42 resultados para non-Gaussian volatility sequences

em Repositório Científico do Instituto Politécnico de Lisboa - Portugal


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Mestrado em Radiações Aplicadas às Tecnologias da Saúde. Área de especialização: Ressonância Magnética

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The iterative simulation of the Brownian bridge is well known. In this article, we present a vectorial simulation alternative based on Gaussian processes for machine learning regression that is suitable for interpreted programming languages implementations. We extend the vectorial simulation of path-dependent trajectories to other Gaussian processes, namely, sequences of Brownian bridges, geometric Brownian motion, fractional Brownian motion, and Ornstein-Ulenbeck mean reversion process.

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Purpose: compliance with treatment is a common problem when treating amblyopic patients. Visual acuity of amblyopic eye does not improve without effective occlusive therapy. The aim of this study is to identify potential risk factors of non-compliance with treatment when it is implemented by family in amblyopic children. Setting: a quantitative transversal study was performed in a public hospital and in a private clinic in Lisbon.

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n a recent paper we reported an experimental study of two N-alkylimidazolium salts. These ionic compounds exhibit liquid crystalline behaviour with melting points above 50 degrees C in bulk. However, if they are sheared, a (possibly non-equilibrium) lamellar phase forms at room temperature. Upon shearing a thin film of the material between microscope slides, textures were observed that are strikingly similar to liquid (wet) foams. The images obtained from polarising optical microscopy (POM) were found to share many of the known quantitative properties of a two-dimensional foam coarsening process. Here we report an experimental study of this foam using a shearing system coupled with POM. The structure and evolution of the foam are investigated through the image analysis of time sequences of micrographs obtained for well-controlled sets of physical parameters (sample thickness, shear rate and temperature). In particular, we find that there is a threshold shear rate below which no foam can form. Above this threshold, a steady-state foam pattern is obtained where the mean cell area generally decreases with increasing shear rate. Furthermore, the steady-state internal cell angles and distribution of the cell number of sides deviate from their equilibrium (i.e. zero-shear) values.

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In this paper we present results on the optimization of multilayered a-SiC:H heterostructures for wavelength-division (de) multiplexing applications. The non selective WDM device is a double heterostructure in a glass/ITO/a-SiC:H (p-i-n) /a-SiC:H(-p) /a-Si:H(-i')/a-SiC:H (-n')/ITO configuration. The single or the multiple modulated wavelength channels are passed through the device, and absorbed accordingly to its wavelength, giving rise to a time dependent wavelength electrical field modulation across it. The effect of single or multiple input signals is converted to an electrical signal to regain the information (wavelength, intensity and frequency) of the incoming photogenerated carriers. Here, the (de) multiplexing of the channels is accomplished electronically, not optically. This approach offers advantages in terms of cost since several channels share the same optical components; and the electrical components are typically less expensive than the optical ones. An electrical model gives insight into the device operation.

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An optically addressed read-write sensor based on two stacked p-i-n heterojunctions is analyzed. The device is a two terminal image sensing structure. The charge packets are injected optically into the p-i-n writer and confined at the illuminated regions changing locally the electrical field profile across the p-i-n reader. An optical scanner is used for charge readout. The design allows a continuous readout without the need for pixel-level patterning. The role of light pattern and scanner wavelengths on the readout parameters is analyzed. The optical-to-electrical transfer characteristics show high quantum efficiency, broad spectral response, and reciprocity between light and image signal. A numerical simulation supports the imaging process. A black and white image is acquired with a resolution around 20 mum showing the potentiality of these devices for imaging applications.

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In this review paper different designs based on stacked p-i'-n-p-i-n heterojunctions are presented and compared with the single p-i-n sensing structures. The imagers utilise self-field induced depletion layers for light detection and a modulated laser beam for sequential readout. The effect of the sensing element structure, cell configurations (single or tandem), and light source properties (intensity and wavelength) are correlated with the sensor output characteristics (light-to-dark sensivity, spatial resolution, linearity and S/N ratio). The readout frequency is optimized showing that scans speeds up to 104 lines per second can be achieved without degradation in the resolution. Multilayered p-i'-n-p-i-n heterostructures can also be used as wavelength-division multiplexing /demultiplexing devices in the visible range. Here the sensor element faces the modulated light from different input colour channels, each one with a specific wavelength and bit rate. By reading out the photocurrent at appropriated applied bias, the information is multiplexed or demultiplexed and can be transmitted or recovered again. Electrical models are present to support the sensing methodologies.

