# Actuarial Mathematics and Life-Table Statistics. Eric V. Slud Mathematics Department University of Maryland, College Park

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1 Actuarial Mathematics and Life-Table Statistics Eric V. Slud Mathematics Department University of Maryland, College Park c 2001

2 c 2001 Eric V. Slud Statistics Program Mathematics Department University of Maryland College Park, MD 20742

3 Contents 0.1 Preface vi 1 Basics of Probability & Interest Probability Theory of Interest Variable Interest Rates Continuous-time Payment Streams Exercise Set Worked Examples Useful Formulas from Chapter Interest & Force of Mortality More on Theory of Interest Annuities & Actuarial Notation Loan Amortization & Mortgage Refinancing Illustration on Mortgage Refinancing Computational illustration in Splus Coupon & Zero-coupon Bonds Force of Mortality & Analytical Models i

4 ii CONTENTS Comparison of Forces of Mortality Exercise Set Worked Examples Useful Formulas from Chapter Probability & Life Tables Interpreting Force of Mortality Interpolation Between Integer Ages Binomial Variables & Law of Large Numbers Exact Probabilities, Bounds & Approximations Simulation of Life Table Data Expectation for Discrete Random Variables Rules for Manipulating Expectations Some Special Integrals Exercise Set Worked Examples Useful Formulas from Chapter Expected Present Values of Payments Expected Payment Values Types of Insurance & Life Annuity Contracts Formal Relations among Net Single Premiums Formulas for Net Single Premiums Expected Present Values for m = Continuous Contracts & Residual Life

5 CONTENTS iii Numerical Calculations of Life Expectancies Exercise Set Worked Examples Useful Formulas from Chapter Premium Calculation m-payment Net Single Premiums Dependence Between Integer & Fractional Ages at Death Net Single Premium Formulas Case (i) Net Single Premium Formulas Case (ii) Approximate Formulas via Case(i) Net Level Premiums Benefits Involving Fractional Premiums Exercise Set Worked Examples Useful Formulas from Chapter Commutation & Reserves Idea of Commutation Functions Variable-benefit Commutation Formulas Secular Trends in Mortality Reserve & Cash Value of a Single Policy Retrospective Formulas & Identities Relating Insurance & Endowment Reserves Reserves under Constant Force of Mortality Reserves under Increasing Force of Mortality

6 iv CONTENTS Recursive Calculation of Reserves Paid-Up Insurance Select Mortality Tables & Insurance Exercise Set Illustration of Commutation Columns Examples on Paid-up Insurance Useful formulas from Chapter Population Theory Population Functions & Indicator Notation Expectation & Variance of Residual Life Stationary-Population Concepts Estimation Using Life-Table Data Nonstationary Population Dynamics Appendix: Large-time Limit of λ(t, x) Exercise Set Population Word Problems Estimation from Life-Table Data General Life-Table Data ML Estimation for Exponential Data MLE for Age Specific Force of Mortality Extension to Random Entry & Censoring Times Kaplan-Meier Survival Function Estimator Exercise Set Worked Examples

7 CONTENTS v 9 Risk Models & Select Mortality Proportional Hazard Models Excess Risk Models Select Life Tables Exercise Set Worked Examples Multiple Decrement Models Multiple Decrement Tables Death-Rate Estimators Deaths Uniform within Year of Age Force of Mortality Constant within Year of Age Cause-Specific Death Rate Estimators Single-Decrement Tables and Net Hazards of Mortality Cause-Specific Life Insurance Premiums Exercise Set Worked Examples Central Limit Theorem & Portfolio Risks Bibliography 217 Solutions & Hints 219

