Computational aspects of prospect theory with asset pricing applications

We develop an algorithm to compute asset allocations for Kahneman and Tversky’s (Econometrica, 47(2), 263–291, 1979) prospect theory. An application to benchmark data as in Fama and French (Journal of Financial Economics, 47(2), 427–465, 1992) shows that the equity premium puzzle is resolved for parameter values similar to those found in the laboratory experiments of Kahneman and Tversky (Econometrica, 47(2), 263–291, 1979). While previous studies like Benartzi and Thaler (The Quarterly Journal of Economics, 110(1), 73–92, 1995), Barberis, Huang and Santos (The Quarterly Journal of Economics, 116(1), 1–53, 2001), and Grüne and Semmler (Asset prices and loss aversion, Germany, Mimeo Bielefeld University, 2005) focussed on dynamic aspects of asset pricing but only used loss aversion to explain the equity premium puzzle our paper explains the unconditional moments of asset pricing by a static two-period optimization problem. However, we incorporate asymmetric risk aversion. Our approach allows reducing the degree of loss aversion from 2.353 to 2.25, which is the value found by Tversky and Kahneman (Journal of Risk and Uncertainty, 5, 297–323, 1992) while increasing the risk aversion from 1 to 0.894, which is a slightly higher value than the 0.88 found by Tversky and Kahneman (Journal of Risk and Uncertainty, 5, 297–323, 1992). The equivalence of these parameter settings is robust to incorporating the size and the value portfolios of Fama and French (Journal of Finance, 47(2), 427–465, 1992). However, the optimal prospect theory portfolios found on this larger set of assets differ drastically from the optimal mean-variance portfolio.     

Mode II intersonic crack propagation in poroelastic media

A crack is steadily running in an elastic isotropic fluid-saturated porous solid at an intersonic constant speed c. The crack tip speeds of interest are bounded below by the slower between the slow longitudinal wave-speed and the shear wave-speed, and above by the fast longitudinal wave-speed. Biot’s theory of poroelasticity with inertia forces governs the motion of the mixture. The poroelastic moduli depend on the porosity, and the complete range of porosities n ∈ [0, 1] is investigated. Solids are obtained as the limit case n = 0, and the continuity of the energy release rate as the porosity vanishes is addressed. Three characteristic regions in the plane (n, c) are delineated, depending on the relative order of the body wave-speeds. Mode II loading conditions are considered, with a permeable crack surface. Cracks with and without process zones are envisaged. In each region, the analytical solution to a Riemann–Hilbert problem provides the stress, pore pressure and velocity fields near the tip of the crack. For subsonic propagation, the asymptotic crack tip fields are known to be continuous in the body [Loret and Radi (2001) J Mech Phys Solids 49(5):995–1020]. In contrast, for intersonic crack propagation without a process zone, the asymptotic stress and pore pressure might display a discontinuity across two or four symmetric rays emanating from the moving crack tip. Under Mode II loading condition, the singularity exponent for energetically admissible tip speeds turns out to be weaker than 1/2, except at a special point and along special curves of the (n, c)-plane. The introduction of a finite length process zone is required so that 1. the energy release rate at the crack tip is strictly positive and finite; 2. the relative sliding of the crack surfaces has the same direction as the applied loading. The presence of the process zone is shown to wipe out possible first order discontinuities.     

Nanostructured TiO2-based mixed metal oxides prepared using microemulsions for carbon monoxide detection

Nanostructured powders of Nb-doped TiO₂ (TN) and SnO₂ mixed with Nb-doped TiO₂ in two different atomic ratios—10 to 1 (TSN 101) and 1 to 1 (TSN 11)—were synthesized using the reverse micelle microemulsion of a nonionic surfactant (brine solution/1-hexanol/Triton X-100/cyclohexane). The powders were characterized by transmission electron microscopy (TEM) and X-ray diffraction (XRD). Thick films were fabricated for gas sensors and characterized by XRD analysis and field emission scanning electron microscopy (FE-SEM). The effects of the film morphology and firing temperature in the range 650–850 °C on CO sensitivity were studied. The best gas response, expressed as the ratio between the resistance in air and the resistance under gas exposure (R _air/R _gas), was measured for TSN 11 at 11 for 1,000 ppm CO exposure. All types of sensors showed good thermal stability. The electrochemical impedance spectroscopy (EIS) measurements were performed in different gas atmospheres (air, O₂, CO and NO₂) to better understand the electrical properties of the nanostructured mixed metal oxides.     

