Evidence of the heterogeneous market hypothesis using wavelet multi -resolution analysis

In finance literature many economic theories and models have been proposed to explain and estimate the relationship between risk and return. Assuming risk averseness and rational behavior on part of the investor, the models are developed which are supposed to help in forming efficient portfolios that either maximize (minimize) the expected rate of return (risk) for a given level of risk (rates of return). One of the most used models to form these efficient portfolios is the Sharpe's Capital Asset Pricing Model (CAPM). In the development of this model it is assumed that the investors have homogeneous expectations about the future probability distribution of the rates of return. That is, every investor assumes the same values of the parameters of the probability distribution. Likewise financial volatility homogeneity is commonly assumed, where volatility is taken as investment risk which is usually measured by the variance of the rates of return. Typically the square root of the variance is used to define financial volatility, furthermore it is also often assumed that the data generating process is made of independent and identically distributed random variables. This again implies that financial volatility is measured from homogeneous time series with stationary parameters. In this dissertation, we investigate the assumptions of homogeneity of market agents and provide evidence for the case of heterogeneity in market participants' information, objectives, and expectations about the parameters of the probability distribution of prices as given by the differences in the empirical distributions corresponding to different time scales, which in this study are associated with different classes of investors, as well as demonstrate that statistical properties of the underlying data generating processes including the volatility in the rates of return are quite heterogeneous. In other words, we provide empirical evidence against the traditional views about homogeneity using non-parametric wavelet analysis on trading data, The results show heterogeneity of financial volatility at different time scales, and time-scale is one of the most important aspects in which trading behavior differs. In fact we conclude that heterogeneity as posited by the Heterogeneous Markets Hypothesis is the norm and not the exception.

Digital signal processing implementation for near real-time wavelet transformation system for binary images

Communication has become an essential function in our civilization. With the increasing demand for communication channels, it is now necessary to find ways to optimize the use of their bandwidth. One way to achieve this is by transforming the information before it is transmitted. This transformation can be performed by several techniques. One of the newest of these techniques is the use of wavelets. Wavelet transformation refers to the act of breaking down a signal into components called details and trends by using small waveforms that have a zero average in the time domain. After this transformation the data can be compressed by discarding the details, transmitting the trends. In the receiving end, the trends are used to reconstruct the image. In this work, the wavelet used for the transformation of an image will be selected from a library of available bases. The accuracy of the reconstruction, after the details are discarded, is dependent on the wavelets chosen from the wavelet basis library. The system developed in this thesis takes a 2-D image and decomposes it using a wavelet bank. A digital signal processor is used to achieve near real-time performance in this transformation task. A contribution of this thesis project is the development of DSP-based test bed for the future development of new real-time wavelet transformation algorithms.

Critério de correntropia no treinamento de redes fuzzy wavelet neural networks para identificação de sistemas dinâmicos não lineares

The great interest in nonlinear system identification is mainly due to the fact that a large amount of real systems are complex and need to have their nonlinearities considered so that their models can be successfully used in applications of control, prediction, inference, among others. This work evaluates the application of Fuzzy Wavelet Neural Networks (FWNN) to identify nonlinear dynamical systems subjected to noise and outliers. Generally, these elements cause negative effects on the identification procedure, resulting in erroneous interpretations regarding the dynamical behavior of the system. The FWNN combines in a single structure the ability to deal with uncertainties of fuzzy logic, the multiresolution characteristics of wavelet theory and learning and generalization abilities of the artificial neural networks. Usually, the learning procedure of these neural networks is realized by a gradient based method, which uses the mean squared error as its cost function. This work proposes the replacement of this traditional function by an Information Theoretic Learning similarity measure, called correntropy. With the use of this similarity measure, higher order statistics can be considered during the FWNN training process. For this reason, this measure is more suitable for non-Gaussian error distributions and makes the training less sensitive to the presence of outliers. In order to evaluate this replacement, FWNN models are obtained in two identification case studies: a real nonlinear system, consisting of a multisection tank, and a simulated system based on a model of the human knee joint. The results demonstrate that the application of correntropy as the error backpropagation algorithm cost function makes the identification procedure using FWNN models more robust to outliers. However, this is only achieved if the gaussian kernel width of correntropy is properly adjusted.

