Hierarchical Markov normal mixture models with applications to financial asset returns

With the aim of constructing predictive distributions for daily returns, we introduce a new Markov normal mixture model in which the components are themselves normal mixtures. We derive the restrictions on the autocovariances and linear representation of integer powers of the time series in terms of the number of components in the mixture and the roots of the Markov process. We use the model prior predictive distribution to study its implications for some interesting functions of returns. We apply the model to construct predictive distributions of daily S&P500 returns, dollarpound returns, and one- and ten-year bonds. We compare the performance of the model with ARCH and stochastic volatility models using predictive likelihoods. The model's performance is about the same as its competitors for the bond returns, better than its competitors for the S&P 500 returns, and much better for the dollar-pound returns. Validation exercises identify some potential improvements. JEL Classification: C53, G12, C11, C14Asset returns, Bayesian, forecasting, MCMC, mixture models

A spectral EM algorithm for dynamic factor models

Realizamos dos contribuciones complementarias para estimar eficientemente modelos factoriales dinámicos: un algoritmo EM espectral y un procedimiento de inferencia indirecta iterada rapidísimo para modelos ARMA sin pérdida de eficiencia asintótica para cualquier número finito de iteraciones. Aunque nuestros métodos pueden estimar dichos modelos con muchas series sin buenas condiciones iniciales, cerca del óptimo recomendamos cambiar a un algoritmo del gradiente calculando analíticamente los gradientes espectrales usando el principio EM. Empleamos con éxito nuestros procedimientos para construir un índice que captura los movimientos comunes de las tasas de crecimiento del empleo a escala sectorial en Estados Unidos y lo comparamos con índices obtenidos con métodos semiparamétricosWe make two complementary contributions to effi ciently estimate dynamic factor models: a frequency domain EM algorithm and a swift iterated indirect inference procedure for ARMA models with no asymptotic effi ciency loss for any fi nite number of iterations. Although our procedures can estimate such models with many series without good initial values, near the optimum we recommend switching to a gradient method that analytically computes spectral scores using the EM principle. We successfully employ our methods to construct an index that captures the common movements of US sectoral employment growth rates, which we compare to the indices obtained by semiparametric method

Copulas in Kambaata

The paper elaborates on the synchronic functions and formal features of the non-locative copulas -ha/-ta and -VV-t in Kambaata. It discusses the intricate distribution rules of the copulas and the relationship between proximal demonstratives, case/gender markers and copulas

The Productivity Paradox and the New Economy: The Spanish Case

This paper studies the impact of the information and communication technologies (ICT) on economic growth in Spain using a dynamic general equilibrium approach. Contrary to previous works, we use a production function with six different capital inputs, three of them corresponding to ICT assets. Calibration of the model suggests that the contribution of ICT to Spanish productivity growth is very relevant, whereas the contribution of non-ICT capital has been even negative. Additionally, over the sample period 1995-2002, we find a negative TFP and productivity growth. These results together aim at the hypothesis that the Spanish economy could be placed within the productivity paradox.New economy, information and communication technologies, technological change, productivity paradox.

Osteoarthritis and Cartilage Biotribology

Osteoarthritis (OA) involves deterioration of cartilage, including enzymatic degradation and mechanical damage, resulting in disruption of cartilage lubrication, which in turn gives rise to the progressive deterioration in the structure and function of the cartilage tissue. In order to discover new lubrication strategies or OA treatment, in vitro test methodologies are necessary, which are inexpensive and have lower approval hurdles at universities and companies worldwide as compared to in vivo tests. Based on this, we propose cartilage models to mimic the enzymatic degradation and mechanical damage (cartilage crack initiation) and report their tribological behavior. Restoring and enhancing cartilage lubrication is crucial for resisting OA deterioration. In this thesis we also propose a series of strategies to enhance cartilage lubrication, including modified hyaluronic acid, coating a biomaterial with macromolecular components which have similar functions to native lubricating molecules found in lamina splendens; and recruitment of lubricous macromolecules from synovia fluid onto damaged cartilage surface

Hierarchical Markov normal mixture models with applications to financial asset returns

