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`

.

You can install from CRAN with:

`install.packages("fixedincome")`

You can install the development version of fixedincome from GitHub with:

```
# install.packages("devtools")
::install_github("wilsonfreitas/R-fixedincome") devtools
```

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.

```
library(fixedincome)
<- spotrate(0.06, "simple", "actual/360", "actual")
sr
sr#> [1] "0.06 simple actual/360 actual"
```

Compound the spot rate for 7 months.

```
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
```

Spot rates can be put inside data.frames.

```
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:fixedincome':
#>
#> first, last
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(fixedincome)
<- tibble(
df 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
#> <SpotRate> <Term>
#> 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.

```
|> mutate(fact = compound(rate, terms))
df #> # A tibble: 5 x 3
#> rate terms fact
#> <SpotRate> <Term> <dbl>
#> 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
```

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

```
source("examples/utils-functions.R")
#>
#> Attaching package: 'bizdays'
#> The following object is masked from 'package:stats':
#>
#> offset
<- get_curve_from_web("2022-02-23")
curve
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)`

For another date.

```
<- get_curve_from_web("2011-02-23")
curve plot(curve)
```

It can show the forward rates for the short term by selecting the first two years.

```
<- get_curve_from_web("2022-02-23")
curve plot(fixedincome::first(curve, "2 years"), show_forward = TRUE)
```

Once interpolation is set, it can be used in the plot.

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

```
<- as.numeric(fixedincome::last(curve, "1 day"))
beta1 <- as.numeric(curve[1]) - beta1
beta2 interpolation(curve) <- fit_interpolation(
interp_nelsonsiegelsvensson(beta1, beta2, 0.01, 0.01, 2, 1), curve
)
interpolation(curve)
#> <Interpolation: nelsonsiegelsvensson >
#> Parameters:
#> beta1 beta2 beta3 beta4 lambda1 lambda2
#> 0.119 -0.013 1.000 -0.975 1.195 1.122
```

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

`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.

```
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.

```
<- c(1, c(3, 6, 9) * 21, c(1, 5, 10) * 252)
risk_terms <- curve[[risk_terms]]
risk_curve interpolation(risk_curve) <- interp_flatforward()
plot(risk_curve, use_interpolation = TRUE)
```