From f578d08515dbdc49103e4a12f4d1ba2050f4f10f Mon Sep 17 00:00:00 2001 From: wilsonfreitas Date: Fri, 15 Jul 2022 15:40:50 -0300 Subject: [PATCH] Updated README --- README.md | 205 +++++++++++++--------- man/figures/README-unnamed-chunk-13-1.png | Bin 7058 -> 6496 bytes man/figures/README-unnamed-chunk-15-1.png | Bin 10200 -> 10000 bytes man/figures/README-unnamed-chunk-17-1.png | Bin 7717 -> 6993 bytes 4 files changed, 119 insertions(+), 86 deletions(-) diff --git a/README.md b/README.md index 2353f8b..25798de 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,4 @@ + # fixedincome @@ -25,13 +26,17 @@ with interest rates using `fixedincome`. You can install from CRAN with: - install.packages("fixedincome") +``` r +install.packages("fixedincome") +``` You can install the development version of fixedincome from [GitHub](https://github.com/) with: - # install.packages("devtools") - devtools::install_github("wilsonfreitas/R-fixedincome") +``` r +# install.packages("devtools") +devtools::install_github("wilsonfreitas/R-fixedincome") +``` ## Examples @@ -57,148 +62,176 @@ restriction can be reviewed in the future. Given that let’s declare an annual spot rate with a `simple` compounding, an `actual/360` and the `actual` calendar. - library(fixedincome) - sr <- spotrate(0.06, "simple", "actual/360", "actual") - sr - #> [1] "0.06 simple actual/360 actual" +``` r +library(fixedincome) +sr <- spotrate(0.06, "simple", "actual/360", "actual") +sr +#> [1] "0.06 simple actual/360 actual" +``` Compound the spot rate for 7 months. - compound(sr, 7, "months") - #> [1] 1.035 +``` r +compound(sr, 7, "months") +#> [1] 1.035 +``` Also compound using dates. - compound(sr, as.Date("2022-02-23"), as.Date("2022-12-28")) - #> [1] 1.051333 +``` r +compound(sr, as.Date("2022-02-23"), as.Date("2022-12-28")) +#> [1] 1.051333 +``` Spot rates can be put inside data.frames. - library(dplyr) - library(fixedincome) - - df <- tibble( - rate = spotrate(rep(10.56 / 100, 5), - compounding = "discrete", - daycount = "business/252", - calendar = "Brazil/ANBIMA" - ), - terms = term(1:5, "years") - ) - - df - #> # A tibble: 5 x 2 - #> rate terms - #> - #> 1 0.1056 discrete business/252 Brazil/ANBIMA 1 year - #> 2 0.1056 discrete business/252 Brazil/ANBIMA 2 years - #> 3 0.1056 discrete business/252 Brazil/ANBIMA 3 years - #> 4 0.1056 discrete business/252 Brazil/ANBIMA 4 years - #> 5 0.1056 discrete business/252 Brazil/ANBIMA 5 years +``` r +library(dplyr) +library(fixedincome) + +df <- tibble( + rate = spotrate(rep(10.56 / 100, 5), + compounding = "discrete", + daycount = "business/252", + calendar = "Brazil/ANBIMA" + ), + terms = term(1:5, "years") +) + +df +#> # A tibble: 5 x 2 +#> rate terms +#> +#> 1 0.1056 discrete business/252 Brazil/ANBIMA 1 year +#> 2 0.1056 discrete business/252 Brazil/ANBIMA 2 years +#> 3 0.1056 discrete business/252 Brazil/ANBIMA 3 years +#> 4 0.1056 discrete business/252 Brazil/ANBIMA 4 years +#> 5 0.1056 discrete business/252 Brazil/ANBIMA 5 years +``` The tidyverse verbs can be easily used with `SpotRate` and `Term` classes. - df |> mutate(fact = compound(rate, terms)) - #> # A tibble: 5 x 3 - #> rate terms fact - #> - #> 1 0.1056 discrete business/252 Brazil/ANBIMA 1 year 1.11 - #> 2 0.1056 discrete business/252 Brazil/ANBIMA 2 years 1.22 - #> 3 0.1056 discrete business/252 Brazil/ANBIMA 3 years 1.35 - #> 4 0.1056 discrete business/252 Brazil/ANBIMA 4 years 1.49 - #> 5 0.1056 discrete business/252 Brazil/ANBIMA 5 years 1.65 +``` r +df |> mutate(fact = compound(rate, terms)) +#> # A tibble: 5 x 3 +#> rate terms fact +#> +#> 1 0.1056 discrete business/252 