Entropy of the Nordic electricity market: anomalous scaling, spikes, and mean-reversion

The electricity market is a very peculiar market due to the large variety of phenomena that can affect the spot price. However, this market still shows many typical features of other speculative (commodity) markets like, for instance, data clustering and mean reversion. We apply the diffusion entropy analysis (DEA) to the Nordic spot electricity market (Nord Pool). We study the waiting time statistics between consecutive spot price spikes and find it to show anomalous scaling characterized by a decaying power-law. The exponent observed in data follows a quite robust relationship with the one implied by the DEA analysis. We also in terms of the DEA revisit topics like clustering, mean-reversion and periodicities. We finally propose a GARCH inspired model but for the price itself. Models in the context of stochastic volatility processes appear under this scope to have a feasible description.Comment: 16 pages, 7 figure

Pricing Exotic Options in a Path Integral Approach

In the framework of Black-Scholes-Merton model of financial derivatives, a path integral approach to option pricing is presented. A general formula to price European path dependent options on multidimensional assets is obtained and implemented by means of various flexible and efficient algorithms. As an example, we detail the cases of Asian, barrier knock out, reverse cliquet and basket call options, evaluating prices and Greeks. The numerical results are compared with those obtained with other procedures used in quantitative finance and found to be in good agreement. In particular, when pricing at-the-money and out-of-the-money options, the path integral approach exhibits competitive performances.Comment: 21 pages, LaTeX, 3 figures, 6 table

Using habitat models to identify marine Important Bird and Biodiversity Areas for Chinstrap penguins in the South Orkney Islands

Tracking individual marine predators can provide vital information to aid the identification of important activity (foraging, commuting, rafting, resting, etc.) hotspots and therefore also to delineate priority sites for conservation. However, in certain locations (e.g. Antarctica) many marine mammal or seabird colonies remain untracked due to logistical constraints, and the colonies that are studied may not be the most important in terms of conservation priorities. Using data for one of the most abundant seabirds in the Antarctic as a case study (the Chinstrap penguin Pygoscelis antarcticus), we tested the use of correlative habitat models (used to predict distribution around untracked colonies) to overcome this limitation, and to enable the identification of important areas at-sea for colonies where tracking data are not available. First, Important Bird and Biodiversity Areas (IBA) were identified using a standardised, published approach using empirical data from birds tracked from colonies located in the South Orkney Islands. Subsequently, novel approaches using predicted distributions of Chinstrap penguins derived from habitatcorrelative habitat models were applied to identify important marine areas, and the results compared with the IBAs. Data were collected from 4 colonies over 4 years and during different stages of the breeding season. Results showed a high degree of overlap between the areas identified as important by observed data (IBAs) and by predicted distributions, revealing that habitat preference models can be used with a high degree of confidence to identify marine IBAs for these penguins. We provide a new method for designating a network of marine IBAs for penguins in Antarctic waters, based on outputs from habitatcorrelative habitat models when tracking data are not available. This can contribute to an evidence-based and precautionary approach to aid the management framework for Antarctic fisheries and for the protection of birds

Parametric estimation of different interest rate processes

The paper examines the estimation of alternative interest rate processes describing the dynamics of UK interest rates. The methodology concentrates on selecting non-parametrically the number of autocovariances to use in calculating a heteroscedasticity and autocorrelation consistent covariance matrix. This is important for drawing correct statistical inferences. It is found that the dependence of volatility on the level of interest rates is not as high in the UK market as has been documented in earlier studies of the US market. Further results reveal that there was a structural change in the parameters of the interest rate process during the period of the participation of Britain in the Exchange Rate Mechanism (ERM) of the European Monetary System. However, by utilizing the proposed non-parametric schemes, it is shown that statistical inference is sensitive to the correct choice of the number of autocovariances.