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https://github.com/wilsonfreitas/R-fixedincome

Fixed income tools for R
https://github.com/wilsonfreitas/R-fixedincome

bonds calendar finance fixed-income interpolation plot pricing-bonds r spot-rate-curve

Last synced: 3 months ago
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Fixed income tools for R

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README 

        
---

output: github_document

---



```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```



# fixedincome 



[![CRAN status](https://www.r-pkg.org/badges/version/fixedincome)](https://CRAN.R-project.org/package=fixedincome)

[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)

[![R-CMD-check](https://github.com/wilsonfreitas/R-fixedincome/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/wilsonfreitas/R-fixedincome/actions/workflows/R-CMD-check.yaml)



Calculations involving interest rates are usually very easy and straightforward,

but sometimes it involves specific issues that makes the task of writing

structured and reproducible code for it chalenging and annoying.

The `fixedincome` package brings many functions to strucutre and create

facilities to handle with interest rates, term structure of interest rates and

specific issues regarding compounding rates and day count rules, for example.



Below there are a few examples on how to create and make calculations with

interest rates using `fixedincome`.



## Installation



You can install from CRAN with:



```{r eval=FALSE}

install.packages("fixedincome")

```



You can install the development version of fixedincome from [GitHub](https://github.com/) with:



```{r eval=FALSE}

# install.packages("devtools")

devtools::install_github("wilsonfreitas/R-fixedincome")

```



## Examples



To create an interest rate we need to specify 4 elements:



- the value of the interest rate itself, a decimal number

- the compounding regime of interest rate, that can be `simple`, `discrete` or

`continuous`.

- the day count rule which defines how interest is accrued over time, we have a

few options, for example, `actual/360` where the days between two dates

are calculated as the difference and the year is assumed to be 360 days.

- the calendar used to count the number of days between two dates,

we have `actual` calendar that compute the difference between two dates.



There is another important topic that wasn't declared here that is the

*frequency* of interest.

To start with the things simple `fixedincome` handles only with *annual* rates

since this represents the great majority of rates used in financial market 

contracts, but this 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.



```{r}

library(fixedincome)

sr <- spotrate(0.06, "simple", "actual/360", "actual")

sr

```



Compound the spot rate for 7 months.



```{r}

compound(sr, 7, "months")

```



Also compound using dates.



```{r}

compound(sr, as.Date("2022-02-23"), as.Date("2022-12-28"))

```



Spot rates can be put inside data.frames.



```{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

```



The tidyverse verbs can be easily used with `SpotRate` and `Term` classes.



```{r}

df |> mutate(fact = compound(rate, terms))

```



### Spot rate curves



Let's create a spot rate curve using web scraping (from B3 website)



```{r}

source("examples/utils-functions.R")

curve <- get_curve_from_web("2022-02-23")

curve

```



`SpotRateCurve` plots can be easily done by calling `plot`.



```{r}

plot(curve)

```



For another date.



```{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.



```{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.



```{r}

curve_2y <- fixedincome::first(curve, "2 years")

interpolation(curve_2y) <- interp_flatforward()

plot(curve_2y, use_interpolation = TRUE, legend_location = "bottomright")

```



Parametric models like the Nelson-Siegel-Svensson model can be fitted to the curve.



```{r}

beta1 <- as.numeric(fixedincome::last(curve, "1 day"))

beta2 <- as.numeric(curve[1]) - beta1

interpolation(curve) <- fit_interpolation(

  interp_nelsonsiegelsvensson(beta1, beta2, 0.01, 0.01, 2, 1), curve

)



interpolation(curve)

```



Once set to the curve it is used in the plot to show daily forward rates.



```{r}

plot(curve, use_interpolation = TRUE, show_forward = TRUE, legend_location = "bottom")

```



The interpolation can be changed in order to compare different interpolations

and the effects in forward rates.



```{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.



```{r}

risk_terms <- c(1, c(3, 6, 9) * 21, c(1, 5, 10) * 252)

risk_curve <- curve[[risk_terms]]

interpolation(risk_curve) <- interp_flatforward()

plot(risk_curve, use_interpolation = TRUE)

```