8 vi CONTENTS 0.1 Preface This book is a course of lectures on the mathematics of actuarial science. The idea behind the lectures is as far as possible to deduce interesting material on contingent present values and life tables directly from calculus and commonsense notions, illustrated through word problems. Both the Interest Theory and Probability related to life tables are treated as wonderful concrete applications of the calculus. The lectures require no background beyond a third semester of calculus, but the prerequisite calculus courses must have been solidly understood. It is a truism of pre-actuarial advising that students who have not done really well in and digested the calculus ought not to consider actuarial studies. It is not assumed that the student has seen a formal introduction to probability. Notions of relative frequency and average are introduced first with reference to the ensemble of a cohort life-table, the underlying formal random experiment being random selection from the cohort life-table population (or, in the context of probabilities and expectations for lives aged x, from the subset of l x members of the population who survive to age x). The calculation of expectations of functions of a time-to-death random variables is rooted on the one hand in the concrete notion of life-table average, which is then approximated by suitable idealized failure densities and integrals. Later, in discussing Binomial random variables and the Law of Large Numbers, the combinatorial and probabilistic interpretation of binomial coefficients are derived from the Binomial Theorem, which the student the is assumed to know as a topic in calculus (Taylor series identification of coefficients of a polynomial.) The general notions of expectation and probability are introduced, but for example the Law of Large Numbers for binomial variables is treated (rigorously) as a topic involving calculus inequalities and summation of finite series. This approach allows introduction of the numerically and conceptually useful large-deviation inequalities for binomial random variables to explain just how unlikely it is for binomial (e.g., life-table) counts to deviate much percentage-wise from expectations when the underlying population of trials is large. The reader is also not assumed to have worked previously with the Theory of Interest. These lectures present Theory of Interest as a mathematical problem-topic, which is rather unlike what is done in typical finance courses.

9 0.1. PREFACE vii Getting the typical Interest problems such as the exercises on mortgage refinancing and present values of various payoff schemes into correct format for numerical answers is often not easy even for good mathematics students. The main goal of these lectures is to reach by a conceptual route mathematical topics in Life Contingencies, Premium Calculation and Demography not usually seen until rather late in the trajectory of quantitative Actuarial Examinations. Such an approach can allow undergraduates with solid preparation in calculus (not necessarily mathematics or statistics majors) to explore their possible interests in business and actuarial science. It also allows the majority of such students who will choose some other avenue, from economics to operations research to statistics, for the exercise of their quantitative talents to know something concrete and mathematically coherent about the topics and ideas actually useful in Insurance. A secondary goal of the lectures has been to introduce varied topics of applied mathematics as part of a reasoned development of ideas related to survival data. As a result, material is included on statistics of biomedical studies and on reliability which would not ordinarily find its way into an actuarial course. A further result is that mathematical topics, from differential equations to maximum likelihood estimators based on complex life-table data, which seldom fit coherently into undergraduate programs of study, are vertically integrated into a single course. While the material in these lectures is presented systematically, it is not separated by chapters into unified topics such as Interest Theory, Probability Theory, Premium Calculation, etc. Instead the introductory material from probability and interest theory are interleaved, and later, various mathematical ideas are introduced as needed to advance the discussion. No book at this level can claim to be fully self-contained, but every attempt has been made to develop the mathematics to fit the actuarial applications as they arise logically. The coverage of the main body of each chapter is primarily theoretical. At the end of each chapter is an Exercise Set and a short section of Worked Examples to illustrate the kinds of word problems which can be solved by the techniques of the chapter. The Worked Examples sections show how the ideas and formulas work smoothly together, and they highlight the most important and frequently used formulas.

10 viii CONTENTS

11 Chapter 1 Basics of Probability and the Theory of Interest The first lectures supply some background on elementary Probability Theory and basic Theory of Interest. The reader who has not previously studied these subjects may get a brief overview here, but will likely want to supplement this Chapter with reading in any of a number of calculus-based introductions to probability and statistics, such as Larson (1982), Larsen and Marx (1985), or Hogg and Tanis (1997) and the basics of the Theory of Interest as covered in the text of Kellison (1970) or Chapter 1 of Gerber (1997). 1.1 Probability, Lifetimes, and Expectation In the cohort life-table model, imagine a number l 0 of individuals born simultaneously and followed until death, resulting in data d x, l x for each age x = 0, 1, 2,..., where l x = number of lives aged x (i.e. alive at birthday x ) and d x = l x l x+1 = number dying between ages x, x + 1 Now, allowing the age-variable x to take all real values, not just whole numbers, treat S(x) = l x /l 0 as a piecewise continuously differentiable non- 1

