Package 'vamc' reference manual

Title:	A Monte Carlo Valuation Framework for Variable Annuities
Description:	Implementation of a Monte Carlo simulation engine for valuing synthetic portfolios of variable annuities, which reflect realistic features of common annuity contracts in practice. It aims to facilitate the development and dissemination of research related to the efficient valuation of a portfolio of large variable annuities. The main valuation methodology was proposed by Gan (2017) <doi:10.1515/demo-2017-0021>.
Authors:	Hengxin Li [aut, cph], Ben Feng [aut, cph], Mingyi Jiang [aut, cph, cre], GuoJun Gan [ctb]
Maintainer:	Mingyi Jiang <[email protected]>
License:	GPL-2
Version:	0.2.1
Built:	2025-03-08 03:00:25 UTC
Source:	https://github.com/cran/vamc

Age One Policy

Description

Age a VA policy specified in inPolicy from currentDate (specified in inPolicy) to targetDate. The againg scenario is given in fundScen. The time step length is specified in dT. Here we input a rather irrelevant parameter df to "hack" for a more flexible user-defined projection function.

Usage

ageOnePolicy(
  inPolicy,
  mortTable,
  fundScen,
  scenDates,
  dT = 1/12,
  targetDate,
  df
)
ageOnePolicy(
  inPolicy,
  mortTable,
  fundScen,
  scenDates,
  dT = 1/12,
  targetDate,
  df
)

Arguments

`inPolicy`	A vector containing 45 attributes of a VA policy, usually a row of a VA portfolio dataframe.
`mortTable`	A dataframe with three columns of doubles representing the mortality table.
`fundScen`	A numScen-by-numStep-by-numFund array of doubles of return factors (i.e., exp(mu_t dt)) in each period.
`scenDates`	A vector containing strings in the format of "YYYY-MM-DD" of dates corresponding to each period in fundScen.
`dT`	A double of stepsize in years; dT = 1 / 12 would be monthly.
`targetDate`	A string in the format of "YYYY-MM-DD" of valuation date of the portfolio.
`df`	A vector of doubles of risk-free discount rates of different tenor (not forward rates), should have length being numStep.

Value

Outputs a vector containing 45 attributes of a VA policy, where currentDate, gbAmt, GMWBbalance, withdrawal, & fundValue could be updated as a result of aging. Usually a row of a VA portfolio dataframe.

Note

Target date MUST be PRIOR to the last date of historical scenario date, Current date MUST be LATER than the first date of historical scenario date.

Examples

exPolicy <- VAPort[1, ]
targetDate <- "2016-01-01"
histFundScen <- genFundScen(fundMap, histIdxScen)
ageOnePolicy(exPolicy, mortTable, histFundScen, histDates, dT = 1 / 12,
targetDate, cForwardCurve)
## Not run: 
targetDate <- "2001-01-01"
histFundScen <- genFundScen(fundMap, histIdxScen)
ageOnePolicy(exPolicy, mortTable, histFundScen, histDates, dT = 1 / 12,
targetDate, cForwardCurve)

## End(Not run)
## Not run: 
exPolicy <- VAPort[1, ]
exPolicy[1, c("currentDate", "issueDate")] <- c("2001-01-01", "2001-01-01")
histFundScen <- genFundScen(fundMap, histIdxScen)
ageOnePolicy(exPolicy, mortTable, histFundScen, histDates, dT = 1 / 12,
targetDate, cForwardCurve)

## End(Not run)
exPolicy <- VAPort[1, ]
targetDate <- "2016-01-01"
histFundScen <- genFundScen(fundMap, histIdxScen)
ageOnePolicy(exPolicy, mortTable, histFundScen, histDates, dT = 1 / 12,
targetDate, cForwardCurve)
## Not run: 
targetDate <- "2001-01-01"
histFundScen <- genFundScen(fundMap, histIdxScen)
ageOnePolicy(exPolicy, mortTable, histFundScen, histDates, dT = 1 / 12,
targetDate, cForwardCurve)

## End(Not run)
## Not run: 
exPolicy <- VAPort[1, ]
exPolicy[1, c("currentDate", "issueDate")] <- c("2001-01-01", "2001-01-01")
histFundScen <- genFundScen(fundMap, histIdxScen)
ageOnePolicy(exPolicy, mortTable, histFundScen, histDates, dT = 1 / 12,
targetDate, cForwardCurve)

## End(Not run)

Age a Portfolio

Description

Age a portfolio of VA policies specified in each inPolicy of inPortfolio from currentDate (specified in inPolicy) to targetDate. The againg scenario is given in fundScen. The time step length is specified in dT. Here we input a rather irrelevant parameter df to "hack" for a more flexible user-defined projection function.