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This paper seeks to study the persistence in the G7’s stock market volatility, which is carried out using the GARCH, IGARCH and FIGARCH models. The data set consists of the daily returns of the S&P/TSX 60, CAC 40, DAX 30, MIB 30, NIKKEI 225, FTSE 100 and S&P 500 indexes over the period 1999-2009. The results evidences long memory in volatility, which is more pronounced in Germany, Italy and France. On the other hand, Japan appears as the country where this phenomenon is less obvious; nevertheless, the persistence prevails but with minor intensity.

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In this paper we analyze the relationship between volatility in index futures markets and the number of open and closed positions. We observe that, although in general both positions are positively correlated with contemporaneous volatility, in the case of S&P 500, only the number of open positions has influence over the volatility. Additionally, we observe a stronger positive relationship on days characterized by extreme movements of these contracting movements dominating the market. Finally, our findings suggest that day-traders are not associated to an increment of volatility, whereas uninformed traders, both opening and closing their positions, have to do with it.

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Financial literature and financial industry use often zero coupon yield curves as input for testing hypotheses, pricing assets or managing risk. They assume this provided data as accurate. We analyse implications of the methodology and of the sample selection criteria used to estimate the zero coupon bond yield term structure on the resulting volatility of spot rates with different maturities. We obtain the volatility term structure using historical volatilities and Egarch volatilities. As input for these volatilities we consider our own spot rates estimation from GovPX bond data and three popular interest rates data sets: from the Federal Reserve Board, from the US Department of the Treasury (H15), and from Bloomberg. We find strong evidence that the resulting zero coupon bond yield volatility estimates as well as the correlation coefficients among spot and forward rates depend significantly on the data set. We observe relevant differences in economic terms when volatilities are used to price derivatives.

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Most financial and economic time-series display a strong volatility around their trends. The difficulty in explaining this volatility has led economists to interpret it as exogenous, i.e., as the result of forces that lie outside the scope of the assumed economic relations. Consequently, it becomes hard or impossible to formulate short-run forecasts on asset prices or on values of macroeconomic variables. However, many random looking economic and financial series may, in fact, be subject to deterministic irregular behavior, which can be measured and modelled. We address the notion of endogenous volatility and exemplify the concept with a simple business-cycles model.

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We are concerned with providing more empirical evidence on forecast failure, developing forecast models, and examining the impact of events such as audit reports. A joint consideration of classic financial ratios and relevant external indicators leads us to build a basic prediction model focused in non-financial Galician SMEs. Explanatory variables are relevant financial indicators from the viewpoint of the financial logic and financial failure theory. The paper explores three mathematical models: discriminant analysis, Logit, and linear multivariate regression. We conclude that, even though they both offer high explanatory and predictive abilities, Logit and MDA models should be used and interpreted jointly.

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This article presents a Markov chain framework to characterize the behavior of the CBOE Volatility Index (VIX index). Two possible regimes are considered: high volatility and low volatility. The specification accounts for deviations from normality and the existence of persistence in the evolution of the VIX index. Since the time evolution of the VIX index seems to indicate that its conditional variance is not constant over time, I consider two different versions of the model. In the first one, the variance of the index is a function of the volatility regime, whereas the second version includes an autoregressive conditional heteroskedasticity (ARCH) specification for the conditional variance of the index.

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In this paper our aim is to gain a better understanding of the relationship between market volatility and industrial structure. As conflicting results have been documented regarding the relationship between market industry concentration and market volatility, this study investigates this relationship in the time series. We have found that this relationship is only significant and positive for Spain. Our results suggest that we cannot generalize across different countries that market industrial structure (concentration) is a significant factor in explaining market volatility.

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The aim of this paper is to analyze the forecasting ability of the CARR model proposed by Chou (2005) using the S&P 500. We extend the data sample, allowing for the analysis of different stock market circumstances and propose the use of various range estimators in order to analyze their forecasting performance. Our results show that there are two range-based models that outperform the forecasting ability of the GARCH model. The Parkinson model is better for upward trends and volatilities which are higher and lower than the mean while the CARR model is better for downward trends and mean volatilities.