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- 1. Pricing Mortality Swaps Using R R in Insurance 2015 Mick Cooney michael.cooney@applied.ai 29 June 2015 Mick Cooney michael.cooney@applied.ai Applied AI Ltd Pricing Mortality Swaps Using R
- 2. Before I Begin... https://xkcd.com/793/ Mick Cooney michael.cooney@applied.ai Applied AI Ltd Pricing Mortality Swaps Using R
- 3. What is a Mortality Swap? Seller provides protection against mortality risk Portfolio of Life-Contingent Annuities Converts the portfolio to Guaranteed Annuities Only guaranteeing mortality-adjusted cashﬂows Mick Cooney michael.cooney@applied.ai Applied AI Ltd Pricing Mortality Swaps Using R
- 4. Swap Annuity Portfolio Random portfolio of annuities: ## customerid age MUR amount ## 1: C97326460 39 350 $100,000 ## 2: C99971880 30 175 $80,000 ## 3: C65134119 34 225 $50,000 ## 4: C35313144 33 200 $90,000 ## 5: C17550793 45 200 $30,000 ## --- ## 196: C37440555 40 200 $50,000 ## 197: C60347677 42 200 $60,000 ## 198: C93383952 45 200 $70,000 ## 199: C08447895 35 150 $60,000 ## 200: C64003360 41 325 $90,000 Mick Cooney michael.cooney@applied.ai Applied AI Ltd Pricing Mortality Swaps Using R
- 5. Interest Rate Swaps Mick Cooney michael.cooney@applied.ai Applied AI Ltd Pricing Mortality Swaps Using R
- 6. Assumptions Starting point — some generalisable for ﬂexibility 1 No consideration of credit risk 2 Swap has ﬁxed lifetime 3 All annuities have annual payments received at same time 4 Fixed cost of capital over lifetime 5 All annuitants have undergone underwriting evaluation 6 APV calculations require a speciﬁc lifetable 7 All annuities have a lifetime at least as long as the swap lifetime Mick Cooney michael.cooney@applied.ai Applied AI Ltd Pricing Mortality Swaps Using R
- 7. Calculating the MUR multiplier lifetable.dt <- fread("lifetable.csv"); A <- 100000; qx <- lifetable.dt[age >= 30][age < 50]$qx; r <- 0.05; calculate.MUR.multiple.diff <- function(MUR, mult) { MUR * price.term.assurance(qx, A = A, r = r, P = 0) - price.term.assurance(mult * qx, A = A, r = r, P = 0); } MUR.values <- seq(0, 10, by = 0.25); ### Determine mortality multiplier that best matches the premium multiplier MUR.mult <- sapply(MUR.values, function(MUR) { optimize(function(mult) abs(calculate.MUR.multiple.diff(MUR, mult)), c(0, 20))$minimum; }); Mick Cooney michael.cooney@applied.ai Applied AI Ltd Pricing Mortality Swaps Using R
- 8. Calculating the MUR multiplier 0.0 2.5 5.0 7.5 10.0 0.0 2.5 5.0 7.5 10.0 MUR Multiplier Mick Cooney michael.cooney@applied.ai Applied AI Ltd Pricing Mortality Swaps Using R
- 9. MonteCarlo Methods Calculating the NAV of a fund of correlated assets with a yearly drawdown: Mick Cooney michael.cooney@applied.ai Applied AI Ltd Pricing Mortality Swaps Using R
- 10. Simulation Approach Simulation example: 10 simulations of 5 years 1 – annuitant still alive 0 – annuitant has deceased ## sim1 sim2 sim3 sim4 sim5 sim6 sim7 sim8 sim9 sim10 ## year1 1 1 1 1 1 1 1 1 1 1 ## year2 1 1 0 1 1 1 1 1 1 1 ## year3 1 1 0 1 1 1 1 1 0 1 ## year4 1 1 0 1 0 1 1 1 0 1 ## year5 1 1 0 1 0 1 1 1 0 1 Mick Cooney michael.cooney@applied.ai Applied AI Ltd Pricing Mortality Swaps Using R
