A proposal for a new specification for a conditionally heteroskedastic variance model: the Quadratic Moving-Average Conditional Heteroskedasticity and an application to the D. Mark-U.S. dollar Exchange Rate

Ever since the appearance of the ARCH model [Engle(1982a)], an impressive array of variance specifications belonging to the same class of models has emerged [i.e. Bollerslev's (1986) GARCH; Nelson's (1990) EGARCH]. This recent domain has achieved very successful developments. Nevertheless, several empirical studies seem to show that the performance of such models is not always appropriate [Boulier(1992)]. In this paper we propose a new specification: the Quadratic Moving Average Conditional heteroskedasticity model. Its statistical properties, such as the kurtosis and the symmetry, as well as two estimators (Method of Moments and Maximum Likelihood) are studied. Two statistical tests are presented, the first one tests for homoskedasticity and the second one, discriminates between ARCH and QMACH specification. A Monte Carlo study is presented in order to illustrate some of the theoretical results. An empirical study is undertaken for the DM-US exchange rate.

Linear least squares estimation of the first order moving average parameter

We propose an iterative procedure to minimize the sum of squares function which avoids the nonlinear nature of estimating the first order moving average parameter and provides a closed form of the estimator. The asymptotic properties of the method are discussed and the consistency of the linear least squares estimator is proved for the invertible case. We perform various Monte Carlo experiments in order to compare the sample properties of the linear least squares estimator with its nonlinear counterpart for the conditional and unconditional cases. Some examples are also discussed

Linear least squares estimation of the first order moving average parameter

We propose an iterative procedure to minimize the sum of squares function which avoids the nonlinear nature of estimating the first order moving average parameter and provides a closed form of the estimator. The asymptotic properties of the method are discussed and the consistency of the linear least squares estimator is proved for the invertible case. We perform various Monte Carlo experiments in order to compare the sample properties of the linear least squares estimator with its nonlinear counterpart for the conditional and unconditional cases. Some examples are also discussed

System identification using a linear combination of cumulant slices

In this paper we develop a new linear approach to identify the parameters of a moving average (MA) model from the statistics of the output. First, we show that, under some constraints, the impulse response of the system can be expressed as a linear combination of cumulant slices. Then, thisresult is used to obtain a new well-conditioned linear methodto estimate the MA parameters of a non-Gaussian process. Theproposed method presents several important differences withexisting linear approaches. The linear combination of slices usedto compute the MA parameters can be constructed from dif-ferent sets of cumulants of different orders, providing a generalframework where all the statistics can be combined. Further-more, it is not necessary to use second-order statistics (the autocorrelation slice), and therefore the proposed algorithm stillprovides consistent estimates in the presence of colored Gaussian noise. Another advantage of the method is that while mostlinear methods developed so far give totally erroneous estimates if the order is overestimated, the proposed approach doesnot require a previous estimation of the filter order. The simulation results confirm the good numerical conditioning of thealgorithm and the improvement in performance with respect to existing methods.

Análisis de la fiabilidad de indicadores para sistemas de seguimiento de precios en los mercados

Para este trabajo se ha desarrollado un programa en Matlab, que nos permite realizar ensayos con algunas de las herramientas fundamentales del análisis técnico. Concretamente nos hemos centrado en el “Indicador de Movimiento Direccional” de Wilder. El programa está formado por seis funciones que permiten descargar datos, hacer la simulación del indicador, ajustar automáticamente algunos de sus parámetros y presentar los resultados obtenidos en la simulación. Con los experimentos y simulaciones realizadas se ha visto la importancia de escoger adecuadamente los períodos de ±DIs (indicadores direccionales positivo y negativo) y el ADX (Average Directional Movement Index). También hemos visto que la reglas decisión apuntadas por autores de reconocido prestigio como Cava y Ortiz ,no siempre se comportan como cabría esperar. Se propone mejorar el rendimiento y la fiabilidad de este indicador Incluyendo alguna media móvil de los precios y el volumen de contratación, en los criterios de decisión. También se podría mejorar implementando un sistema para que se pudiesen autoajustar los criterios de decisión.