12 2 CHAPTER 1. BASICS OF PROBABILITY & INTEREST increasing function called the survivor or survival function. Then for all positive real x, S(x) S(x + t) is the fraction of the initial cohort which fails between time x and x + t, and S(x) S(x + t) S(x) = l x l x+t l x denotes the fraction of those alive at exact age x who fail before x + t. Question: what do probabilities have to do with the life table and survival function? To answer this, we first introduce probability as simply a relative frequency, using numbers from a cohort life-table like that of the accompanying Illustrative Life Table. In response to a probability question, we supply the fraction of the relevant life-table population, to obtain identities like P r(life aged 29 dies between exact ages 35 and 41 or between 52 and 60 ) { }/ = S(35) S(41) + S(52) S(60) = (l 35 l 41 ) + (l 52 l 60 ) where our convention is that a life aged 29 is one of the cohort surviving to the 29th birthday. The idea here is that all of the lifetimes covered by the life table are understood to be governed by an identical mechanism of failure, and that any probability question about a single lifetime is really a question concerning the fraction of those lives about which the question is asked (e.g., those alive at age x) whose lifetimes will satisfy the stated property (e.g., die either between 35 and 41 or between 52 and 60). This frequentist notion of probability of an event as the relative frequency with which the event occurs in a large population of (independent) identical units is associated with the phrase law of large numbers, which will bediscussed later. For now, remark only that the life table population should be large for the ideas presented so far to make good sense. See Table 1.1 for an illustration of a cohort life-table with realistic numbers. Note: see any basic probability textbook, such as Larson (1982), Larsen and Marx (1985), or Hogg and Tanis (1997) for formal definitions of the notions of sample space, event, probability, and conditional probability. The main ideas which are necessary to understand the discussion so far are really l 29

13 1.1. PROBABILITY 3 Table 1.1: Illustrative Life-Table, simulated to resemble realistic US (Male) life-table. For details of simulation, see Section 3.4 below. Age x l x d x x l x d x

14 4 CHAPTER 1. BASICS OF PROBABILITY & INTEREST matters of common sense when applied to relative frequency but require formal axioms when used more generally: Probabilities are numbers between 0 and 1 assigned to subsets of the entire range of possible outcomes (in the examples, subsets of the interval of possible human lifetimes measured in years). The probability P (A B) of the union A B of disjoint (i.e., nonoverlapping) sets A and B is necessarily the sum of the separate probabilities P (A) and P (B). When probabilities are requested with reference to a smaller universe of possible outcomes, such as B = lives aged 29, rather than all members of a cohort population, the resulting conditional probabilities of events A are written P (A B) and calculated as P (A B)/P (B), where A B denotes the intersection or overlap of the two events A, B. Two events A, B are defined to be independent when P (A B) = P (A) P (B) or equivalently, as long as P (B) > 0 the conditional probability P (A B) expressing the probability of A if B were known to have occurred, is the same as the (unconditional) probability P (A). The life-table data, and the mechanism by which members of the population die, are summarized first through the survivor function S(x) which at integer values of x agrees with the ratios l x /l 0. Note that S(x) has values between 0 and 1, and can be interpreted as the probability for a single individual to survive at least x time units. Since fewer people are alive at larger ages, S(x) is a decreasing function of x, and in applications S(x) should be piecewise continuously differentiable (largely for convenience, and because any analytical expression which would be chosen for S(x) in practice will be piecewise smooth). In addition, by definition, S(0) = 1. Another way of summarizing the probabilities of survival given by this function is to define the density function f(x) = ds dx (x) = S (x) Then, by the funda- as the (absolute) rate of decrease of the function S. mental theorem of calculus, for any ages a < b,