Identification of unusual events in multi-channel bridge monitoring data using wavelet transform and outlier analysis

Peer reviewed

Nuevas capacidades estatales para una sociedad transformada : Instituciones y políticas sanitarias en la provincia de Santa Fe primera mitad del siglo XX

El presente trabajo indaga las dinámicas de transformación de las políticas sanitarias en la provincia de Santa Fe en la primera mitad del siglo XX, focalizando tres coyunturas: en primer lugar, el contexto previo y la sanción de la ley de sanidad en 1932; en segundo término, los cambios sostenidos durante la segunda mitad de la década de 1930 que darían lugar a la creación del Ministerio de Salud y Trabajo en 1941; finalmente las innovaciones proyectadas desde la intervención de 1943 y los posteriores gobiernos peronistas. A partir de los debates parlamentarios, las memorias ministeriales, la prensa y las publicaciones especializadas, nos proponemos dar cuenta de algunos aspectos de este proceso de transformación en las políticas sanitarias locales en diálogo con el plano nacional.

Nuevas capacidades estatales para una sociedad transformada : Instituciones y políticas sanitarias en la provincia de Santa Fe primera mitad del siglo XX

El presente trabajo indaga las dinámicas de transformación de las políticas sanitarias en la provincia de Santa Fe en la primera mitad del siglo XX, focalizando tres coyunturas: en primer lugar, el contexto previo y la sanción de la ley de sanidad en 1932; en segundo término, los cambios sostenidos durante la segunda mitad de la década de 1930 que darían lugar a la creación del Ministerio de Salud y Trabajo en 1941; finalmente las innovaciones proyectadas desde la intervención de 1943 y los posteriores gobiernos peronistas. A partir de los debates parlamentarios, las memorias ministeriales, la prensa y las publicaciones especializadas, nos proponemos dar cuenta de algunos aspectos de este proceso de transformación en las políticas sanitarias locales en diálogo con el plano nacional.

Nuevas capacidades estatales para una sociedad transformada : Instituciones y políticas sanitarias en la provincia de Santa Fe primera mitad del siglo XX

El presente trabajo indaga las dinámicas de transformación de las políticas sanitarias en la provincia de Santa Fe en la primera mitad del siglo XX, focalizando tres coyunturas: en primer lugar, el contexto previo y la sanción de la ley de sanidad en 1932; en segundo término, los cambios sostenidos durante la segunda mitad de la década de 1930 que darían lugar a la creación del Ministerio de Salud y Trabajo en 1941; finalmente las innovaciones proyectadas desde la intervención de 1943 y los posteriores gobiernos peronistas. A partir de los debates parlamentarios, las memorias ministeriales, la prensa y las publicaciones especializadas, nos proponemos dar cuenta de algunos aspectos de este proceso de transformación en las políticas sanitarias locales en diálogo con el plano nacional.

VLSI design and implementation of 2-D Inverse Discrete Wavelet Transform

This paper proposes a JPEG-2000 compliant architecture capable of computing the 2 -D Inverse Discrete Wavelet Transform. The proposed architecture uses a single processor and a row-based schedule to minimize control and routing complexity and to ensure that processor utilization is kept at 100%. The design incorporates the handling of borders through the use of symmetric extension. The architecture has been implemented on the Xilinx Virtex2 FPGA.

Nuevas capacidades estatales para una sociedad transformada : Instituciones y políticas sanitarias en la provincia de Santa Fe primera mitad del siglo XX

El presente trabajo indaga las dinámicas de transformación de las políticas sanitarias en la provincia de Santa Fe en la primera mitad del siglo XX, focalizando tres coyunturas: en primer lugar, el contexto previo y la sanción de la ley de sanidad en 1932; en segundo término, los cambios sostenidos durante la segunda mitad de la década de 1930 que darían lugar a la creación del Ministerio de Salud y Trabajo en 1941; finalmente las innovaciones proyectadas desde la intervención de 1943 y los posteriores gobiernos peronistas. A partir de los debates parlamentarios, las memorias ministeriales, la prensa y las publicaciones especializadas, nos proponemos dar cuenta de algunos aspectos de este proceso de transformación en las políticas sanitarias locales en diálogo con el plano nacional.

Multiresolution analysis (discrete wavelet transform) through Daubechies family for emotion recognition in speech

We propose a study of the mathematical properties of voice as an audio signal -- This work includes signals in which the channel conditions are not ideal for emotion recognition -- Multiresolution analysis- discrete wavelet transform – was performed through the use of Daubechies Wavelet Family (Db1-Haar, Db6, Db8, Db10) allowing the decomposition of the initial audio signal into sets of coefficients on which a set of features was extracted and analyzed statistically in order to differentiate emotional states -- ANNs proved to be a system that allows an appropriate classification of such states -- This study shows that the extracted features using wavelet decomposition are enough to analyze and extract emotional content in audio signals presenting a high accuracy rate in classification of emotional states without the need to use other kinds of classical frequency-time features -- Accordingly, this paper seeks to characterize mathematically the six basic emotions in humans: boredom, disgust, happiness, anxiety, anger and sadness, also included the neutrality, for a total of seven states to identify