The R package RPESE (Risk and Performance Estimators Standard Errors) implements a new method for computing accurate standard errors of risk and performance estimators when returns are serially dependent. The new method makes use of the representation of a risk or performance estimator as a summation of a time series of influence-function (IF) transformed returns, and computes estimator standard errors using a sophisticated method of estimating the spectral density at frequency zero of the time series of IF-transformed returns. Two additional packages used by RPESE are introduced, namely RPEIF which computes and provides graphical displays of the IF of risk and performance estimators, and RPEGLMEN which implements a regularized Gamma generalized linear model polynomial fit to the periodogram of the time series of the IF-transformed returns. A Monte Carlo study shows that the new method provides more accurate estimates of standard errors for risk and performance estimators compared to well-known alternative methods in the presence of serial correlation.
RPESE, RPEIF, RPEGLMEN, PerformanceAnalytics, RobStatTM, nse, sandwich
Econometrics, Finance, Robust, SocialSciences
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For attribution, please cite this work as
Christidis & Martin, "The R Journal: RPESE: Risk and Performance Estimators Standard Errors with Serially Dependent Data", The R Journal, 2022
BibTeX citation
@article{RJ-2021-106,
author = {Christidis, Anthony-Alexander and Martin, R. Douglas},
title = {The R Journal: RPESE: Risk and Performance Estimators Standard Errors with Serially Dependent Data},
journal = {The R Journal},
year = {2022},
note = {https://doi.org/10.32614/RJ-2021-106},
doi = {10.32614/RJ-2021-106},
volume = {13},
issue = {2},
issn = {2073-4859},
pages = {697-712}
}