Usage

agePortfolio(
  inPortfolio,
  mortTable,
  fundScen,
  scenDates,
  dT = 1/12,
  targetDate,
  df
)
agePortfolio(
  inPortfolio,
  mortTable,
  fundScen,
  scenDates,
  dT = 1/12,
  targetDate,
  df
)

Arguments

`inPortfolio`	A dataframe containing numPolicy rows and 45 attributes of each VA policy.
`mortTable`	A dataframe with three columns of doubles representing the mortality table.
`fundScen`	A numScen-by-numStep-by-numFund array of doubles of return factors (i.e., exp(mu_t dt)) in each period.
`scenDates`	A vector containing strings in the format of "YYYY-MM-DD" of dates corresponding to each period in fundScen.
`dT`	A double of stepsize in years; dT = 1 / 12 would be monthly.
`targetDate`	A string in the format of "YYYY-MM-DD" of valuation date of the portfolio.
`df`	A vector of doubles of risk-free discount rates of different tenor (not forward rates), should have length being numStep.

Value

Outputs a dataframe containing numPolicy rows and 45 attributes of each VA policy, where currentDate, gbAmt, GMWBbalance, withdrawal, & fundValue of each policy could be updated as a result of aging.

Note

Target date MUST be PRIOR to the last date of historical scenario date, Current date MUST be LATER than the first date of historical scenario date.

Examples

targetDate <- "2016-01-01"
histFundScen <- genFundScen(fundMap, histIdxScen)
agePortfolio(VAPort[1:2, ], mortTable, histFundScen, histDates, dT = 1 / 12,
targetDate, cForwardCurve)
## Not run: 
targetDate <- "2001-01-01"
histFundScen <- genFundScen(fundMap, histIdxScen)
agePortfolio(VAPort, mortTable, histFundScen, histDates, dT = 1 / 12,
targetDate, cForwardCurve)

## End(Not run)
## Not run: 
VAPort[1, c("currentDate", "issueDate")] <- c("2001-01-01", "2001-01-01")
histFundScen <- genFundScen(fundMap, histIdxScen)
agePortfolio(VAPort, mortTable, histFundScen, histDates, dT = 1 / 12,
targetDate, cForwardCurve)

## End(Not run)
targetDate <- "2016-01-01"
histFundScen <- genFundScen(fundMap, histIdxScen)
agePortfolio(VAPort[1:2, ], mortTable, histFundScen, histDates, dT = 1 / 12,
targetDate, cForwardCurve)
## Not run: 
targetDate <- "2001-01-01"
histFundScen <- genFundScen(fundMap, histIdxScen)
agePortfolio(VAPort, mortTable, histFundScen, histDates, dT = 1 / 12,
targetDate, cForwardCurve)

## End(Not run)
## Not run: 
VAPort[1, c("currentDate", "issueDate")] <- c("2001-01-01", "2001-01-01")
histFundScen <- genFundScen(fundMap, histIdxScen)
agePortfolio(VAPort, mortTable, histFundScen, histDates, dT = 1 / 12,
targetDate, cForwardCurve)

## End(Not run)

Build Curve

Description

Bootstrap discount factors from a yield curve.

Usage

buildCurve(
  swapRates,
  tenors,
  fixFreq = 6,
  fixDCC = "Thirty360",
  fltFreq = 6,
  fltDCC = "Thirty360",
  calendar = "General",
  bdc = c("Actual", "Preceding", "Following", "Modified_Prec", "Modified_Foll"),
  curveDate,
  numSetDay,
  yieldCurveDCC = "Thirty360",
  holidays = NULL
)
buildCurve(
  swapRates,
  tenors,
  fixFreq = 6,
  fixDCC = "Thirty360",
  fltFreq = 6,
  fltDCC = "Thirty360",
  calendar = "General",
  bdc = c("Actual", "Preceding", "Following", "Modified_Prec", "Modified_Foll"),
  curveDate,
  numSetDay,
  yieldCurveDCC = "Thirty360",
  holidays = NULL
)