15 1.1. PROBABILITY 5 P (life aged 0 dies between ages a and b) = (l a l b )/l 0 = S(a) S(b) = b a ( S (x)) dx = b a f(x) dx (1.1) which has the very helpful geometric interpretation that the probability of dying within the interval [a, b] is equal to the area under the curve y = f(x) over the x-interval [a, b]. Note also that the probability rule which assigns the integral f(x) dx to the set A (which may be an interval, a union A of intervals, or a still more complicated set) obviously satisfies the first two of the bulleted axioms displayed above. The terminal age ω of a life table is an integer value large enough that S(ω) is negligibly small, but no value S(t) for t < ω is zero. For practical purposes, no individual lives to the ω birthday. While ω is finite in real life-tables and in some analytical survival models, most theoretical forms for S(x) have no finite age ω at which S(ω) = 0, and in those forms ω = by convention. Now we are ready to define some terms and motivate the notion of expectation. Think of the age T at which a specified newly born member of the population will die as a random variable, which for present purposes means a variable which takes various values x with probabilities governed by the life table data l x and the survivor function S(x) or density function f(x) in a formula like the one just given in equation (1.1). Suppose there is a contractual amount Y which must be paid (say, to the heirs of that individual) at the time T of death of the individual, and suppose that the contract provides a specific function Y = g(t ) according to which this payment depends on (the whole-number part of) the age T at which death occurs. What is the average value of such a payment over all individuals whose lifetimes are reflected in the life-table? Since d x = l x l x+1 individuals (out of the original l 0 ) die at ages between x and x + 1, thereby generating a payment g(x), the total payment to all individuals in the life-table can be written as (l x l x+1 ) g(x) x Thus the average payment, at least under the assumption that Y = g(t )

16 6 CHAPTER 1. BASICS OF PROBABILITY & INTEREST depends only on the largest whole number [T ] less than or equal to T, is x (l x l x+1 ) g(x) / l 0 = } x (S(x) S(x + 1))g(x) = x+1 x f(t) g(t) dt = (1.2) f(t) g(t) dt x 0 This quantity, the total contingent payment over the whole cohort divided by the number in the cohort, is called the expectation of the random payment Y = g(t ) in this special case, and can be interpreted as the weighted average of all of the different payments g(x) actually received, where the weights are just the relative frequency in the life table with which those payments are received. More generally, if the restriction that g(t) depends only on the integer part [t] of t were dropped, then the expectation of Y = g(t ) would be given by the same formula E(Y ) = E(g(T )) = 0 f(t) g(t) dt The last displayed integral, like all expectation formulas, can be understood as a weighted average of values g(t ) obtained over a population, with weights equal to the probabilities of obtaining those values. Recall from the Riemann-integral construction in Calculus that the integral f(t)g(t)dt can be regarded approximately as the sum over very small time-intervals [t, t + ] of the quantities f(t)g(t), quantities which are interpreted as the base of a rectangle multiplied by its height f(t)g(t), and the rectangle closely covers the area under the graph of the function f g over the interval [t, t + ]. The term f(t)g(t) can alternatively be interpreted as the product of the value g(t) essentially equal to any of the values g(t ) which can be realized when T falls within the interval [t, t + ] multiplied by f(t). The latter quantity is, by the Fundamental Theorem of the Calculus, approximately equal for small to the area under the function f over the interval [t, t + ], and is by definition equal to the probability with which T [t, t + ]. In summary, E(Y ) = g(t)f(t)dt 0 is the average of values g(t ) obtained for lifetimes T within small intervals [t, t + ] weighted by the probabilities of approximately f(t) with which those T and g(t ) values are obtained. The expectation is a weighted average because the weights f(t) sum to the integral 0 f(t)dt = 1. The same idea and formula can be applied to the restricted population of lives aged x. The resulting quantity is then called the conditional

