2007-2008 has proven that the impact of financial market volatility is not only limited to the financial industry. It shows that volatility may be costly for the economy as a whole. For example, extreme stock market volatility may negatively influence aggregate investments behavior, in particular as companies often require equity as a source of external financing. This thesis contributes to the volatility literature by investigating several relevant aspects of volatility. First, we focus on the parameter estimation of multivariate volatility models, which is problematic if the number of considered assets increases. Second, we consider the question what exactly causes financial market volatility? In this context, we relate volatility with various types of information. In addition, we pay attention to modeling volatility, by adapting volatility models such that they allow for including possible exogenous variables. Finally, we turn to forecasting techniques of volatility, with the focus on the combination of density forecasts

Fractional integration and fat tails for realized covariance kernels

We introduce a new fractionally integrated model for covariance matrix dynamics based on the long-memory behavior of daily realized covariance matrix kernels. We account for fat tails in the data by an appropriate distributional assumption. The covariance matrix dynamics are formulated as a numerically efficient matrix recursion that ensures positive definiteness under simple parameter constraints. Using intraday stock data over the period 2001-2012, we construct realized covariance kernels and show that the new fractionally integrated model statistically and economically outperforms recent alternatives such as the multivariate HEAVY model and the multivariate HAR model. In addition, the long-memory behavior is more important during non-crisis periods

The R Package MitISEM: Mixture of Student-t Distributions using Importance Sampling Weighted Expectation Maximization for Efficient and Robust Simulation

This paper presents the R package MitISEM, which provides an automatic and flexible method to approximate a non-elliptical target density using adaptive mixtures of Student-t densities, where only a kernel of the target density is required. The approximation can be used as a candidate density in Importance Sampling or Metropolis Hastings methods for Bayesian inference on model parameters and probabilities. The package provides also an extended MitISEM algorithm, ‘sequential MitISEM’, which substantially decreases the computational time when the target density has to be approximated for increasing data samples. This occurs when the posterior distribution is updated with new observations and/or when one computes model probabilities using predictive likelihoods. We illustrate the MitISEM algorithm using three canonical statistical and econometric models that are characterized by several types of non-elliptical posterior shapes and that describe well-known data patterns in econometrics and finance. We show that the candidate distribution obtained by MitISEM outperforms those obtained by ‘naive’ approximations in terms of numerical efficiency. Further, the MitISEM approach can be used for Bayesian model comparison, using the predictive likelihoods

A Class of Adaptive EM-based Importance Sampling Algorithms for Efficient and Robust Posterior and Predictive Simulation

A class of adaptive sampling methods is introduced for efficient posterior and predictive simulation. The proposed methods are robust in the sense that they can handle target distributions that exhibit non-elliptical shapes such as multimodality and skewness. The basic method makes use of sequences of importance weighted Expectation Maximization steps in order to efficiently construct a mixture of Student-t densities that approximates accurately the target distribution -typically a posterior distribution, of which we only require a kernel - in the sense that the Kullback-Leibler divergence between target and mixture is minimized. We label this approach Mixture of t by Importance Sampling and Expectation Maximization (MitISEM). We also introduce three extensions of the basic MitISEM approach. First, we propose a method for applying MitISEM in a sequential manner, so that the candidate distribution for posterior simulation is cleverly updated when new data become available. Our results show that the computational effort reduces enormously. This sequential approach can be combined with a tempering approach, which facilitates the simulation from densities with multiple modes that are far apart. Second, we introduce a permutation-augmented MitISEM approach, for importance sampling from posterior distributions in mixture models without the requirement of imposing identification restrictions on the model's mixture regimes' parameters. Third, we propose a partial MitISEM approach, which aims at approximating the marginal and conditional posterior distributions of subsets of model parameters, rather than the joint. This division can substantially reduce the dimension of the approximation problem