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Moving Average Models (moving + average_models)
Selected AbstractsChaotic analysis of predictability versus knowledge discovery techniques: case study of the Polish stock marketEXPERT SYSTEMS, Issue 5 2002Hak Chun Increasing evidence over the past decade indicates that financial markets exhibit nonlinear dynamics in the form of chaotic behavior. Traditionally, the prediction of stock markets has relied on statistical methods including multivariate statistical methods, autoregressive integrated moving average models and autoregressive conditional heteroskedasticity models. In recent years, neural networks and other knowledge techniques have been applied extensively to the task of predicting financial variables. This paper examines the relationship between chaotic models and learning techniques. In particular, chaotic analysis indicates the upper limits of predictability for a time series. The learning techniques involve neural networks and case,based reasoning. The chaotic models take the form of R/S analysis to measure persistence in a time series, the correlation dimension to encapsulate system complexity, and Lyapunov exponents to indicate predictive horizons. The concepts are illustrated in the context of a major emerging market, namely the Polish stock market. [source] Real-time forecasting of photosmog episodes: the Naples case studyJOURNAL OF CHEMOMETRICS, Issue 7 2001A. Riccio Abstract In this paper we analysed the ozone time series data collected by the local monitoring network in the Naples urban area (southern Italy) during the spring/summer period of 1996. Our aim was to identify a reliable and effective model that could be used for the real-time forecasting of photosmog episodes. We studied the applicability of seasonal autoregressive integrated moving average models with some exogenous variables (ARIMAX) to our case study. The choice of exogenous variables,temperature, [NO2]/[NO] ratio and wind speed,was based on physical reasoning. The forecasting performance of all models was evaluated with data not used in model development, by means of an array of statistical indices: the comparison between observed and forecast means and standard deviations; intercept and slope of a least squares regression of forecast variable on observed variable; mean absolute and root mean square errors; and 95% confidence limits of forecast variable. The assessment of all models was also based on their tendency to forecast critical episodes. It was found that the model using information from the temperature data set to predict peak ozone levels gives satisfactory results, about 70% of critical episodes being correctly predicted by the 24,h ahead forecast function. Copyright © 2001 John Wiley & Sons, Ltd. [source] Evaluation of correlation forecasting models for risk managementJOURNAL OF FORECASTING, Issue 7 2007Vasiliki D. Skintzi Abstract Reliable correlation forecasts are of paramount importance in modern risk management systems. A plethora of correlation forecasting models have been proposed in the open literature, yet their impact on the accuracy of value-at-risk calculations has not been explicitly investigated. In this paper, traditional and modern correlation forecasting techniques are compared using standard statistical and risk management loss functions. Three portfolios consisting of stocks, bonds and currencies are considered. We find that GARCH models can better account for the correlation's dynamic structure in the stock and bond portfolios. On the other hand, simpler specifications such as the historical mean model or simple moving average models are better suited for the currency portfolio.,,Copyright © 2007 John Wiley & Sons, Ltd. [source] A Bayesian nonlinearity test for threshold moving average modelsJOURNAL OF TIME SERIES ANALYSIS, Issue 5 2010Qiang Xia We propose a Bayesian test for nonlinearity of threshold moving average (TMA) models. First, we obtain the marginal posterior densities of all parameters, including the threshold and delay, of the TMA model using Gibbs sampler with the Metropolis,Hastings algorithm. And then, we adopt reversible-jump Markov chain Monte Carlo methods to calculate the posterior probabilities for MA and TMA models. Posterior evidence in favour of the TMA model indicates threshold nonlinearity. Simulation experiments and a real example show that our method works very well in distinguishing MA and TMA models. [source] Estimation in integer-valued moving average modelsAPPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 3 2001Kurt Brännäs Abstract The paper presents new characterizations of the integer-valued moving average model. For four model variants, we give moments and probability generating functions. Yule,Walker and conditional least-squares estimators are obtained and studied by Monte Carlo simulation. A new generalized method of moment estimator based on probability generating functions is presented and shown to be consistent and asymptotically normal. The small sample performance is in some instances better than those of alternative estimators. Copyright © 2001 John Wiley & Sons, Ltd. [source] | ||||||||||