Brazil/ANBIMA 1 year 1.11 +#> 2 0.1056 discrete business/252 Brazil/ANBIMA 2 years 1.22 +#> 3 0.1056 discrete business/252 Brazil/ANBIMA 3 years 1.35 +#> 4 0.1056 discrete business/252 Brazil/ANBIMA 4 years 1.49 +#> 5 0.1056 discrete business/252 Brazil/ANBIMA 5 years 1.65 +``` ### Spot rate curves Let’s create a spot rate curve using web scraping (from B3 website) - source("examples/utils-functions.R") - curve <- get_curve_from_web("2022-02-23") - curve - #> SpotRateCurve - #> 1 day 0.1065 - #> 3 days 0.1064 - #> 25 days 0.1111 - #> 44 days 0.1138 - #> 66 days 0.1168 - #> 87 days 0.1189 - #> 108 days 0.1207 - #> 131 days 0.1220 - #> 152 days 0.1227 - #> 172 days 0.1235 - #> # ... with 29 more rows - #> discrete business/252 Brazil/ANBIMA - #> Reference date: 2022-02-23 +``` r +source("examples/utils-functions.R") +curve <- get_curve_from_web("2022-02-23") +curve +#> SpotRateCurve +#> 1 day 0.1065 +#> 3 days 0.1064 +#> 25 days 0.1111 +#> 44 days 0.1138 +#> 66 days 0.1168 +#> 87 days 0.1189 +#> 108 days 0.1207 +#> 131 days 0.1220 +#> 152 days 0.1227 +#> 172 days 0.1235 +#> # ... with 29 more rows +#> discrete business/252 Brazil/ANBIMA +#> Reference date: 2022-02-23 +``` `SpotRateCurve` plots can be easily done by calling `plot`. - plot(curve) +``` r +plot(curve) +``` For another date. - curve <- get_curve_from_web("2011-02-23") - plot(curve) +``` r +curve <- get_curve_from_web("2011-02-23") +plot(curve) +``` It can show the forward rates for the short term by selecting the first two years. - curve <- get_curve_from_web("2022-02-23") - plot(fixedincome::first(curve, "2 years"), show_forward = TRUE) +``` r +curve <- get_curve_from_web("2022-02-23") +plot(fixedincome::first(curve, "2 years"), show_forward = TRUE) +``` Once interpolation is set, it can be used in the plot. - interpolation(curve) <- interp_flatforward() - plot( - fixedincome::first(curve, "2 years"), - use_interpolation = TRUE, legend_location = "bottomright" - ) +``` r +interpolation(curve) <- interp_flatforward() +plot( + fixedincome::first(curve, "2 years"), + use_interpolation = TRUE, legend_location = "bottomright" +) +``` Parametric models like the Nelson-Siegel-Svensson model can be fitted to the curve. - interpolation(curve) <- fit_interpolation( - interp_nelsonsiegelsvensson(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), curve - ) +``` r +interpolation(curve) <- fit_interpolation( + interp_nelsonsiegelsvensson(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), curve +) - interpolation(curve) - #> - #> Parameters: - #> beta1 beta2 beta3 beta4 lambda1 lambda2 - #> 0.09283 0.01325 0.04613 0.05007 0.00022 0.01092 +interpolation(curve) +#> +#> Parameters: +#> beta1 beta2 beta3 beta4 lambda1 lambda2 +#> 0.300 -0.181 -0.858 0.611 0.053 0.054 +``` Once set to the curve it is used in the plot to show daily forward rates. - plot(curve, use_interpolation = TRUE, show_forward = TRUE) +``` r +plot(curve, use_interpolation = TRUE, show_forward = TRUE) +``` The interpolation can be changed in order to compare different interpolations and the effects in forward rates. - interpolation(curve) <- interp_flatforward() - plot( - curve, - use_interpolation = TRUE, show_forward = TRUE, - legend_location = "bottomright" - ) +``` r +interpolation(curve) <- interp_flatforward() +plot( + curve, + use_interpolation = TRUE, show_forward = TRUE, + legend_location = "bottomright" +) +``` Interpolation enables the creation of standardized curves, commonly used in risk management to build risk factors. - risk_terms <- c(1, c(3, 6, 9) * 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