Arguments

`swapRates`	A vector of doubles of swap rates.
`tenors`	A vector of integers of corresponding tenors.
`fixFreq`	An integer of fixed leg frequency of payment in months. Default is 6, semi-annual payments.
`fixDCC`	A string of fixed leg day count convention from four options: "Thirty360", "ACT360", "ACT365", or "ACTACT". Default is "Thirty360".
`fltFreq`	An integer of floating leg frequency of payment in months. Default is 6, semi-annual payments.
`fltDCC`	A string of floating leg day count convention from four options: "Thirty360", "ACT360", "ACT365", or "ACTACT". Default is "Thirty360".
`calendar`	A string of the desired calendar convention from two options: "NY": New York holiday calendar "General": all weekdays are business days
`bdc`	A string of business day convention from five options: "Actual": No rolling on the date applied even if it is a non-business day "Preceding": 1st business day before holiday "Following": 1st business day after holiday "Modified_Prec": Same as "Preceding" unless it belongs to a different month, in which case 1st business day after holiday "Modified_Foll": Same as "Following" unless it belongs to a different month, in which case 1st business day before holiday Default is "Actual".
`curveDate`	A string in the format of "YYYY-MM-DD" of yield curve date.
`numSetDay`	An integer of settlement days from yield curve date.
`yieldCurveDCC`	A string of yield curve day count convention from four options: "Thirty360", "ACT360", "ACT365", or "ACTACT". Default is "Thirty360".
`holidays`	An optional vector dates of user-defined holidays. If provided, within the given holidays range, the calendar provided in the parameter "calendar" will not be applied; If the date is not in the given holidays range, it will follow the calendar provided in the "calendar" parameter

Value

Outputs a data frame of strings of discount dates and doubles of discount factors.

Examples

rate <- c(0.69, 0.77, 0.88, 1.01, 1.14, 1.38, 1.66, 2.15) * 0.01
tenor <- c(1, 2, 3, 4, 5, 7, 10, 30)
fixFreq <- 6
fixDCC <- "Thirty360"
fltFreq <- 6
fltDCC <- "ACT360"
calendar <- "NY"
bdc <- "Modified_Foll"
curveDate <- "2016-02-08"
numSetDay <- 2
yieldCurveDCC <- "Thirty360"
holidays <- NULL
buildCurve(rate, tenor, fixFreq, fixDCC, fltFreq, fltDCC, calendar, bdc,
           curveDate, numSetDay, yieldCurveDCC, holidays)
rate <- c(0.69, 0.77, 0.88, 1.01, 1.14, 1.38, 1.66, 2.15) * 0.01
tenor <- c(1, 2, 3, 4, 5, 7, 10, 30)
fixFreq <- 6
fixDCC <- "Thirty360"
fltFreq <- 6
fltDCC <- "ACT360"
calendar <- "NY"
bdc <- "Modified_Foll"
curveDate <- "2016-02-08"
numSetDay <- 2
yieldCurveDCC <- "Thirty360"
holidays <- NULL
buildCurve(rate, tenor, fixFreq, fixDCC, fltFreq, fltDCC, calendar, bdc,
           curveDate, numSetDay, yieldCurveDCC, holidays)

Calculate Mortality Factors

Description

Calculates the mortality factors (t - 1)px q(x + t - 1) and tpx required to valuate the inPolicy. Extract gender, age (birth date & current date), valuation date (current date), and maturity date from inPolicy, mortality rates from mortTable.

Usage

calcMortFactors(inPolicy, mortTable, dT = 1/12)
calcMortFactors(inPolicy, mortTable, dT = 1/12)

Arguments

`inPolicy`	A vector containing 45 attributes of a VA policy, usually a row of a VA portfolio dataframe.
`mortTable`	A dataframe with three columns of doubles representing the mortality table.
`dT`	A double of stepsize in years; dT = 1 / 12 would be monthly.

Value

Outputs a two-column data frame of doubles of mortFactors (t - 1)px q(x + t - 1) and tpx.

