DynamicBeta: Time Varying Beta via Kalman filter & smoother
In sa-ccr/Trading: CCR, Advanced Correlation & Beta Estimates, Betting Strategies

DynamicBeta

R Documentation

Time Varying Beta via Kalman filter & smoother

Description

Calculates the beta of an investment strategy or stock by applying the Kalman filter & smoother. Apart from the beta timeseries, the state covariances are also returned so as to provide an estimate of the uncertainty of the results. The python package "Pykalman" is used for the calculations given its proven stability.

Usage

DynamicBeta(csvfilename, do_not_set_to_true = FALSE)

Arguments

`csvfilename`	the name of csv file containing the track record of the fund & the benchmark
`do_not_set_to_true`	function returns zero when TRUE - used only so as to pass the CRAN tests where pykalman couldn't be installed

Value

A list of beta values based on Kalman Filter & smoother and the respective covariance matrices

Author(s)

Tasos Grivas <tasos@openriskcalculator.com>

Examples


## calling DynamicBeta() without an argument loads a test file containing a sample track 
## record and a benchmark index
## ATTENTION!!: set do_not_set_to_true to FALSE when running the example
##-- this is only used to pass CRAN tests whereby
## pykalman was not installable!
dyn_beta_values = DynamicBeta(do_not_set_to_true = TRUE)

sa-ccr/Trading documentation built on Feb. 23, 2024, 9:26 p.m.