From 239219b81d482d829f1aa0ae8c013b035031c339 Mon Sep 17 00:00:00 2001 From: a91quaini Date: Fri, 5 Apr 2024 13:04:17 +0200 Subject: [PATCH] fgx uses gross returns --- .Rbuildignore | 1 + R/data.R | 22 +- R/fgx_factors_test.R | 41 +- R/sdf_coefficients.R | 11 +- data-raw/17_Industry_Portfolios.CSV | 1173 ++++++++++++++++++ data-raw/25_Portfolios_5x5.CSV | 1173 ++++++++++++++++++ data-raw/F-F_Momentum_Factor.CSV | 1167 +++++++++++++++++ data-raw/F-F_Research_Data_5_Factors_2x3.csv | 729 +++++++++++ data-raw/make_factors.R | 17 + data-raw/make_returns.R | 30 + data-raw/make_risk_free.R | 6 + data/factors.rda | Bin 10487 -> 11774 bytes data/returns.rda | Bin 116381 -> 129386 bytes data/risk_free.rda | Bin 0 -> 2566 bytes inst/examples/example.R | 67 +- inst/examples/risk_premia.png | Bin 154153 -> 156245 bytes inst/examples/sdf_coefficients.png | Bin 0 -> 147519 bytes man/FGXFactorsTest.Rd | 11 +- man/factors.Rd | 4 +- man/returns.Rd | 4 +- man/risk_free.Rd | 27 + 21 files changed, 4443 insertions(+), 40 deletions(-) create mode 100644 data-raw/17_Industry_Portfolios.CSV create mode 100644 data-raw/25_Portfolios_5x5.CSV create mode 100644 data-raw/F-F_Momentum_Factor.CSV create mode 100644 data-raw/F-F_Research_Data_5_Factors_2x3.csv create mode 100644 data-raw/make_factors.R create mode 100644 data-raw/make_returns.R create mode 100644 data-raw/make_risk_free.R create mode 100644 data/risk_free.rda create mode 100644 inst/examples/sdf_coefficients.png create mode 100644 man/risk_free.Rd diff --git a/.Rbuildignore b/.Rbuildignore index c502ef3..7a5870e 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -7,3 +7,4 @@ ^cran-comments\.md$ ^CRAN-SUBMISSION$ ^readme.bib +^data-raw$ diff --git a/R/data.R b/R/data.R index f261263..8746ec2 100644 --- a/R/data.R +++ b/R/data.R @@ -1,7 +1,7 @@ -#' Test Asset Excess Returns - monthly observations from `01/1970` to `12/2021` +#' Test Asset Excess Returns - monthly observations from `07/1963` to `02/2024` #' #' Monthly excess returns on the `25` Size/Book-to-Market double sorted portfolios -#' and the `17` industry portfolios from `01/1970` to `12/2021`. +#' and the `17` industry portfolios from `07/1963` to `02/2024`. #' #' @format ## `returns` #' A data frame with `624` rows and `43` columns: @@ -12,9 +12,9 @@ #' @source "returns" -#' Factors - monthly observations from `01/1970` to `12/2021` +#' Factors - monthly observations from `07/1963` to `02/2024` #' -#' Monthly observations from `01/1970` to `12/2021` of +#' Monthly observations from `07/1963` to `02/2024` of #' the Fama-French `5` factors and the momentum factor. #' #' @format ## `factors` @@ -25,3 +25,17 @@ #' } #' @source "factors" + +#' Risk free - monthly observations from `07/1963` to `02/2024` +#' +#' Monthly observations from `07/1963` to `02/2024` of +#' the US risk free asset. +#' +#' @format ## `risk_free` +#' A data frame with `624` rows and `2` columns: +#' \describe{ +#' \item{Date}{Date in yyyymm format} +#' \item{...}{risk free observations} +#' } +#' @source +"risk_free" diff --git a/R/fgx_factors_test.R b/R/fgx_factors_test.R index 2a2ac6a..56c0d4f 100644 --- a/R/fgx_factors_test.R +++ b/R/fgx_factors_test.R @@ -29,8 +29,8 @@ #' pre-whiten the series by fitting a VAR(1), #' i.e., a vector autoregressive model of order 1. #' -#' @param returns A `n_observations x n_returns`-dimensional matrix of test asset -#' excess returns. +#' @param gross_returns A `n_observations x n_returns`-dimensional matrix of test asset +#' gross returns. #' @param control_factors A `n_observations x n_control_factors`-dimensional #' matrix of control or benchmark factors. #' @param new_factors A `n_observations x n_new_factors`-dimensional @@ -50,16 +50,19 @@ #' control_factors = intrinsicFRP::factors[,2:4] #' new_factors = intrinsicFRP::factors[,5:7] #' returns = intrinsicFRP::returns[,-1] +#' RF = intrinsicFRP::risk_free[,-1] +#' +#' gross_returns = returns + 1 + RF #' #' output = FGXFactorsTest( -#' returns, +#' gross_returns, #' control_factors, #' new_factors #' ) #' #' @export FGXFactorsTest = function( - returns, + gross_returns, control_factors, new_factors, n_folds = 5, @@ -69,7 +72,7 @@ FGXFactorsTest = function( # check function arguments if (check_arguments) { - CheckData(returns, control_factors) + CheckData(gross_returns, control_factors) stopifnot("`new_factors` must contain numeric values" = is.numeric(new_factors)) stopifnot("`new_factors` contains more variables (columns) than observations (rows)" = nrow(new_factors) > ncol(new_factors)) stopifnot("`new_factors` must not contain missing values (NA/NaN)" = !anyNA(new_factors)) @@ -77,10 +80,13 @@ FGXFactorsTest = function( } + # set number of new factors + n_new = ncol(new_factors) + # compute moments of data - avg_returns = colMeans(returns) - cov_returns_control_factors = stats::cov(returns, control_factors) - cov_returns_new_factors = stats::cov(returns, new_factors) + avg_returns = colMeans(gross_returns) + cov_returns_control_factors = stats::cov(gross_returns, control_factors) + cov_returns_new_factors = stats::cov(gross_returns, new_factors) # first lasso selection: # lasso regression of average returns on the covariances between returns @@ -98,7 +104,7 @@ FGXFactorsTest = function( # lasso regression of each covariance between new factors and returns on # covariances between control factors and returns # take the union of the first and all second step selections - for (ii in 1:ncol(new_factors)) { + for (ii in 1:n_new) { second_lasso = glmnet::cv.glmnet( x = cov_returns_control_factors, @@ -118,7 +124,8 @@ FGXFactorsTest = function( # OLS regression of average returns on covariances between new factors and returns # and on the covariances between the selected control factors and returns. # the selected control factors are the union of the first and all second step selections. - covariances = cbind( + predictors = cbind( + matrix(1, ncol(gross_returns), 1), cov_returns_new_factors, cov_returns_control_factors[, idx_selected, drop=FALSE] ) @@ -126,8 +133,8 @@ FGXFactorsTest = function( # cross_sectional_fit = stats::lm(avg_returns ~ covariances) # compute the SDF coefficients sdf_coefficients = solve( - t(covariances) %*% covariances, - t(covariances) %*% avg_returns + t(predictors) %*% predictors, + t(predictors) %*% avg_returns ) ## Estimate the Newey-West type covariance estimator of the new factors' @@ -138,7 +145,7 @@ FGXFactorsTest = function( control_factors = control_factors[,idx_selected] cov_selected = c() - for (ii in 1:ncol(new_factors)) { + for (ii in 1:n_new) { lasso_fit = glmnet::cv.glmnet( x = control_factors, @@ -159,19 +166,19 @@ FGXFactorsTest = function( # compute the Newey-West type covariance estimator covariance = .Call(`_intrinsicFRP_FGXThreePassCovarianceCpp`, - returns, + gross_returns, control_factors, new_factors, - sdf_coefficients, + sdf_coefficients[-1], cov_selected ) # extract the standard errors - standard_errors = sqrt(diag(covariance)) / sqrt(nrow(returns)) + standard_errors = sqrt(diag(covariance)) / sqrt(nrow(gross_returns)) # return a list containing the new factors' SDF coefficients and corresponding # standard errors - output = list(sdf_coefficients[1:ncol(new_factors)], standard_errors, idx_selected) + output = list(sdf_coefficients[1 + 1:n_new], standard_errors, idx_selected) names(output) = c("sdf_coefficients", "standard_errors", "controls_selected") return(output) diff --git a/R/sdf_coefficients.R b/R/sdf_coefficients.R index 1bdb36d..66f9f40 100644 --- a/R/sdf_coefficients.R +++ b/R/sdf_coefficients.R @@ -85,13 +85,20 @@ SDFCoefficients = function( } # compute the FRP estimate and, eventually, their standard errors - return(.Call(`_intrinsicFRP_SDFCoefficientsCpp`, + output = .Call(`_intrinsicFRP_SDFCoefficientsCpp`, returns, factors, misspecification_robust, include_standard_errors, hac_prewhite, target_level_gkr2014_screening - )) + ) + + if (target_level_gkr2014_screening > 0) { + # Transform c++ indices (starting at 0) into R indices (starting at 1). + output$selected_factor_indices = output$selected_factor_indices + 1 + } + + return(output) } diff --git a/data-raw/17_Industry_Portfolios.CSV b/data-raw/17_Industry_Portfolios.CSV new file mode 100644 index 0000000..da3a0db --- /dev/null +++ b/data-raw/17_Industry_Portfolios.CSV @@ -0,0 +1,1173 @@ +Date,Food ,Mines,Oil ,Clths,Durbl,Chems,Cnsum,Cnstr,Steel,FabPr,Machn,Cars ,Trans,Utils,Rtail,Finan,Other +192607, 0.48, 3.78, -1.41, 6.02, -1.62, 8.46, 1.42, 2.31, 4.07, 8.77, 3.79, 17.43, 1.83, 7.04, 0.13, -0.02, 1.22 +192608, 2.91, 0.69, 3.60, 0.15, -1.96, 5.70, 5.84, 4.33, 2.17, -5.56, 2.35, 3.96, 4.54, -1.69, -0.68, 4.47, 3.11 +192609, 1.20, 1.10, -3.68, 0.26, 0.24, 5.48, 1.21, -0.06, 0.15, -4.13, -0.65, 5.57, 0.32, 2.04, 0.21, -1.61, 1.82 +192610, -3.06, -0.79, -1.02, 0.37, -6.07, -4.76, 0.69, -4.79, -3.85, -5.13, -3.29, -8.29, -2.90, -2.63, -2.26, -5.51, -0.88 +192611, 6.37, 4.38, -0.01, 2.22, -1.95, 5.27, 4.63, 2.45, 3.86, 3.57, 4.54, -0.28, 2.19, 3.71, 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a/data-raw/make_returns.R b/data-raw/make_returns.R new file mode 100644 index 0000000..059df35 --- /dev/null +++ b/data-raw/make_returns.R @@ -0,0 +1,30 @@ +## obtain data on portfolio excess returns from the Kenneth French data library +# from csv file to data.frame +# assumes that superfluous text and equally weighted returns were removed from +# the csv files, and the first column was named "Date" +# also, requires data on FF3 factors (for the risk-free return) + +path = "data-raw/" +ff5 = "F-F_Research_Data_5_Factors_2x3.csv" +mebeme = "25_Portfolios_5x5.CSV" +ind = "17_Industry_Portfolios.CSV" + +# source data on the risk free and portfolio returns +factors_ff5 = read.csv(file=paste(path, ff5, sep = "")) +returns_mebeme = read.csv(paste(path, mebeme, sep = "")) +returns_ind = read.csv(paste(path, ind, sep = "")) + +start = factors_ff5[1,"Date"] +returns_mebeme = returns_mebeme[returns_mebeme[,"Date"] >= start,] +returns_ind = returns_ind[returns_ind[,"Date"] >= start,] +returns = cbind( + returns_mebeme, + returns_ind[,-1] +) +RF = factors_ff5[,7] + +# subtract the risk free and divide by 100 (to obtain excess returns not in percentage) +returns[, -1] = (returns[, -1] - RF) / 100 +returns = as.matrix(returns) + +usethis::use_data(returns, overwrite = TRUE) diff --git a/data-raw/make_risk_free.R b/data-raw/make_risk_free.R new file mode 100644 index 0000000..fa13e7b --- /dev/null +++ b/data-raw/make_risk_free.R @@ -0,0 +1,6 @@ +factors_ff5 <- read.csv("data-raw/F-F_Research_Data_5_Factors_2x3.csv") + +risk_free = as.matrix(factors_ff5[,c(1, 7)]) +risk_free[,2] = risk_free[,2] / 100 + +usethis::use_data(risk_free, overwrite = TRUE) diff --git a/data/factors.rda b/data/factors.rda index 3d8918ee2cb461aaa23d7cbbd4d6ec4fa85c6fd6..e73c808aa3dd23159852aaea8322aeaa3c096ff2 100644 GIT binary patch literal 11774 zcmd6tRa6|#^5=)(?iwJtdmy-b&;bT_cMI;$Fu23u?