Examples

exPolicy <- VAPort[1, ]
calcMortFactors(exPolicy, mortTable, dT = 1 / 12)
exPolicy <- VAPort[1, ]
calcMortFactors(exPolicy, mortTable, dT = 1 / 12)

Constant Forward Curve

Description

A dataset containing 2 percent continuously compounded annual interest rate for illustration purposes.

Usage

cForwardCurve
cForwardCurve

Format

A vector with 360 elements:

rate: discount rate

...

Fund Map for 10 Funds

Description

A dataset containing a default mapping from five indices to ten different funds.

Usage

fundMap
fundMap

Format

A matrix with 10 rows and 5 columns:

index name: name for each index
fund number: proportion of fund allocated to a particular index

...

Generate Fund Scenerio

Description

Calculate numScen-by-numStep-by-numFund fund scenarios based on given index scenarios indexScen and fund map fundMap that maps indices to funds.

Usage

genFundScen(fundMap, indexScen)
genFundScen(fundMap, indexScen)

Arguments

`fundMap`	A numFund-by-numIndex matrix of doubles, mapping indices to funds.
`indexScen`	A numScen-by-numStep-by-numIndex array of doubles, index scenarios.

Value

Outputs a numScen-by-numStep-by-numFund array of doubles of fund scenarios.

Examples

genFundScen(fundMap, indexScen)
genFundScen(fundMap, indexScen)

Generate Index Scenerio

Description

Simulate a 3D array, numScen-by-numStep-by-numIndex, of Black-Scholes return factors for numIndex indices in each of numStep time steps and each of numScen scenarios. Covariances among indices are specified in covMatrix. Stepsize is given is dT and interpolated discount factors are given in vDF. Random seed is optional for reproducibility.

Usage

genIndexScen(
  covMatrix,
  numScen,
  numStep,
  indexNames,
  dT = 1/12,
  forwardCurve,
  seed
)
genIndexScen(
  covMatrix,
  numScen,
  numStep,
  indexNames,
  dT = 1/12,
  forwardCurve,
  seed
)

Arguments

`covMatrix`	A numIndex-by-numIndex matrix of doubles of covariances among numIndex indices.
`numScen`	An integer of number of scenario (sample paths) to be simulated.
`numStep`	An integer of number of periods to be simulated.
`indexNames`	A vector of strings containing index names.
`dT`	A double of stepsize in years; dT = 1 / 12 would be monthly.
`forwardCurve`	A vector of doubles of discount rates at each time step.
`seed`	An integer of the deterministic seed for random sampling.

Value

Outputs a 3D array (numScen-by-numStep-by-numIndex) of index scenarios

Examples

genIndexScen(mCov, 100, 360, indexNames, 1 / 12, cForwardCurve, 1)
genIndexScen(mCov, 100, 360, indexNames, 1 / 12, cForwardCurve, 1)

Generate Portfolio at Inception

Description

Generate a portfolio of VA contracts at inception based on given attribute ranges and investment fund information.

Usage

genPortInception(
  birthDayRng = c("1950-01-01", "1980-01-01"),
  issueRng = c("2001-08-01", "2014-01-01"),
  matRng = c(15, 30),
  acctValueRng = c(50000, 5e+05),
  femPct = 0.4,
  fundFee = c(30, 50, 60, 80, 10, 38, 45, 55, 47, 46),
  baseFee = 200,
  prodPct = rep(1/19, 19),
  prodType = c("DBRP", "DBRU", "DBSU", "ABRP", "ABRU", "ABSU", "IBRP", "IBRU", "IBSU",
    "MBRP", "MBRU", "MBSU", "WBRP", "WBRU", "WBSU", "DBAB", "DBIB", "DBMB", "DBWB"),
  riderFee = c(25, 35, 35, 50, 60, 60, 60, 70, 70, 50, 60, 60, 65, 75, 75, 75, 85, 75,
    90),
  rollUpRate = rep(5, 19),
  withdrawalRate = rep(5, 19),
  numPolicy = 10
)
genPortInception(
  birthDayRng = c("1950-01-01", "1980-01-01"),
  issueRng = c("2001-08-01", "2014-01-01"),
  matRng = c(15, 30),
  acctValueRng = c(50000, 5e+05),
  femPct = 0.4,
  fundFee = c(30, 50, 60, 80, 10, 38, 45, 55, 47, 46),
  baseFee = 200,
  prodPct = rep(1/19, 19),
  prodType = c("DBRP", "DBRU", "DBSU", "ABRP", "ABRU", "ABSU", "IBRP", "IBRU", "IBSU",
    "MBRP", "MBRU", "MBSU", "WBRP", "WBRU", "WBSU", "DBAB", "DBIB", "DBMB", "DBWB"),
  riderFee = c(25, 35, 35, 50, 60, 60, 60, 70, 70, 50, 60, 60, 65, 75, 75, 75, 85, 75,
    90),
  rollUpRate = rep(5, 19),
  withdrawalRate = rep(5, 19),
  numPolicy = 10
)

