Tools for fixed income calculations.
To declare an annual spot rate with a simple compounding and an actual/360 day count.
as.spotrate(0.06, 'simple', 'actual/360')
# 0.06 simple actual/360 Compound the spot rate for 7 months.
sr <- as.spotrate(0.06, 'simple', 'actual/360')
compound(sr, '7 months')
# [1] 1.035Spot rates can be created using a string representation
as.spotrate('0.06 discrete actual/365')
# 0.06 discrete actual/365Let's create a spot rate curve.
Firstly you need to create a spotrate vector and associate it to terms by creating a spotratecurve.
rates <- as.spotrate(c(0.0719, 0.056, 0.0674, 0.0687, 0.07), 'simple', 'actual/365')
curve <- as.spotratecurve(c(1, 11, 26, 47, 62), rates, units='days', name='TS')
curve
## TS
## 1 days 0.0719
## 11 days 0.0560
## 26 days 0.0674
## 47 days 0.0687
## 62 days 0.0700
## simple actual/365 require(ggplot2)
ggplot(data=as.data.frame(curve), aes(x=terms, y=rates)) + geom_line() + geom_point()
Bonds can be created using a data.frame and spotrate can be set as one of columns.
# Declaring bonds
dc <- as.daycount('actual/360') # daycount rule
comp <- as.compounding('continuous') # compounding regime
days <- c(97, 242, 321) # days to maturity
sr <- as.spotrate(rep(0.06, length(days)), comp, dc) # discount rate
bonds <- data.frame(DaysToMaturity=days, Rate=sr, Notional=100000)
bonds
# DaysToMaturity Rate Notional
# 1 97 0.06 discrete business/252 ANBIMA 1e+05
# 2 242 0.06 discrete business/252 ANBIMA 1e+05
# 3 321 0.06 discrete business/252 ANBIMA 1e+05
# Pricing bond -- discounting their notional value
within(bonds, {
PV <- Notional*discount(Rate, DaysToMaturity)
})
# DaysToMaturity Rate Notional PV
# 1 97 0.06 continuous actual/360 1e+05 98396.33
# 2 242 0.06 continuous actual/360 1e+05 96046.92
# 3 321 0.06 continuous actual/360 1e+05 94790.59library(bizdays)
cal <- Calendar(holidays=holidaysANBIMA, name='ANBIMA', weekdays=c('saturday', 'sunday'))
dc <- as.daycount('business/252')
comp <- as.compounding('discrete')
dates <- as.Date(c('2014-09-07', '2015-03-07', '2015-09-07'))
sr <- as.spotrate(rep(0.06, length(dates)), comp, dc, cal)
bonds <- data.frame(RefDate=as.Date('2014-03-21'), Maturity=dates, Rate=sr, Notional=100000)
bonds
# RefDate Maturity Rate Notional
# 1 2014-03-21 2014-09-07 0.06 discrete business/252 ANBIMA 1e+05
# 2 2014-03-21 2015-03-07 0.06 discrete business/252 ANBIMA 1e+05
# 3 2014-03-21 2015-09-07 0.06 discrete business/252 ANBIMA 1e+05
within(bonds, {
PV <- Notional*discount(Rate, from=RefDate, to=Maturity)
})
# RefDate Maturity Rate Notional PV
# 1 2014-03-21 2014-09-07 0.06 discrete business/252 ANBIMA 1e+05 97353.43
# 2 2014-03-21 2015-03-07 0.06 discrete business/252 ANBIMA 1e+05 94558.01
# 3 2014-03-21 2015-09-07 0.06 discrete business/252 ANBIMA 1e+05 91842.86- new names:
- spotrate
- zerocurve
- fwdcurve
- indexcurve
- historicaldata: zoo
- create 30/360 daycount
- create spot rate curve
- append rates to an spot rate curve
- set interpolation to curve, as an attribute: why interpolate explicitly when the curve knows which interpolation it should use
- vectorized operations for SpotRate functions
- functions to sum a curve
- function to shift a curve
- plot interest rate
- plot spot rate curve with interpolated points
- as.data.frame.spotratecurve
- other interpolation methods
- Linear
- LogLinear
- Monotone Cubic Spline (Hyman)
- Hermite
- Natural Spline
- Constrained Spline
- create an interp class
- implement print method
- interp is a spotratecurve attribute
- length method for spotratecurve
- The spotratecurve should have the same parameters as spotrate
- spotratecurve should have a name
- spotratecurve should be sliced
- head(src, n)
- tail(src, n)
- CHECK src[1:10]
- src[1:10, offset=1] -> daily forward curve
- how to compute forward curves
- A SpotRate should be removed from a SpotRateCurve: curve[-10]
- Not so simple: curve[n] returns a rate
- curve[-n] should return a SpotRateCurve without the removed SpotRate
- So this doesn't fit, different returns for the same operation
- Proposal: curve[n] <- NA or curve[n] <- NULL
- SpotRateCurve should be a matrix, terms would be rownames, rates values and the rest attributes, like xts
- dim
- dimnames(curve) <- list(terms, curve_name)
- ???
- Need a price curve: indexcurve, pricecurve
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