fBasics-package           package:fBasics           R Documentation

_P_o_r_t_f_o_l_i_o _M_o_d_e_l_l_i_n_g, _O_p_t_i_m_i_z_a_t_i_o_n _a_n_d _B_a_c_k_t_e_s_t_i_n_g

_D_e_s_c_r_i_p_t_i_o_n:

     The Rmetrics "fbasics" package is a collection of functions to 
     explore and to investigate basic properties of financial returns 
     and related quantities.

     The covered fields include techniques of explorative data analysis
     and the investigation of distributional properties, including
     parameter estimation and hypothesis testing.

     For _Explorative Data Anlysis_ several plot functions are
     available to explore the time series themselves, to show their
     distributional  properties, and to encover correlations and
     dependencies.

     Functions to compute _Distributional Properties_ of financial 
     returns and derivated and related series are enclosed. The in
     detail  considered distributions include the normal, the
     student-t, the stable, the family of generalised hyperbolic, and
     max drawdown distributions. Moment and log-likelihood estimators
     allow to estimate the distributional parameters. Functions to
     compute moments  and modes ar also avialable.

     The functions from _Hypothesis Testing_ one sample and two sample
     tests. Most of the one sample tests deal with testing normality.
     The two sample tests allow to compare two series to find out if
     the series are correlated or their distributions have the same
     distributional parameters.

_D_e_t_a_i_l_s:


       Package:    fBasics
       Type:       Package
       Version:    270.73
       Date:       2008
       License:    GPL Version 2 or later
       Copyright:  (c) 1999-2008 Diethelm Wuertz and Rmetrics Foundation
       URL:        <URL: http://www.rmetrics.org>

_O_v_e_r_v_i_e_w::

     The following chapters give a brief introduction how to optimize
     and analyze portfolios.

     1. Explorative Data Analysis
      2. Distributional Properties
      3. Hypthesis Testing

_1. _E_x_p_l_o_r_a_t_i_v_e _D_a_t_a _A_n_a_l_y_s_i_s:

     Explorative data analysis of financial return series and related 
     series 

     Exploratory Data Analysis is an approach for data analysis that 
     employs a variety of techniques most of graphical nature to
     maximize insight into a data set, to uncover underlying
     structures,  and to detect outliers and anomalies. For this
     several functions are implemented to plot the series, to plot the
     distribution from different views, and to visualize correlations
     and dependencies.

       *Time Series Plots:*  
       'seriesPlot'          Returns a tailored return series plot,
       'cumulatedPlot'       returns a cumulated series given the returns,
       'returnPlot'          returns returns given the cumulated series,
       'drawdownPlot'        returns drawdowns given the return series,
                             
       *Density Plots:*      
       'histPlot'            Returns a tailored histogram plot,
       'densityPlot'         returns a tailored kernel density estimate plot,
       'logDensityPlot'      returns a tailored log kernel density estimate plot,
       'qqPlot'              returns a quantile-quantile plot,
       'scalinglawPlot'      returns a scaling law plot,
                             
       *Box Plots:*          
       'boxPlot'             Returns a side-by-side standard box plot,
       'boxPercentilePlot'   Returns a side-by-side box-percentile plot,
                             
       *CorrelationPlots:*   
       'acfPlot'             autocorrelation function plot,
       'pacfPlot'            partial autocorrelation function plot,
       'lacfPlot'            lagged autocorrelation function plot,
       'teffectPlot'         Taylor effect plot.

_2. _D_i_s_t_r_i_b_u_t_i_o_n_a_l _P_r_o_p_e_r_t_i_e_s:

     The distributional properties can be investigate for several types
     of distribution functions which are important in the investigation
     of financial returns and related time series.

     _Stable and Skew-Stable Distribution_

       '[dpqr]stable'  the stable and skew-stable distribution,
       'stableMode'    the stable and skew-stable mode,
       'stableSlider'  interactive stable distribution display.

     _The family of Generalized Hyperbolic Distributions_

       *GH:*        
       '[dpqr]gh'   GH, Generlized hyperbolic distribution,
       'ghFit'      GH parameter estimation,
       'ghMode'     mode of the GH distribution,
                    
       *HYP:*       
       '[dpqr]hyp'  HYP, hyperbolic distribution,
       'hypFit'     HYP parameter estimation,
       'hypMode'    mode of the hyperbolic distribution,
       'hypSlider'  HYP distribution Slider,
                    
       *NIG:*       
       '[dpqr]nig'  NIG, hyperbolic distribution,
       'nigFit'     NIG parameter estimation,
       'nigMode'    mode of the NIG distribution,
       'nigSlider'  NIG distribution Slider,
                    
       *GHT:*       
       '[dpqr]ght'  GHT, generalized hyperbolic Student-t,
       'ghtFit'     GHT parameter estimation,
       'ghtMode'    mode of the GHT distribution,
       'ghtSlider'  GHT distribution Slider.

     _The max-Drawdown Distribution_

     The functions compute compute drawdown statistics.  Included are
     density, distribution function,   and random generation for the
     maximum drawdown  distributions. In addition the expectation of 
     drawdowns for Brownian motion can be computed.

       '[dpr]maxdd'  the max drawdown distribution,
       'maxddStats'  the expectation of drawdowns.

_3. _H_y_p_t_h_e_s_i_s _T_e_s_t_i_n_g:

     _One Sample Normality Tests_

     normality Tests

     _Two Sample Tests_

     correlationTest

     locationTest

     varianceTest

     scaleTest ?

_N_o_t_e:

     With Rmetrics version 2.7.0 ...