17 1.2. THEORY OF INTEREST 7 expected value of g(t ) given that T x. The formula will be different in two ways: first, the range of integration is from x to, because of the resitriction to individuals in the life-table who have survived to exact age x; second, the density f(t) must be replaced by f(t)/s(x), the so-called conditional density given T x, which is found as follows. From the definition of conditional probability, for t x, P (t T t + T x) = P ( [t T t + ] [T x]) P (T x) = P (t T t + ) P (T x) = S(t) S(t + ) S(x) Thus the density which can be used to calculate conditional probabilities P (a T b T x) for x < a < b is lim 0 1 P (t T t+ S(t) S(t + ) T x) = lim 0 S(x) = S (t) S(x) = f(t) S(x) The result of all of this discussion of conditional expected values is the formula, with associated weighted-average interpretation: E(g(T ) T x) = 1 S(x) x g(t) f(t) dt (1.3) 1.2 Theory of Interest Since payments based upon unpredictable occurrences or contingencies for insured lives can occur at different times, we study next the Theory of Interest, which is concerned with valuing streams of payments made over time. The general model in the case of constant interest is as follows. Compounding at time-intervals h = 1/m, with nominal interest rate i (m), means that a unit amount accumulates to (1 + i (m) /m) after a time h = 1/m. The principal or account value 1+i (m) /m at time 1/m accumulates over the time-interval from 1/m until 2/m, to (1+i (m) /m) (1+i (m) /m) = (1+i (m) /m) 2. Similarly, by induction, a unit amount accumulates to (1+i (m) /m) n = (1+i (m) /m) T m after the time T = nh which is a multiple of n whole units of h. In the

18 8 CHAPTER 1. BASICS OF PROBABILITY & INTEREST limit of continuous compounding (i.e., m ), the unit amount compounds to e δ T after time T, where the instantaneous annualized nominal interest rate δ = lim m i (m) (also called the force of interest) will be shown to exist. In either case of compounding, the actual Annual Percentage Rate or APR or effective interest rate is defined as the amount (minus 1, and multiplied by 100 if it is to be expressed as a percentage) to which a unit compounds after a single year, i.e., respectively as i APR = ) (1 + i(m) m 1 or e δ 1 m The amount to which a unit invested at time 0 accumulates at the effective interest rate i APR over a time-duration T (still assumed to be a multiple of 1/m) is therefore ) T ) (1 + i APR = (1 + i(m) mt = e δ T m This amount is called the accumulation factor operating over the interval of duration T at the fixed interest rate. Moreover, the first and third expressions of the displayed equation also make perfect sense when the duration T is any positive real number, not necessarily a multiple of 1/m. All the nominal interest rates i (m) for different periods of compounding are related by the formulas (1 + i (m) /m) m = 1 + i = 1 + i APR, i (m) = m { (1 + i) 1/m 1 } (1.4) Similarly, interest can be said to be governed by the discount rates for various compounding periods, defined by 1 d (m) /m = (1 + i (m) /m) 1 Solving the last equation for d (m) gives d (m) = i (m) /(1 + i (m) /m) (1.5) The idea of discount rates is that if \$1 is loaned out at interest, then the amount d (m) /m is the correct amount to be repaid at the beginning rather than the end of each fraction 1/m of the year, with repayment of the principal of \$1 at the end of the year, in order to amount to the same

