The development of linear infrastructure increases the degree of fragmentation of natural areas and has a negative impact on biodiversity and the range of available ecosystem services. The basic competing land use model is expanded to include infrastructure development. The extended model leads to the conclusion that due to the dual impact of the infrastructure (lowering the value of ecosystem services and increasing the private rents to developed land), the size of the natural area in the long-term equilibrium will be lower compared to the basic model. The preservation of nature ceases to be profitable enough. Infrastructure also reduces the marginal costs of conversion and thus increasing the volume of natural land being converted at avery moment along the transition path. If the decisions on optimal management of natural areas and infrastructure development are undertaken together, the result is a lower density of the infrastructure network and a larger ecosystem area in the steady state.
Various trading strategies have been proposed that use estimates of the Hurst coefficient, which is an indicator of long-range dependence, for the calculation of buy and sell signals. This paper introduces frequency-domain tests for long-range dependence which do, in contrast to conventional procedures, not assume that the number of used periodogram ordinates grow with the length of the time series. These tests are applied to series of gold price returns and stock index returns in a rolling analysis. The results suggest that there is no long-range dependence, indicating that trading strategies based on fractal dynamics have no sound statistical basis.
In the paper we present and apply a Bayesian jump-diffusion model and stochastic volatility models with jumps. The problem of how to classify an observation as a result of a jump is addressed, under the Bayesian approach, by introducing latent variables. The empirical study is focused on the time series of gas forward contract prices and EUA futures prices. We analyse the frequency of jumps and relate the moments in which jumps occur to calendar effects or political and economic events and decisions. The calendar effects explain many jumps in gas contract prices. The single jump is identified in the EUA futures prices under the SV-type models. The jump is detected on the day the European Parliament voted against the European Commission’s proposal of backloading. The Bayesian results are compared with the outcomes of selected non-Bayesian techniques used for detecting jumps.
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