Description Slots Methods Author(s) Examples

Class "regressionMulti" holds the results of an original portfolio, its benchmark, and the results of regression analysis of a multi-period portfolio.

`date.var`

:Object of class

`"character"`

storing the date(s) in the class object.`ret.var`

:Object of class

`"character"`

storing the name of the return variable.`reg.var`

:Object of class

`"character"`

storing the name of the regressors.`benchmark.weight`

:Object of class

`"character"`

storing the name of the benchmark weight variable.`portfolio.weight`

:Object of class

`"character"`

storing the name of the portfolio weight variable in the universe dataframe.`coefficients`

:Object of class

`"matrix"`

storing the estimated coefficients of the regression model for each time period.`benchmark.ret`

:Object of class

`"matrix"`

storing the benchmark return of the input portfolio for each time period.`portfolio.ret`

:Object of class

`"matrix"`

storing the portfolio return of the input portfolio for each time period.`act.ret`

:Object of class

`"matrix"`

storing the active return of the input portfolio for each time period.`act.expo`

:Object of class

`"matrix"`

storing the active exposure according to the regressors for each time period.`contrib`

:Object of class

`"matrix"`

storing the contribution of the regressors according to the input for each time period.`universe`

:Object of class

`"list"`

storing the entire input data frame.

- exposure
`signature(object = "regressionMulti")`

: Calculate and display the exposure of the input category of a portfolio.- plot
`signature(x = "regressionMulti", y = "missing")`

: Plot the exposure or the return of a regressionMulti class object.- returns
`signature(object = "regressionMulti")`

: Calculate the contribution of various effects based on the regression analysis.- show
`signature(object = "regressionMulti")`

: Summarize the essential information about the portfolio.- summary
`signature(object = "regressionMulti")`

: Summarize the portfolio and the regression-based attribution.

Yang Lu yang.lu@williams.edu

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pa documentation built on May 29, 2017, 11:44 a.m.

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