Pricing options and computing implied volatilities using neural networks

This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of accelerating the corresponding numerical methods. With ANNs being universal function approximators, this method trains an optimized ANN on a data set generated by a sophisticated financial model, and runs the trained ANN as an agent of the original solver in a fast and efficient way. We test this approach on three different types of solvers, including the analytic solution for the Black-Scholes equation, the COS method for the Heston stochastic volatility model and Brent's iterative root-finding method for the calculation of implied volatilities. The numerical results show that the ANN solver can reduce the computing time significantly

Modelling water-harvesting systems in the arid south of Tunisia using SWAT

In many arid countries, runoff water-harvesting systems support the livelihood of the rural population. Little is known, however, about the effect of these systems on the water balance components of arid watersheds. The objective of this study was to adapt and evaluate the GIS-based watershed model SWAT (Soil Water Assessment Tool) for simulating the main hydrologic processes in arid environments. The model was applied to the 270-km(2) watershed of wadi Koutine in southeast Tunisia, which receives about 200 mm annual rain. The main adjustment for adapting the model to this dry Mediterranean environment was the inclusion of water-harvesting systems, which capture and use surface runoff for crop production in upstream subbasins, and a modification of the crop growth processes. The adjusted version of the model was named SWAT-WH. Model evaluation was performed based on 38 runoff events recorded at the Koutine station between 1973 and 1985. The model predicted that the average annual watershed rainfall of the 12-year evaluation period (209 mm) was split into ET (72%), groundwater recharge (22%) and outflow (6%). The evaluation coefficients for calibration and validation were, respectively, R-2 (coefficient of determination) 0.77 and 0.44; E (Nash-Sutcliffe coefficient) 0.73 and 0.43; and MAE (Mean Absolute Error) 2.6 mm and 3.0 mm, indicating that the model could reproduce the observed events reasonably well. However, the runoff record was dominated by two extreme events, which had a strong effect on the evaluation criteria. Discrepancies remained mainly due to uncertainties in the observed daily rainfall and runoff data. Recommendations for future research include the installation of additional rainfall and runoff gauges with continuous data logging and the collection of more field data to represent the soils and land use. In addition, crop growth and yield monitoring is needed for a proper evaluation of crop production, to allow an economic assessment of the different water uses in the watershed

Molecular insights into the biogeography and species status of New Zealand's endemic Latrodectus spider species; L. katipo and L. atritus (Araneae, Theridiidae)

New Zealand's endemic sand dune Latrodectus widow spider species, L. katipo and L. atritus, possess behavioral and physiological attributes likely to promote dispersal over large distances. Morphological, physiological and behavioural similarities between L. katipo and L. hasselti, an Australian endemic, suggest gene flow may occur across the Tasman Sea. In this study we examine intraspecific and interspecific genetic relationships within the ND1 gene region between L. katipo, L. atritus, L. hasselti and L. hesperus to assess whether the genetic evidence supports current taxonomic species designations. We found low interspecific pairwise distances among L. katipo and L. atritus populations, suggesting either introgression, incomplete lineage sorting, or that the current taxonomic distinction between the two species may be invalid. Parsimony and maximum likelihood analyses were inconclusive as to the relationships between the New Zealand Latrodectus species and the Australian L. hasselti. Low pairwise distances between L. hasselti and the New Zealand widow fauna indicated that L. katipo and L. atritus were not present in New Zealand before the fragmentation of Gondwana

Accurate determination of elastic parameters for multi-component membranes

Heterogeneities in the cell membrane due to coexisting lipid phases have been conjectured to play a major functional role in cell signaling and membrane trafficking. Thereby the material properties of multiphase systems, such as the line tension and the bending moduli, are crucially involved in the kinetics and the asymptotic behavior of phase separation. In this Letter we present a combined analytical and experimental approach to determine the properties of phase-separated vesicle systems. First we develop an analytical model for the vesicle shape of weakly budded biphasic vesicles. Subsequently experimental data on vesicle shape and membrane fluctuations are taken and compared to the model. The combined approach allows for a reproducible and reliable determination of the physical parameters of complex vesicle systems. The parameters obtained set limits for the size and stability of nanodomains in the plasma membrane of living cells.Comment: (*) authors contributed equally, 6 pages, 3 figures, 1 table; added insets to figure

Variable clinical expression in patients with a germline MEN1 disease gene mutation: clues to a genotype–phenotype correlation

