Displaying similar documents to “Joint investment strategies with a superadditive capitalization function”

Calculating the variance in Markov-processes with random reward.

Francisco Benito (1982)

Trabajos de Estadística e Investigación Operativa

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In this article we present a generalization of Markov Decision Processes with discreet time where the immediate rewards in every period are not deterministic but random, with the two first moments of the distribution given. Formulas are developed to calculate the expected value and the variance of the reward of the process, formulas which generalize and partially correct other results. We make some observations about the distribution of rewards for processes with limited...

Weighting quantitative and qualitative variables in clustering methods.

Karina Gibert, Ulises Cortés (1997)

Mathware and Soft Computing

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Description of individuals in ill-structured domains produces messy data matrices. In this context, automated classification requires the management of those kind of matrices. One of the features involved in clustering is the evaluation of distances between individuals. Then, a special function to calculate distances between individuals partially simultaneously described by qualitative and quantitative variables is required. In this paper properties and details of the metrics...

Relative Measurement and Its Generalization in Decision Making. Why Pairwise Comparisons are Central in Mathematics for the Measurement of Intangible Factors. The Analytic Hierarchy/Network Process.

Thomas L. Saaty (2008)

RACSAM

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According to the great mathematician Henri Lebesgue, making direct comparisons of objects with regard to a property is a fundamental mathematical process for deriving measurements. Measuring objects by using a known scale first then comparing the measurements works well for properties for which scales of measurement exist. The theme of this paper is that direct comparisons are necessary to establish measurements for intangible properties that have no scales of measurement. In that case...

Modelling stock returns with AR-GARCH processes.

Elzbieta Ferenstein, Miroslaw Gasowski (2004)

SORT

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Financial returns are often modelled as autoregressive time series with random disturbances having conditional heteroscedastic variances, especially with GARCH type processes. GARCH processes have been intensely studied in financial and econometric literature as risk models of many financial time series. Analyzing two data sets of stock prices we try to fit AR(1) processes with GARCH or EGARCH errors to the log returns. Moreover, hyperbolic or generalized error distributions occur to...