Design of an enhanced access point to optimize TCP performance in Wi-Fi hotspot networks

In the last years, the number of Wi-Fi hotspots at public venues has undergone a substantial growth, promoting the WLAN technologies as the ubiquitous solution to provide high-speed wireless connectivity in public areas. However, the adoption of a random access CSMA-based paradigm for the 802.11 MAC protocol makes difficult to ensure high throughput and a fair allocation of radio resources in 802.11-based WLANs. In this paper we evaluate extensively via simulations the interaction between the flow control mechanisms implemented at the TCP layer and the contention avoidance techniques used at the 802.11 MAC layer. We conducted our study considering initially M wireless stations performing downloads from the Internet. From our results, we observed that the TCP downlink throughput is not limited by the collision events, but by the inability of the MAC protocol to assign a higher chance of accessing the channel to the base station. We propose a simple and easy to implement modification of the base station’s behavior with the purpose of increasing the TCP throughput reducing useless MAC protocol overheads. With our scheme, the base station is allowed to transmit periodically bursts of data frames towards the mobile hosts. We design a resource allocation protocol aimed at maximizing the success probability of the uplink transmissions by dynamically adapting the burst length to the collision probability estimated by the base station. By its design, our scheme is also beneficial to achieve a fairer allocation of the channel bandwidth among the downlink and uplink flows, and among TCP and UDP flows. Simulation results confirm both the improvement in the TCP downlink throughput and the reduction of system unfairness.     

A support vector machine integrated system for the classification of operation anomalies in nuclear components and systems

A support vector machine (SVM) approach to the classification of transients in nuclear power plants is presented. SVM is a machine-learning algorithm that has been successfully used in pattern recognition for cluster analysis. In the present work, single- and multiclass SVM are combined into a hierarchical structure for distinguishing among transients in nuclear systems on the basis of measured data. An example of application of the approach is presented with respect to the classification of anomalies and malfunctions occurring in the feedwater system of a boiling water reactor. The data used in the example are provided by the HAMBO simulator of the Halden Reactor Project.     

An Analysis of the Dependence Among Financial Markets by Spatial Contagion

Spatial contagion between two financial markets X and Y appears when there is more dependence between X and Y when they are doing badly than when they exhibit typical performance. In this paper, we introduce an index to measure the contagion effects. This tool is based on the use of suitable copulas associated with the markets and on the calculation of the related conditional Spearman's correlation coefficients. As an empirical application, the proposed index is used to create a clustering of European stock market indices to assess their behavior in the recent years. The whole procedure is expected to be useful for portfolio diversification in crisis periods.     

Optimizing a remotely sensed proxy for plankton biomass in Lake Kivu

Many regions of the world, including inland lakes, present with suboptimal conditions for the remotely sensed retrieval of optical signals, thus challenging the limits of available satellite data-processing tools, such as atmospheric correction models (ACM) and water constituent-retrieval (WCR) algorithms. Working in such regions, however, can improve our understanding of remote-sensing tools and their applicability in new contexts, in addition to potentially offering useful information about aquatic ecology. Here, we assess and compare 32 combinations of two ACMs, two WCRs, and three binary categories of data quality standards to optimize a remotely sensed proxy of plankton biomass in Lake Kivu. Each parameter set is compared against the available ground-truth match-ups using Spearman’s right-tailed ρ. Focusing on the best sets from each ACM–WCR combination, their performances are discussed with regard to data distribution, sample size, spatial completeness, and seasonality. The results of this study may be of interest both for ecological studies on Lake Kivu and for epidemiological studies of disease, such as cholera, the dynamics of which has been associated with plankton biomass in other regions of the world.     

Quad‐Mesh Generation and Processing: A Survey

Triangle meshes have been nearly ubiquitous in computer graphics, and a large body of data structures and geometry processing algorithms based on them has been developed in the literature. At the same time, quadrilateral meshes, especially semi‐regular ones, have advantages for many applications, and significant progress was made in quadrilateral mesh generation and processing during the last several years. In this survey we discuss the advantages and problems of techniques operating on quadrilateral meshes, including surface analysis and mesh quality, simplification, adaptive refinement, alignment with features, parametrisation and remeshing.     

Local SVM approaches for fast and accurate classification of remote-sensing images

In this article, the task of remote-sensing image classification is tackled with local maximal margin approaches. First, we introduce a set of local kernel-based classifiers that alleviate the computational limitations of local support vector machines (SVMs), maintaining at the same time high classification accuracies. Such methods rely on the following idea: (a) during training, build a set of local models covering the considered data and (b) during prediction, choose the most appropriate local model for each sample to evaluate. Additionally, we present a family of operators on kernels aiming to integrate the local information into existing (input) kernels in order to obtain a quasi-local (QL) kernel. To compare the performances achieved by the different local approaches, an experimental analysis was conducted on three distinct remote-sensing data sets. The obtained results show that interesting performances can be achieved in terms of both classification accuracy and computational cost.