- 11. Running the Simulation Run the calculation: mortport.dt <- fread("mortswap_portfolio.csv"); lifetable.dt <- fread("lifetable.csv"); n.sim <- 1000; mortswap.value.sim <- calculate.mortality.swap.price(mortport.dt, lifetable = lifetable.dt, hedge.apv.cashflows = TRUE, interest.rate = 0.05, years.in.force = 20, n.sim = n.sim, return.all.data = FALSE); print(dollar_format(largest_with_cents = 1e8)(mortswap.value.sim[1:20])); ## [1] "$748,064.31" "$461,186.76" "$2,274,787.66" "$-1,251,047.55" ## [5] "$1,954,488.45" "$-1,251,047.55" "$-2,237,366.32" "$1,733,535.87" ## [9] "$1,707,822.63" "$-1,299,952.30" "$2,139,507.05" "$-757,888.16" ## [13] "$-309,255.02" "$-717,420.20" "$1,927,893.22" "$1,417,572.90" ## [17] "$1,571,751.87" "$-739,440.89" "$-1,990,786.63" "$475,010.30" Mick Cooney michael.cooney@applied.ai Applied AI Ltd Pricing Mortality Swaps Using R
- 12. Viewing the Output Simulated cashﬂows (excludes initial premium): $−2,000,000 $0 $2,000,000 $4,000,000 $6,000,000 0.00 0.25 0.50 0.75 1.00 Percentile NetCashflow/Loss Mick Cooney michael.cooney@applied.ai Applied AI Ltd Pricing Mortality Swaps Using R
- 13. Viewing the Output Simulated cashﬂows (excludes initial premium): $−2,000,000 $0 $2,000,000 $4,000,000 $6,000,000 0.00 0.25 0.50 0.75 1.00 Percentile NetCashflow/Loss Mick Cooney michael.cooney@applied.ai Applied AI Ltd Pricing Mortality Swaps Using R
- 14. Pricing Tail Risk How to price the tail risk? Michael Lewis “In Nature’s Casino” – NYT Aug 2007 http://www.nytimes.com/2007/08/26/magazine/ 26neworleans-t.html?pagewanted=all Consensus around 4× expected loss Need to calculate tail averages Mick Cooney michael.cooney@applied.ai Applied AI Ltd Pricing Mortality Swaps Using R
- 15. Pricing Tail Risk $0 $1,000,000 $2,000,000 $3,000,000 $4,000,000 $5,000,000 75 80 85 90 95 100 Tail Percentile MeanLossAmount Mick Cooney michael.cooney@applied.ai Applied AI Ltd Pricing Mortality Swaps Using R
- 16. Pricing Tail Risk Ensemble of 1,000 valuations of 10,000 iterations: 90% 95% 99% 99.5% 0e+00 2e−06 4e−06 6e−06 8e−06 0e+00 2e−06 4e−06 6e−06 0e+00 1e−06 2e−06 3e−06 0.0e+00 5.0e−07 1.0e−06 1.5e−06 2.0e−06 $3,500,000 $3,600,000 $3,700,000 $4,200,000 $4,300,000 $4,400,000 $4,500,000 $5,600,000 $5,800,000 $6,000,000 $6,200,000 $6,000,000 $6,250,000 $6,500,000 $6,750,000 $7,000,000 Mean Loss Amount ProbabilityDensity Mick Cooney michael.cooney@applied.ai Applied AI Ltd Pricing Mortality Swaps Using R
- 17. Time-in-Force Dependency Scaling of 95% mean with time-in-force of swap: $0 $1,000,000 $2,000,000 $3,000,000 5 10 15 20 Swap Time−in−Force (years) 95%MeanLossValue Mick Cooney michael.cooney@applied.ai Applied AI Ltd Pricing Mortality Swaps Using R
- 18. Time-in-Force Dependency (Ensemble) times.in.force <- 1:20; n.sim <- 1000; n.ens <- 10; calculate.tif <- function(tif) { mortswap.value.sim <- calculate.mortality.swap.price(mortport.dt, lifetable = lifetable.dt, hedge.apv.cashflows = TRUE, interest.rate = 0.05, years.in.force = tif, n.sim = n.sim, return.all.data = FALSE); iterval <- calculate.quantile.averages(mortswap.value.sim, 0.95); } tif.ensemble <- sapply(times.in.force, function(iter.tif) { replicate(n.ens, calculate.tif(iter.tif)) }); Mick Cooney michael.cooney@applied.ai Applied AI Ltd Pricing Mortality Swaps Using R
- 19. Time-in-Force Dependency (Ensemble) $0 $1,000,000 $2,000,000 $3,000,000 5 10 15 20 Year MeanLossAverage Mick Cooney michael.cooney@applied.ai Applied AI Ltd Pricing Mortality Swaps Using R
- 20. Summary R package: mcmortswap https://bitbucket.org/appliedai/mcmortswap Email: michael.cooney@applied.ai Slides available on bitbucket: https://bitbucket.org/appliedai/r_in_insurance_2015 Mick Cooney michael.cooney@applied.ai Applied AI Ltd Pricing Mortality Swaps Using R

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