The goal of ycevo is to provide means for the non-parametric
estimation of the discount function and yield curve of bonds.
If you use any data or code from the ycevo package CRAN release in a
publication, please use the following citation:
Bonsoo Koo, and Yangzhuoran Fin Yang (2024). ycevo: Non-Parametric Estimation of the Yield Curve Evolution. R package version 0.2.1. https://CRAN.R-project.org/package=ycevo.
The package provides code used in Koo, La Vecchia, & Linton (2021). Please use the following citation if you use any result from the paper.
Koo, B., La Vecchia, D., & Linton, O. (2021). Estimation of a nonparametric model for bond prices from cross-section and time series information. Journal of Econometrics, 220(2), 562-588.
The package is in active development and it have been experiencing substantial changes. Since the existing forks have remained inactive for several years, significant modifications have been implemented through rebase. It is advisable for any existing forks to undergo the rebase process.
git remote add upstream https://github.com/bonsook/ycevo.git
git fetch upstream
git rebase upstream/master
git push origin master --force
You can install the released version of ycevo from CRAN with:
install.packages("ycevo")You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("bonsook/ycevo")library(ycevo)
library(lubridate)
#>
#> Attaching package: 'lubridate'
#> The following objects are masked from 'package:base':
#>
#> date, intersect, setdiff, union
## Simulate
set.seed(1)
bonds <- ycevo_data()
bonds
#> # A tibble: 388,795 × 5
#> qdate id price tupq pdint
#> <date> <fct> <dbl> <dbl> <dbl>
#> 1 2023-01-02 20230106.106643 103. 4 103.
#> 2 2023-01-02 20230721.107768 106. 19 3.88
#> 3 2023-01-02 20230721.107768 106. 200 104.
#> 4 2023-01-02 20240203.106386 106. 32 3.19
#> 5 2023-01-02 20240203.106386 106. 213 3.19
#> 6 2023-01-02 20240203.106386 106. 397 103.
#> 7 2023-01-02 20240817.109392 113. 46 4.70
#> 8 2023-01-02 20240817.109392 113. 227 4.70
#> 9 2023-01-02 20240817.109392 113. 411 4.70
#> 10 2023-01-02 20240817.109392 113. 593 105.
#> # ℹ 388,785 more rows## Estimate at specified time points
x <- seq(ymd("2023-03-01"), ymd("2023-08-01"), by = "1 month")
res <- ycevo(bonds, x)
## Supports parallel processing and progress bar
# future::plan(future::multisession)
# res <- progressr::with_progress(ycevo(bonds, x))res
#> # A tibble: 6 × 2
#> qdate .est
#> * <date> <list>
#> 1 2023-03-01 <tibble [73 × 3]>
#> 2 2023-04-01 <tibble [73 × 3]>
#> 3 2023-05-01 <tibble [73 × 3]>
#> 4 2023-06-01 <tibble [73 × 3]>
#> 5 2023-07-01 <tibble [73 × 3]>
#> 6 2023-08-01 <tibble [73 × 3]>## Predict
augment(res)
#> # A tibble: 438 × 4
#> qdate tau .discount .yield
#> <date> <dbl> <dbl> <dbl>
#> 1 2023-03-01 0.0822 0.986 0.169
#> 2 2023-03-01 0.164 0.985 0.0912
#> 3 2023-03-01 0.247 0.984 0.0654
#> 4 2023-03-01 0.329 0.983 0.0528
#> 5 2023-03-01 0.411 0.982 0.0454
#> 6 2023-03-01 0.493 0.980 0.0407
#> 7 2023-03-01 0.658 0.977 0.0351
#> 8 2023-03-01 0.822 0.974 0.0321
#> 9 2023-03-01 0.986 0.970 0.0305
#> 10 2023-03-01 1.15 0.967 0.0296
#> # ℹ 428 more rows
## Plot
autoplot(res)
## Compare to the true yield curve
curve_true <- list()
for(i in seq_along(x)) {
curve_true[[i]] <- tibble(
tau=1:20,
qdate = x[[i]],
.yield = get_yield_at((x[[i]] - ymd("2023-01-01"))/365, tau),
.discount = exp(-tau * .yield))
}
curve_true <- bind_rows(curve_true)
curve_true <- pivot_longer(
curve_true,
c(.discount, .yield),
names_to = ".est",
values_to = ".value")
autoplot(res) +
geom_line(data = curve_true, linetype = "dashed")This package is free and open source software, licensed under GPL-3