(XjH?oO}(L4zjQ-@SM5|6$MB zv#(p{Q}wMr{n8KpP+eUTy4L)h;yQHdI+t->Pyppc;NSn3?Q^UH2z@WUu(Yu1lDzQ} 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intrinsicFRP::factors[,-1] returns = intrinsicFRP::returns[,-1] +RF = intrinsicFRP::risk_free[,-1] # simulate a useless factor and add it to the matrix of factors set.seed(23) @@ -22,9 +24,49 @@ ff6 = 1:6 # "Mkt-RF" "SMB" "HML" "RMW" "CMA" "Mom" # model comprising the Fama-French 6 factors and the simulated useless factor ff6usl = 1:7 # "Mkt-RF" "SMB" "HML" "RMW" "CMA" "Mom" "Useless" -fm_sdf_coeff = intrinsicFRP::SDFCoefficients(returns, factors[,ff6usl], misspecification_robust = FALSE, include_standard_errors = TRUE) -gkr_sdf_coeff = intrinsicFRP::SDFCoefficients(returns, factors[,ff6usl], include_standard_errors = TRUE) -gkr_sdf_coeff = intrinsicFRP::SDFCoefficients(returns, factors[,ff6usl], include_standard_errors = TRUE, target_level_gkr2014_screening = 0.05) +fm_sdf = intrinsicFRP::SDFCoefficients( + returns, + factors[,ff6usl], + misspecification_robust = FALSE, + include_standard_errors = TRUE +) +gkr_sdf = intrinsicFRP::SDFCoefficients( + returns, factors[,ff6usl], + include_standard_errors = TRUE +) + +# create dataframe +df = data.frame( + Factor = factor( + rep(colnames(factors[,ff6usl]), 2), + levels = colnames(factors[,ff6usl]) + ), + Estimator = factor( + rep(c("FM", "GKR"), each=ncol(factors[,ff6usl])), + levels = c("FM", "GKR") + ), + sdf_coefficients = c(fm_sdf$sdf_coefficients, gkr_sdf$sdf_coefficients), + standard_errors = c(fm_sdf$standard_errors, gkr_sdf$standard_errors) +) + +# Create the plot +ggplot2::ggplot(df, ggplot2::aes( + x = as.factor(.data$Factor), y = .data$sdf_coefficients, fill = .data$Estimator)) + + ggplot2::theme(text=ggplot2::element_text(size=16)) + + ggplot2::geom_bar(stat = "identity", position = "dodge", width=0.5, color="black") + + ggplot2::labs(x = "Factor", y = "SDF coefficient") + + ggplot2::geom_errorbar(ggplot2::aes( + x=as.factor(Factor), + ymin=sdf_coefficients - stats::qnorm(0.975) * standard_errors, + ymax=sdf_coefficients + stats::qnorm(0.975) * standard_errors), + linewidth=.8, position = ggplot2::position_dodge(0.5), width = 0.25) + +ggplot2::ggsave( + "inst/examples/sdf_coefficients.png", + width = 7, + height = 5, + dpi=600 +) # compute tradable factor risk premia and their standard errors @@ -51,14 +93,14 @@ oracle_tfrp = intrinsicFRP::OracleTFRP( ) # create dataframe -df <- data.frame( +df = data.frame( Factor = factor( rep(colnames(factors[,ff6usl]), 4), levels = colnames(factors[,ff6usl]) ), Estimator = factor( - rep(c("FM-FRP", "KRS-FRP", "TFRP", "O-TFRP"), each=ncol(factors[,ff6usl])), - levels = c("FM-FRP", "KRS-FRP", "TFRP", "O-TFRP") + rep(c("FM", "KRS", "TFRP", "O-TFRP"), each=ncol(factors[,ff6usl])), + levels = c("FM", "KRS", "TFRP", "O-TFRP") ), risk_premia = c(fm_frp$risk_premia, krs_frp$risk_premia, tfrp$risk_premia, oracle_tfrp$risk_premia), standard_errors = c( @@ -92,7 +134,14 @@ which(oracle_tfrp$risk_premia != 0) intrinsicFRP::GKRFactorScreening(returns, factors[,ff6]) # compute the FGX factor screening procedure -intrinsicFRP::FGXFactorsTest(returns, factors[,ff3], factors[,4:7]) +gross_returns = returns + 1 + RF +fgx_output = intrinsicFRP::FGXFactorsTest(gross_returns, factors[,ff3], factors[,4:7]) + +alpha = 0.05 +quantile = stats::qnorm(1 - alpha / 2) +lows = fgx_output$sdf_coefficients - fgx_output$standard_errors * quantile +highs = fgx_output$sdf_coefficients + fgx_output$standard_errors * quantile +fgx_new_selected = which(lows * highs > 0) # compute the HJ misspecification distance of the Fama-French 3 and 6 factor models intrinsicFRP::HJMisspecificationDistance(returns, factors[,ff3]) diff --git a/inst/examples/risk_premia.png b/inst/examples/risk_premia.png index 4d21d83f0f46c2e65bccffec545dcfb5bf64472c..fe15cdc0bd9ea9ec7a3350b1f644933543f30abd 100644 GIT binary patch literal 156245 zcmeFa2RPR4`#0@B13(b&l70zRt^2R$6@JvQ5iqXlPbS zN*q5!L$mrK4bAfOCG+qvyOR?;@W-O_QsT#HX375^=0yAAUzVCns9Ms{Xq+Jb|6}dF zV@GIcHq%HRKca9sxToI4_`dD<;_sFUR!}?&4n~Ma$oVs3qito$E 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