Multiple endocrine neoplasia type 1 is an inherited endocrine tumor syndrome, predominantly characterized by tumors of the parathyroid glands, gastroenteropancreatic tumors, pituitary adenomas, adrenal adenomas, and neuroendocrine tumors of the thymus, lungs or stomach. Multiple endocrine neoplasia type 1 is caused by germline mutations of the multiple endocrine neoplasia type 1 tumor suppressor gene. The initial germline mutation, loss of the wild-type allele, and modifying genetic and possibly epigenetic and environmental events eventually result in multiple endocrine neoplasia type 1 tumors. Our understanding of the function of the multiple endocrine neoplasia type 1 gene product, menin, has increased significantly over the years. However, to date, no clear genotype–phenotype correlation has been established

Seasonal Predictability of Daily Rainfall Characteristics in Central Northern Chile for Dry-Land Management

The seasonal predictability of daily winter rainfall characteristics relevant to dry-land management was investigated in the Coquimbo region of central northern Chile, with focus on the seasonal rainfall total, daily rainfall frequency, and mean daily rainfall intensity on wet days at the station scale. Three approaches of increasing complexity were tested. First, an index of the simultaneous El Nino–Southern Oscillation (ENSO) was regressed onto May–August (MJJA) observed precipitation; this explained 32% of station-averaged rainfall-amount variability, but performed poorly in a forecasting setting. The second approach used retrospective seasonal forecasts made with three general circulation models (GCMs) to produce downscaled seasonal rainfall statistics by means of canonical correlation analysis (CCA). In the third approach, a nonhomogeneous hidden Markov model (nHMM) driven by the GCM’s seasonal forecasts was used to model stochastic daily rainfall sequences. While the CCA is used as a downscaling method for the seasonal rainfall characteristics themselves, the nHMM has the ability to simulate a large ensemble of daily rainfall sequences at each station from which the rainfall statistics were calculated. Similar cross-validated skill estimates were obtained using both the CCA and nHMM, with the highest correlations with observations found for seasonal rainfall amount and rainfall frequency (up to 0.9 at individual stations). These findings were interpreted using analyses of observed rainfall spatial coherence, and by means of synoptic rainfall states derived from the HMM. The downscaled hindcasts were then tailored to meteorological drought prediction, using the standardized precipitation index (SPI) based on seasonal values, the frequency of substantial rainfall days (>15 mm; FREQ15) and the daily accumulated precipitation deficit. Deterministic hindcasts of SPI showed high hit rates, with high ranked probability skill score for probabilistic hindcasts of FREQ15 obtained via the nHMM

On the data-driven COS method

In this paper, we present the data-driven COS method, ddCOS. It is a Fourier-based financial option valuation method which assumes the availability of asset data samples: a characteristic function of the underlying asset probability density function is not required. As such, the presented technique represents a generalization of the well-known COS method [1]. The convergence of the proposed method is in line with Monte Carlo methods for pricing financial derivatives. The ddCOS method is then particularly interesting for density recovery and also for the efficient computation of the option's sensitivities Delta and Gamma. These are often used in risk management, and can be obtained at a higher accuracy with ddCOS than with plain Monte Carlo methods

Using an inverse modelling approach to evaluate the water retention in a simple water harvesting technique

In arid and semi-arid zones, runoff harvesting techniques are often applied to increase the water retention and infiltration on steep slopes. Additionally, they act as an erosion control measure to reduce land degradation hazards. Nevertheless, few efforts were observed to quantify the water harvesting processes of these techniques and to evaluate their efficiency. In this study, a combination of detailed field measurements and modelling with the HYDRUS-2D software package was used to visualize the effect of an infiltration trench on the soil water content of a bare slope in northern Chile. Rainfall simulations were combined with high spatial and temporal resolution water content monitoring in order to construct a useful dataset for inverse modelling purposes. Initial estimates of model parameters were provided by detailed infiltration and soil water retention measurements. Four different measurement techniques were used to determine the saturated hydraulic conductivity (<I>K</I><sub>sat</sub>) independently. The tension infiltrometer measurements proved a good estimator of the <I>K</I><sub>sat</sub> value and a proxy for those measured under simulated rainfall, whereas the pressure and constant head well infiltrometer measurements showed larger variability. Six different parameter optimization functions were tested as a combination of soil-water content, water retention and cumulative infiltration data. Infiltration data alone proved insufficient to obtain high model accuracy, due to large scatter on the data set, and water content data were needed to obtain optimized effective parameter sets with small confidence intervals. Correlation between the observed soil water content and the simulated values was as high as <I>R</I><sup>2</sup>=0.93 for ten selected observation points used in the model calibration phase, with overall correlation for the 22 observation points equal to 0.85. The model results indicate that the infiltration trench has a significant effect on soil-water storage, especially at the base of the trench