Suboptimal filtering and nonlinear time scale transformation for the analysis of multiexponential decays

Multiexponential decays may contain time-constants differing in several orders of magnitudes. In such cases, uniform sampling results in very long records featuring a high degree of oversampling at the final part of the transient. Here, we analyze a nonlinear time scale transformation to reduce the total number of samples with minimum signal distortion, achieving an important reduction of the computational cost of subsequent analyses. We propose a time-varying filter whose length is optimized for minimum mean square error

Impulse response recovery of linear systems through weighted cumulant slice

Identifiability of the so-called ω-slice algorithm is proven for ARMA linear systems. Although proofs were developed in the past for the simpler cases of MA and AR models, they were not extendible to general exponential linear systems. The results presented in this paper demonstrate a unique feature of the ω-slice method, which is unbiasedness and consistency when order is overdetermined, regardless of the IIR or FIR nature of the underlying system, and numerical robustness.

Forecasting tourism demand to Catalonia: neural networks vs. time series models

The increasing interest aroused by more advanced forecasting techniques, together with the requirement for more accurate forecasts of tourismdemand at the destination level due to the constant growth of world tourism, has lead us to evaluate the forecasting performance of neural modelling relative to that of time seriesmethods at a regional level. Seasonality and volatility are important features of tourism data, which makes it a particularly favourable context in which to compare the forecasting performance of linear models to that of nonlinear alternative approaches. Pre-processed official statistical data of overnight stays and tourist arrivals fromall the different countries of origin to Catalonia from 2001 to 2009 is used in the study. When comparing the forecasting accuracy of the different techniques for different time horizons, autoregressive integrated moving average models outperform self-exciting threshold autoregressions and artificial neural network models, especially for shorter horizons. These results suggest that the there is a trade-off between the degree of pre-processing and the accuracy of the forecasts obtained with neural networks, which are more suitable in the presence of nonlinearity in the data. In spite of the significant differences between countries, which can be explained by different patterns of consumer behaviour,we also find that forecasts of tourist arrivals aremore accurate than forecasts of overnight stays.

Forecasting tourism demand using consumer expectations

Abstract Purpose- There is a lack of studies on tourism demand forecasting that use non-linear models. The aim of this paper is to introduce consumer expectations in time-series models in order to analyse their usefulness to forecast tourism demand. Design/methodology/approach- The paper focuses on forecasting tourism demand in Catalonia for the four main visitor markets (France, the UK, Germany and Italy) combining qualitative information with quantitative models: autoregressive (AR), autoregressive integrated moving average (ARIMA), self-exciting threshold autoregressions (SETAR) and Markov switching regime (MKTAR) models. The forecasting performance of the different models is evaluated for different time horizons (one, two, three, six and 12 months). Findings- Although some differences are found between the results obtained for the different countries, when comparing the forecasting accuracy of the different techniques, ARIMA and Markov switching regime models outperform the rest of the models. In all cases, forecasts of arrivals show lower root mean square errors (RMSE) than forecasts of overnight stays. It is found that models with consumer expectations do not outperform benchmark models. These results are extensive to all time horizons analysed. Research limitations/implications- This study encourages the use of qualitative information and more advanced econometric techniques in order to improve tourism demand forecasting. Originality/value- This is the first study on tourism demand focusing specifically on Catalonia. To date, there have been no studies on tourism demand forecasting that use non-linear models such as self-exciting threshold autoregressions (SETAR) and Markov switching regime (MKTAR) models. This paper fills this gap and analyses forecasting performance at a regional level. Keywords Tourism, Forecasting, Consumers, Spain, Demand management Paper type Research paper

Análisis de la fiabilidad de indicadores para sistemas de seguimiento de precios en los mercados

Para este trabajo se ha desarrollado un programa en Matlab, que nos permite realizar ensayos con algunas de las herramientas fundamentales del análisis técnico. Concretamente nos hemos centrado en el “Indicador de Movimiento Direccional” de Wilder. El programa está formado por seis funciones que permiten descargar datos, hacer la simulación del indicador, ajustar automáticamente algunos de sus parámetros y presentar los resultados obtenidos en la simulación. Con los experimentos y simulaciones realizadas se ha visto la importancia de escoger adecuadamente los períodos de ±DIs (indicadores direccionales positivo y negativo) y el ADX (Average Directional Movement Index). También hemos visto que la reglas decisión apuntadas por autores de reconocido prestigio como Cava y Ortiz ,no siempre se comportan como cabría esperar. Se propone mejorar el rendimiento y la fiabilidad de este indicador Incluyendo alguna media móvil de los precios y el volumen de contratación, en los criterios de decisión. También se podría mejorar implementando un sistema para que se pudiesen autoajustar los criterios de decisión.