With the aim of constructing predictive distributions for daily returns, we introduce a new Markov normal mixture model in which the components are themselves normal mixtures. We derive the restrictions on the autocovariances and linear representation of integer powers of the time series in terms of the number of components in the mixture and the roots of the Markov process. We use the model prior predictive distribution to study its implications for some interesting functions of returns. We apply the model to construct predictive distributions of daily S&P500 returns, dollarpound returns, and one- and ten-year bonds. We compare the performance of the model with ARCH and stochastic volatility models using predictive likelihoods. The model's performance is about the same as its competitors for the bond returns, better than its competitors for the S&P 500 returns, and much better for the dollar-pound returns. Validation exercises identify some potential improvements

Spitting Performance Parameters and Their Biomechanical Implications in the Spitting Spider, Scytodes thoracica

Spitting spiders Scytodes spp. subdue prey by entangling them at a distance with a mixture of silk, glue, and venom. Using high-speed videography and differential interference contrast microscopy, the performance parameters involved in spit ejection by Scytodes thoracica (Araneae, Scytodidae) were measured. These will ultimately need to be explained in biomechanical and fluid dynamic terms. It was found that the ejection of “spit” from the opening of the venom duct (near the proximal end of the fang) was orderly. It resulted in a pattern that scanned along a lateral-medial axis (due to fang oscillations) while traversing from ventral to dorsal (due to cheliceral elevation). Each lateral-to-medial sweep of a fang produced silk-borne beads of glue that were not present during each subsequent medial-to-lateral sweep. The ejection of “spit” was very rapid. A full scan (5–57 fang cycles, one upsweep of a chelicera) typically occupied less than 30 ms and involved fang oscillations at 278–1781 Hz. Ejection velocities were measured as high as 28.8 m/s. The “spit” was contractile. During the 0.2 s following ejection, silk shortened by 40–60% and the product of a full scan by both of the chelicerae could exert an aggregate contractile force of 0.1 – 0.3 mN. Based on these parameters, hypotheses are described concerning the biomechanical and fluid dynamic processes that could enable this kind of material ejection

Multivariate moments expansion density: application of the dynamic equicorrelation model

In this study, we propose a new semi-nonparametric (SNP) density model for describing the density of portfolio returns. This distribution, which we refer to as the multivariate moments expansion (MME), admits any non-Gaussian (multivariate) distribution as its basis because it is specified directly in terms of the basis density’s moments. To obtain the expansion of the Gaussian density, the MME is a reformulation of the multivariate Gram-Charlier (MGC), but the MME is much simpler and tractable than the MGC when positive transformations are used to produce well-defined densities. As an empirical application, we extend the dynamic conditional equicorrelation (DECO) model to an SNP framework using the MME. The resulting model is parameterized in a feasible manner to admit two-stage consistent estimation and it represents the DECO as well as the salient non-Gaussian features of portfolio return distributions. The in- and out-of-sample performance of a MME-DECO model of a portfolio of 10 assets demonstrate that it can be a useful tool for risk management purposes

Why Churches Need Free-riders: Religious Capital Formation and Religious Group Survival

According to existing theory, religion thrives when groups overcome the free-rider problem in the production of religious goods. This paper explains, however, that allowing some free-riding is necessary in a dynamic setting. If an individual only contributes when she has high religious capital, and if capital only forms after exposure to the religious good, then a church must allow her to temporarily free-ride in order to turn her into a future contributor. Free-riders comprise a risky but necessary investment by the church. Strict churches screen out riskier investments yet still allow some free-riding. This explanation yields predictions consistent with the empirical evidence.Religion; Free-riding; Religious capital

Fault Diagnosis Method for Mobile Ad-hoc Network by Using Smart Neural Networks

AbstractMANETs are dynamic collection of autonomous nodes that communicate with each other via wireless connections. One of the events that the network should have expected it to be a fault, and the behavior is more important, in this network. So that fault diagnosis can effect on final performance of the network in such a way that it does not fall under the negative impact of the fault. A non-linear neural network is a statistical method for modeling data or the tools to make decisions. Artificial neural network is a method for pattern recognition and classification. Error detection is a problem of categorization or classification. The use of neural networks can be useful in fault diagnosis in MANETs because of fault diagnosis is a classification problem. But one problem with this method is placed in a local optimum. Here a method based on the combination of the back-propagation algorithm, a local search algorithm and learning automata as efficient global search, is proposed. In the proposed method, the algorithm of learning automata adjusting learning rate on neural network according to given formula. For training and testing the neural network of the mobile network parameters that were measured, were used as input and output. The results show that the proposed method in terms of repeatability, reliability and lack of placement in a local optimum is better