Arguments

`birthDayRng`	A vector of two strings in 'YYYY-MM-DD' of birthday range.
`issueRng`	A vector of two strings in 'YYYY-MM-DD' of issue date range.
`matRng`	A vector of two integers, range of policy maturity.
`acctValueRng`	A vector of two doubles, range of initial account values.
`femPct`	A double, percentage of female policyholders in the portfolio.
`fundFee`	A vector of doubles, fees charged by each fund in bps.
`baseFee`	A double, base fee for all funds in bps.
`prodPct`	A vector of non-negative doubles, proportions of rider types.
`prodType`	A vector of strings, names of different rider types.
`riderFee`	A vector of doubles, rider fees for different riders in bps.
`rollUpRate`	A vector of doubles, roll up rates for different rider types in bps.
`withdrawalRate`	A vector of doubles, withdrawal rates for different rider types in bps.
`numPolicy`	An integer, number of each type of policies to be generated.

Value

Outputs a data frame of 45 columns of attributes in an annuity contract.

Examples

genPortInception(c("1980-01-01", "1990-01-01"), c("2001-08-01", "2014-01-01"),
c(15, 30), c(5e4, 5e5), 0.4, c(30, 50, 60, 80, 10, 38, 45, 55, 47, 46),
200, rep(1 / 4, 4), c("WBRP", "WBRU", "WBSU", "DBWB"),
riderFee = c(25, 35, 35, 50), rep(5, 4), rep(5, 4), 100)
## Not run: 
genPortInception()

## End(Not run)
genPortInception(c("1980-01-01", "1990-01-01"), c("2001-08-01", "2014-01-01"),
c(15, 30), c(5e4, 5e5), 0.4, c(30, 50, 60, 80, 10, 38, 45, 55, 47, 46),
200, rep(1 / 4, 4), c("WBRP", "WBRU", "WBSU", "DBWB"),
riderFee = c(25, 35, 35, 50), rep(5, 4), rep(5, 4), 100)
## Not run: 
genPortInception()

## End(Not run)

Historical Scenario Dates

Description

A dataset containing the dates at which historical returns for different indices were observed.

Usage

histDates
histDates

Format

A vector with 175 elements:

date: each observation date of the historical scenarios

...

Historical Index Scenario for 5 Indices over 175 Months

Description

A dataset containing a matrix, number of indices (5) by number of time steps (175), of observed historical returns for each index in each of time step in the past.

Usage

histIdxScen
histIdxScen

Format

A data frame with dimensions 175 rows and 10 columns:

FIXED: historical return for index "FIXED" in one month
INT: historical return for index "INT" in one month
MONEY: historical return for index "MONEY" in one month
SMALL: historical return for index "SMALL" in one month
US: historical return for index "US" in one month

...

Remark

These historical index scenarios were assessed on 2008-09-12

Source

http://www.math.uconn.edu/~gan/software.html

Index Names

Description

A dataset containing names for each index.

Usage

indexNames
indexNames

Format

A vector with 5 elements:

name: name of the index

...

5 Indices for 10 Scenarios over 360 Months

Description

A dataset containing a 3D array, number of scenarios (10) by number of indices (5) by number of time steps (360), of Black-Scholes return factors for each index in each of time step and each of scenario.

Usage

indexScen
indexScen

Format

A 3D array with dimensions 10x360x5:

scenario: scenario number
month: month since valuation date
index number: monthly return for a particular index in one scenario one month

...

Covariance Matrix for 5 Indices

Description

A dataset containing the covariance matrix among the returns of five indices.

Usage

mCov
mCov

Format

A matrix with 5 rows and 5 columns:

index number: number for each index

...