Nuclear magnetic resonance chemical shifts with the statistical average of orbital-dependent model potentials in Kohn-Sham density functional theory

The performance of the SAOP potential for the calculation of NMR chemical shifts was evaluated. SAOP results show considerable improvement with respect to previous potentials, like VWN or BP86, at least for the carbon, nitrogen, oxygen, and fluorine chemical shifts. Furthermore, a few NMR calculations carried out on third period atoms (S, P, and Cl) improved when using the SAOP potential

A numerical model for the gamma-ray emission of the microquasar LS 5039

The possible association between the microquasar LS 5039 and the EGRET source 3EG J1824-1514 suggests that microquasars could also be sources of high energy gamma-rays. In this paper, we explore, with a detailed numerical model, if this system can produce the emission detected by EGRET (>100 MeV) through inverse Compton (IC) scattering. Our numerical approach considers a population of relativistic electrons entrained in a cylindrical inhomogeneous jet, interacting with both the radiation and the magnetic fields, taking into account the Thomson and Klein-Nishina regimes of interaction. The computed spectrum reproduces the observed spectral characteristics at very high energy.

Gamma-ray emission from microquasars: a numerical model for LSI+61º303

We explore the possible association between the microquasar LSI +61°303 and the EGRET source 2CG 135+01/3EG J0241+6103 by studying, with a detailed numerical model, whether this system can produce the emission and the variability detected by EGRET (>100 MeV) through inverse Compton (IC) scattering. Our numerical approach considers a population of relativistic electrons entrained in a cylindrical inhomogeneous jet, interacting with both the radiation and the magnetic fields, taking into account the Thomson and Klein-Nishina regimes of interaction. Our results reproduce the observed spectral characteristics and variability at γ-rays, thus strengthening the identification of LSI +61°303 as a high-energy γ-ray source.

A microquasar model applied to unidentified gamma-ray sources

Among unidentified gamma-ray sources in the galactic plane, there are some that present significant variability and have been proposed to be high-mass microquasars. To deepen the study of the possible association between variable low galactic latitude gamma-ray sources and microquasars, we have applied a leptonic jet model based on the microquasar scenario that reproduces the gamma-ray spectrum of three unidentified gamma-ray sources, 3EG J1735-1500, 3EG J1828+0142 and GRO J1411-64, and is consistent with the observational constraints at lower energies. We conclude that if these sources were generated by microquasars, the particle acceleration processes could not be as efficient as in other objects of this type that present harder gamma-ray spectra. Moreover, the dominant mechanism of high-energy emission should be synchrotron self-Compton (SSC) scattering, and the radio jets may only be observed at low frequencies. For each particular case, further predictions of jet physical conditions and variability generation mechanisms have been made in the context of the model. Although there might be other candidates able to explain the emission coming from these sources, microquasars cannot be excluded as counterparts. Observations performed by the next generation of gamma-ray instruments, like GLAST, are required to test the proposed model.

A broadband leptonic model for gamma-ray emitting microquasars

Observational and theoretical studies point to microquasars (MQs) as possible counterparts of a significant fraction of the unidentified gamma-ray sources detected so far. At present, a proper scenario to explain the emission beyond soft X-rays from these objects is not known, nor what the precise connection is between the radio and the high-energy radiation. We develop a new model where the MQ jet is dynamically dominated by cold protons and radiatively dominated by relativistic leptons. The matter content and power of the jet are both related with the accretion process. The magnetic field is assumed to be close to equipartition, although it is attached to and dominated by the jet matter. For the relativistic particles in the jet, their maximum energy depends on both the acceleration efficiency and the energy losses. The model takes into account the interaction of the relativistic jet particles with the magnetic field and all the photon and matter fields. Such interaction produces significant amounts of radiation from radio to very high energies through synchrotron, relativistic Bremsstrahlung, and inverse Compton (IC) processes. Variability of the emission produced by changes in the accretion process (e.g. via orbital eccentricity) is also expected. The effects of the gamma-ray absorption by the external photon fields on the gamma-ray spectrum have been taken into account, revealing clear spectral features that might be observed. This model is consistent to the accretion scenario, energy conservation laws, and current observational knowledge, and can provide deeper physical information of the source when tested against multiwavelength data.