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Bootstrapping financial time series
 Journal of Economic Surveys
, 2002
"... It is well known that time series of returns are characterized by volatility clustering and excess kurtosis. Therefore, when modelling the dynamic behavior of returns, inference and prediction methods, based on independent and/or Gaussian observations may be inadequate. As bootstrap methods are not ..."
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Cited by 15 (3 self)
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It is well known that time series of returns are characterized by volatility clustering and excess kurtosis. Therefore, when modelling the dynamic behavior of returns, inference and prediction methods, based on independent and/or Gaussian observations may be inadequate. As bootstrap methods are not, in general, based on any particular assumption on the distribution of the data, they are well suited for the analysis of returns. This paper reviews the application of bootstrap procedures for inference and prediction of …nancial time series. In relation to inference, bootstrap techniques have been applied to obtain the sample distribution of statistics for testing, for example, autoregressive dynamics in the conditional mean and variance, unit roots in the mean, fractional integration in volatility and the predictive ability of technical trading rules. On the other hand, bootstrap procedures have been used to estimate the distribution of returns which is of interest, for example, for Value at Risk
Vouldis, Global Approximations to Cost and Production Functions using
 Artificial Neural Networks, International Journal of Computational Intelligence Systems
"... The estimation of cost and production functions in economics usually relies on standard specifications which are less that satisfactory in numerous situations. However, instead of fitting the data with a prespecified model, Artificial Neural Networks (ANNs) let the data itself serve as evidence to ..."
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Cited by 2 (0 self)
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The estimation of cost and production functions in economics usually relies on standard specifications which are less that satisfactory in numerous situations. However, instead of fitting the data with a prespecified model, Artificial Neural Networks (ANNs) let the data itself serve as evidence to support the model’s estimation of the underlying process. In this context, the proposed approach combines the strengths of economics, statistics and machine learning research and the paper proposes a global approximation to arbitrary cost and production functions, respectively, given by ANNs. Suggestions on implementation are proposed. All relevant measures such as Returns to Scale (RTS) and Total Factor Productivity (TFP) may be computed routinely.
A multiple testing procedure for input variable selection in
"... neural networks ..."
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Parametric Bootstrap for Test of Contrast Difference in Neural Networks
"... This work concernes the contrast difference test and its asymptotic properties for non linear autoregressive models. Our approach is based on an application of the parametric bootstrap method. It is a resampling method based on the estimate parameters of the models. The resulting methodology is il ..."
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This work concernes the contrast difference test and its asymptotic properties for non linear autoregressive models. Our approach is based on an application of the parametric bootstrap method. It is a resampling method based on the estimate parameters of the models. The resulting methodology is illustrated by simulations of multilayer perceptron models, and an asymptotic justification is given at the end. 1
FOREX PREDICTION USING AN ARTIFICIAL INTELLIGENCE SYSTEM By
, 2004
"... Graduate Collage of the ..."
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Neural Networks for Approximating the Cost and Production Functions
"... Abstract — Most business decisions depend on accurate approximations to the cost and production functions. Traditionally, the estimation of cost and production functions in economics relies on standard specifications which are less than satisfactory in numerous situations. However, instead of fittin ..."
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Abstract — Most business decisions depend on accurate approximations to the cost and production functions. Traditionally, the estimation of cost and production functions in economics relies on standard specifications which are less than satisfactory in numerous situations. However, instead of fitting the data with a prespecified model, Artificial Neural Networks let the data itself serve as evidence to support the model’s estimation of the underlying process. In this context, the proposed approach combines the strengths of economics, statistics and machine learning research and the paper proposes a global approximation to arbitrary cost and production functions, respectively, given by ANNs. Suggestions on implementation are proposed and empirical application relies on standard techniques. All relevant measures such as scale economies and total factor productivity may be computed routinely.
Parametric Bootstrap for Asymptotic Test of Contrast Difference in Neural Networks
"... This work concernes the contrast difference test and its asymptotic properties for non linear autoregressive models. Our approach is based on an application of the parametric bootstrap method. It is a resampling method based on the estimate parameters of the models. The resulting methodology is il ..."
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This work concernes the contrast difference test and its asymptotic properties for non linear autoregressive models. Our approach is based on an application of the parametric bootstrap method. It is a resampling method based on the estimate parameters of the models. The resulting methodology is illustrated by simulations of multilayer perceptron models, and an asymptotic justification is given at the end. 1 Non Linear AutoRegressive Models Let be two positive integers. A functional autoregressive process on is a sequence of random vectors defined by: fiffflfffiffffi
"!$#&% '
(1) where % (
is an i.i.d. noise with mean 0 and constant variance)$ * , and where function is known. The parameter + belongs to a subset, of  (.0/213). Such a model is denoted below by 465 Let 787 ff 797 be the Euclidean norm on We define the contrast process associated to the least squares by:
Valid Statistical Inference Based on Feedforward Artificial Neural Network Models
"... With the help of recent theoretical results, we use the estimates from neural network modelling as basis for formal statistical inference. Multilayer perceptrons are applied to model biomass in a complex alpine terrain with limited amount of variables by combining temporal remote sensing with class ..."
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With the help of recent theoretical results, we use the estimates from neural network modelling as basis for formal statistical inference. Multilayer perceptrons are applied to model biomass in a complex alpine terrain with limited amount of variables by combining temporal remote sensing with classical field methods from plant physiology. We test the hypothesis that the dynamics of the biomass distribution can be captured with the help of georegistered and orthorectified colour images from the opposing hill slope. Therefore the network model is trained carefully and misspecification is tested by the nonlinearity tests of Ramsey and of Teräsvirta, Lin and Granger. Plausibility and sensitivity analysis as well as ecological considerations in respect of content support the validity of our final model. With the help of bootstrap techniques the significance of colour patterns for modelling phytomass is demonstrated. 1
Estimating Properties of Flow Statistics using Bootstrap
"... Traffic measurement has been gaining increasing attention of the network community in the last years, due to its application in a variety of important areas, such as traffic engineering and network planning. Much effort has been devoted to passive flow measurement since collecting packetlevel infor ..."
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Traffic measurement has been gaining increasing attention of the network community in the last years, due to its application in a variety of important areas, such as traffic engineering and network planning. Much effort has been devoted to passive flow measurement since collecting packetlevel information in high speed links makes this process extremely complex and expensive. There are some techniques for dealing with flow statistics in current commercial routers and associated measurement infrastructure. However, even though flowlevel information is more compact than packetlevel information, transmitting and storing it would still impose a significant burden on the operation of a typical Internet Service Provider (ISP). In this paper, we advocate that only a small portion of the flow records need to be preserved for further processing. We propose the use of the Bootstrap resampling technique for deriving statistical properties from a previously preprocessed sampled set of flows. Our results show that only 10 % or less of the original sampled statistics is necessary in order for Bootstrap to reconstruct the main characteristics of the original raw flow records. Key words Network Traffic Measurement; Sampling Technique.