Mortality Rate for Male and Female from Ages 5 to 115

Description

A dataset containing the mortality rates for male and female from ages 5 to 115 (table IAM 1996 from the Society of Actuaries).

Usage

mortTable
mortTable

Format

A data frame with 110 rows and 3 columns:

age: individual's age
male: mortality of a male at a particular age ranging from 5 to 115
female: mortality of a female at a particular age ranging from 5 to 115

...

Source

https://mort.soa.org

Swap Rates across 30 Years

Description

A dataset containing US swap rates for various maturities.

Usage

swapRate
swapRate

Format

A vector with 8 elements:

rate: swap rate

...

Remark

These swap rates were assessed on 2016-02-08

Source

http://www.federalreserve.gov

Valuate One Policy

Description

Valuate a VA policy specified in inPolicy based on the simulated fund scenarios fundScen. The time step length is specified in dT and the discount rate for each period is specified in df.

Usage

valuateOnePolicy(inPolicy, mortTable, fundScen, dT = 1/12, df)
valuateOnePolicy(inPolicy, mortTable, fundScen, dT = 1/12, df)

Arguments

`inPolicy`	A vector containing 45 attributes of a VA policy, usually a row of a VA portfolio dataframe.
`mortTable`	A dataframe with three columns of doubles representing the mortality table.
`fundScen`	A numScen-by-numStep-by-numFund array of doubles of return factors (i.e., exp(mu_t dt)) in each period.
`dT`	A double of stepsize in years; dT = 1 / 12 would be monthly.
`df`	A vector of doubles of risk-free discount rates of different tenor (not forward rates), should have length being numStep.

Value

Outputs a list of doubles of policyValue, the average discounted payoff of the VA, and riskCharge, the average discounted risk charges.

Examples

fundScen <- genFundScen(fundMap, indexScen)[1, , ]
exPolicy <- VAPort[1, ]
valuateOnePolicy(exPolicy, mortTable, fundScen, 1 / 12, cForwardCurve)
fundScen <- genFundScen(fundMap, indexScen)[1, , ]
exPolicy <- VAPort[1, ]
valuateOnePolicy(exPolicy, mortTable, fundScen, 1 / 12, cForwardCurve)

Valuate a Portfolio

Description

Valuate a portfolio VA policies specified in each curPolicy of inPortfolio based on the simulated fund scenarios fundScen. The time step length is specified in dT and the discount rate for each period is specified in df.

Usage

valuatePortfolio(inPortfolio, mortTable, fundScen, dT = 1/12, df)
valuatePortfolio(inPortfolio, mortTable, fundScen, dT = 1/12, df)

Arguments

`inPortfolio`	A dataframe containing numPolicy rows and 45 attributes of each VA policy.
`mortTable`	A dataframe with three columns of doubles representing the mortality table.
`fundScen`	A numScen-by-numStep-by-numFund array of doubles of return factors (i.e., exp(mu_t dt)) in each period.
`dT`	A double of stepsize in years; dT = 1 / 12 would be monthly.
`df`	A vector of doubles of risk-free discount rates of different tenor (not forward rates), should have length being numStep.

Value

Outputs a list of doubles of portVal, the sum of average discounted payoff of the VAs in inPortfolio, portRC, the sum of average discounted risk charges of the VAs in inPortfolio, and vectors of doubles of these average discounted values for each policy.

Examples

fundScen <- genFundScen(fundMap, indexScen)[1, , ]
valuatePortfolio(VAPort[1:2, ], mortTable, fundScen, 1 / 12, cForwardCurve)
fundScen <- genFundScen(fundMap, indexScen)[1, , ]
valuatePortfolio(VAPort[1:2, ], mortTable, fundScen, 1 / 12, cForwardCurve)

vamc: A package for pricing a pool of variable annuities.

Description

The vamc package provides a Monte Carlo engine for valuating a pool of variable annuities. The key steps are: YieldCurveGeneration, ScenarioGeneration, PolicyGenerationl, and MonteCarloValuation.

YieldCurveGeneration functions

YieldCurveGeneration generates a forward curve from swap rates. The forward curve is obtained by solving for swap rates that equates values of floating and fixed notes.

