Matthieu Garcin

Heart rate variability (HRV) has been analysed using linear and nonlinear methods. In the framework of a controlled neonatal stress model, we applied tone-entropy (T-E) analysis at multiple lags to understand the influence of external stressors on healthy term neonates. Forty term neonates were included in the study. HRV was analysed using multi-lag T-E at two resting and two stress phases (heel stimulation and a heel stick blood drawing phase). Higher mean entropy values and lower mean tone values when stressed showed a reduction in randomness with increased sympathetic and reduced parasympathetic activity. A ROC analysis was used to estimate the diagnostic performances of tone and entropy and combining both features. Comparing the resting and simulation phase separately, the performance of tone outperformed entropy, but combining the two in a quadratic linear regression model, neonates in resting as compared to stress phases could be distinguished with high accuracy. This raises the possibility that when applied across short time segments, multi-lag T-E becomes an additional tool for more objective assessment of neonatal stress.

Abstract The arthropod populations in five pear orchards were sampled by beating branches twice a month during the year 2003 in the Provence region of south‐eastern France. Multivariate analyses linking the matrix of species with a certain number of environmental variables (distance from the hedge, type of agricultural practices in the orchard, year effect, etc.) were performed to identify the most explanatory variables in terms of composition and structure (richness and equitability) of different arthropod populations. Univariate analyses were performed on descriptive population variables (numbers, diversity and richness) to highlight intrasite or intersite differences. These analyses revealed significant differences in the composition of orchard populations. The variance revealed by all environmental variables explained 28.7% of population composition. The variance revealed by hedge variables only explained 2.2% of population composition, while that revealed by variables concerning management practices explained 12.4%.

The fractional Brownian motion (fBm) extends the standard Brownian motion by introducing some dependence between non-overlapping increments. Consequently, if one considers for example that log-prices follow an fBm, one can exploit the non-Markovian nature of the fBm to forecast future states of the process and make statistical arbitrages. We provide new insights into forecasting an fBm, by proposing theoretical formulas for accuracy metrics relevant to a systematic trader, from the hit ratio to the expected gain and risk of a simple strategy. In addition, we answer some key questions about optimizing trading strategies in the fBm framework: Which lagged increments of the fBm, observed in discrete time, are to be considered? If the predicted increment is close to zero, up to which threshold is it more profitable not to invest? We also propose empirical applications on high-frequency FX rates, as well as on realized volatility series, exploring the rough volatility concept in a forecasting perspective.