The R Journal: accepted article

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RPESE: Risk and Performance Estimators Standard Errors with Serially Dependent Data PDF download
Anthony-Alexander Christidis and R. Douglas Martin

Abstract The Risk and Performance Estimators Standard Errors package RPESE 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.

Received: 2021-05-24; online 2021-12-15, supplementary material, (2.2 Kb)
CRAN packages: RPESE, RPEIF, RPEGLMEN, PerformanceAnalytics, RobStatTM, nse, sandwich
CRAN Task Views implied by cited CRAN packages: Econometrics, Finance, Robust, SocialSciences

CC BY 4.0
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  author = {Anthony-Alexander Christidi and  R. Douglas Martin},
  title = {{RPESE: Risk and Performance Estimators Standard Errors with Serially Dependent Data}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-106},
  url = {}