ScenarioGeneration functions

ScenarioGeneration generates a random fund scenario under Black-Scholes. After simulating random index scenarios, a fundMap is used to allocate returns of indices to each fund according to proportion of investment.

PolicyGenerationl functions

PolicyGenerationl randomly generates a pool of variable annuities for user-input birthday range, issue-date range, maturity range, account value range, female percentage, fund management fee, fund base fee, product types, rider fee of each type, roll-up-rate for roll-up featured guarantees, withdrawal rate for GMWB, and number of policies to be generated for each type.

MonteCarloValuation functions

MonteCarloValuation discounts cash flow from living and death benefits, as well as risk charges for each policy in the portfolio.

References

Gan G, Valdez EA (2017). “Valuation of Large Variable Annuity Portfolios: Monte Carlo Simulation and Synthetic Datasets.” Dependence Modeling, 5, 354–374. doi:10.1515/demo-2017-0021.

A Randomly Generated Pool of Variable Annuities

Description

A dataset containing information of the policy and the policy holder.

Usage

VAPort
VAPort

Format

A data frame with 19 row and 45 columns:

recordID: Unique identifier of the policy
survivorShip: Positive weighting number
gender: Gender of the policyholder
productType: Product type
issueDate: Issue date
matDate: Maturity date
birthDate: Birth date of the policyholder
currentDate: Current date
baseFee: M&E (Mortality & Expense) fee
riderFee: Rider fee
rollUpRate: Roll-up rate
gbAmt: Guaranteed benefit
gmwbBalance: GMWB balance
wbWithdrawalRate: Guaranteed withdrawal rate
withdrawal: Withdrawal so far
fundNum1: Fund number of the 1st investment fund
fundNum2: Fund number of the 2nd investment fund
fundNum3: Fund number of the 3rd investment fund
fundNum4: Fund number of the 4th investment fund
fundNum5: Fund number of the 5th investment fund
fundNum6: Fund number of the 6th investment fund
fundNum7: Fund number of the 7th investment fund
fundNum8: Fund number of the 8th investment fund
fundNum9: Fund number of the 9th investment fund
fundNum10: Fund number of the 10th investment fund
fundValue1: Fund value of the 1st investment fund
fundValue2: Fund value of the 2nd investment fund
fundValue3: Fund value of the 3rd investment fund
fundValue4: Fund value of the 4th investment fund
fundValue5: Fund value of the 5th investment fund
fundValue6: Fund value of the 6th investment fund
fundValue7: Fund value of the 7th investment fund
fundValue8: Fund value of the 8th investment fund
fundValue9: Fund value of the 9th investment fund
fundValue10: Fund value of the 10th investment fund
fundFee1: Fund management fee of the 1st investment fund
fundFee2: Fund management fee of the 2nd investment fund
fundFee3: Fund management fee of the 3rd investment fund
fundFee4: Fund management fee of the 4th investment fund
fundFee5: Fund management fee of the 5th investment fund
fundFee6: Fund management fee of the 6th investment fund
fundFee7: Fund management fee of the 7th investment fund
fundFee8: Fund management fee of the 8th investment fund
fundFee9: Fund management fee of the 9th investment fund
fundFee10: Fund management fee of the 10th investment fund

...

Package 'vamc'

Help Index

Age One Policy

Description

Usage

Arguments

Value

Note

Examples

Age a Portfolio

Description

Usage

Arguments

Value

Note

Examples

Build Curve

Description

Usage

Arguments

Value

Examples

Calculate Mortality Factors

Description

Usage

Arguments

Value

Examples

Constant Forward Curve

Description

Usage

Format

Fund Map for 10 Funds

Description

Usage

Format

Generate Fund Scenerio

Description

Usage

Arguments

Value

Examples

Generate Index Scenerio

Description

Usage

Arguments

Value

Examples

Generate Portfolio at Inception

Description

Usage

Arguments

Value

Examples

Historical Scenario Dates

Description

Usage

Format

Historical Index Scenario for 5 Indices over 175 Months

Description

Usage

Format

Remark

Source

Index Names

Description

Usage

Format

5 Indices for 10 Scenarios over 360 Months

Description

Usage

Format

Covariance Matrix for 5 Indices

Description

Usage

Format

Mortality Rate for Male and Female from Ages 5 to 115

Description

Usage

Format