Titlebook: Discrete Time Series, Processes, and Applications in Finance; Gilles Zumbach Book 2013 Springer-Verlag Berlin Heidelberg 2013 91B84, 91B70

效果

https://doi.org/10.1007/978-0-387-77893-8need to be extended in order to denote accurately the time and time interval(s) dependencies in the computed time series like returns and volatilities. For inhomogeneous time series, a set of convenient operators is introduced, which makes it possible to estimate efficiently derived quantities like

有幫助

Flávio E. Nácul,John M. O’Donnellcity and the fat-tailed distributions. This chapter contains a systematic empirical analysis of the stylized facts for FX time series, often using a multiscale analysis in order to extract at best the deviations from a simple random walk. The analysis includes probability density functions, scaling

curriculum

IRATE

J. Esteban Varela M.D.,Albert J. Varon M.D.fat tails. The starting point is always the normal random walk written by Bachelier in 1900, and the structure of the extensions can be classified along some broad categories. A?first “grand tour” of the various mathematical structures is given in this chapter, presenting the core ideas underlying t

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Eructation

J. Esteban Varela M.D.,Albert J. Varon M.D.ess is introduced first. Two equivalent formulations are presented: the original formulation of Engle and another form that is easier to understand intuitively and that allows for natural multiscale generalizations. This process is investigated in detail, in particular volatility forecasts and lagge

Tortuous

頑固

Nutritional Care in Coloproctologyes is always time-reversal invariant, but not the processes with three or more states. In order to obtain realistic probability density, the constraints on the parameters of a regime-switching process are quite strong. But within these constraints, it is not possible to obtain a realistic multiscale

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時間等

Bulat A. Ziganshin,John A. Elefteriadesinvariant under time reversal. Because financial data are dominated by randomness, the differences, if they exist, are small. Three estimators are presented which are sensitive to the direction of time. When applied to empirical data, they show unambiguously that financial time series are not invari