19 1.2. THEORY OF INTEREST 9 effective interest rate. The reason is that, according to the definition, the amount 1 d (m) /m accumulates at nominal interest i (m) (compounded m times yearly) to (1 d (m) /m) (1 + i (m) /m) = 1 after a time-period of 1/m. The quantities i (m), d (m) are naturally introduced as the interest payments which must be made respectively at the ends and the beginnings of successive time-periods 1/m in order that the principal owed at each time j/m on an amount \$ 1 borrowed at time 0 will always be \$ 1. To define these terms and justify this assertion, consider first the simplest case, m = 1. If \$ 1 is to be borrowed at time 0, then the single payment at time 1 which fully compensates the lender, if that lender could alternatively have earned interest rate i, is \$ (1 + i), which we view as a payment of \$ 1 principal (the face amount of the loan) and \$ i interest. In exactly the same way, if \$ 1 is borrowed at time 0 for a time-period 1/m, then the repayment at time 1/m takes the form of \$ 1 principal and \$ i (m) /m interest. Thus, if \$ 1 was borrowed at time 0, an interest payment of \$ i (m) /m at time 1/m leaves an amount \$ 1 still owed, which can be viewed as an amount borrowed on the time-interval (1/m, 2/m]. Then a payment of \$ i (m) /m at time 2/m still leaves an amount \$ 1 owed at 2/m, which is deemed borrowed until time 3/m, and so forth, until the loan of \$ 1 on the final time-interval ((m 1)/m, 1] is paid off at time 1 with a final interest payment of \$ i (m) /m together with the principal repayment of \$ 1. The overall result which we have just proved intuitively is: \$ 1 at time 0 is equivalent to the stream of m payments of \$ i (m) /m at times 1/m, 2/m,..., 1 plus the payment of \$ 1 at time 1. Similarly, if interest is to be paid at the beginning of the period of the loan instead of the end, the interest paid at time 0 for a loan of \$ 1 would be d = i/(1 + i), with the only other payment a repayment of principal at time 1. To see that this is correct, note that since interest d is paid at the same instant as receiving the loan of \$ 1, the net amount actually received is 1 d = (1 + i) 1, which accumulates in value to (1 d)(1 + i) = \$ 1 at time 1. Similarly, if interest payments are to be made at the beginnings of each of the intervals (j/m, (j + 1)/m] for j = 0, 1,..., m 1, with a final principal repayment of \$ 1 at time 1, then the interest payments should be d (m) /m. This follows because the amount effectively borrowed

20 10 CHAPTER 1. BASICS OF PROBABILITY & INTEREST (after the immediate interest payment) over each interval (j/m, (j + 1)/m] is \$ (1 d (m) /m), which accumulates in value over the interval of length 1/m to an amount (1 d (m) /m)(1 + i (m) /m) = 1. So throughout the year-long life of the loan, the principal owed at (or just before) each time (j + 1)/m is exactly \$ 1. The net result is \$ 1 at time 0 is equivalent to the stream of m payments of \$ d (m) /m at times 0, 1/m, 2/m,..., (m 1)/m plus the payment of \$ 1 at time 1. A useful algebraic exercise to confirm the displayed assertions is: Exercise. Verify that the present values at time 0 of the payment streams with m interest payments in the displayed assertions are respectively m j=1 i (m) m (1 + i) j/m + (1 + i) 1 and m 1 j=0 d (m) m (1 + i) j/m + (1 + i) 1 and that both are equal to 1. These identities are valid for all i > Variable Interest Rates Now we formulate the generalization of these ideas to the case of non-constant instantaneously varying, but known or observed, nominal interest rates δ(t), for which the old-fashioned name would be time-varying force of interest. Here, if there is a compounding-interval [kh, (k + 1)h) of length h = 1/m, one would first use the instantaneous continuously-compounding interest-rate δ(kh) available at the beginning of the interval to calculate an equivalent annualized nominal interest-rate over the interval, i.e., to find a number r m (kh) such that ( 1 + r ) ( ) m(kh) 1/m ( δ(kh) ) = e δ(kh) = exp m m In the limit of large m, there is an essentially constant principal amount over each interval of length 1/m, so that over the interval [b, b + t), with instantaneous compounding, the unit principal amount accumulates to lim m eδ(b)/m e δ(b+h)/m e δ(b+[mt]h)/m

21 1.2. THEORY OF INTEREST 11 = exp lim m 1 m [mt] 1 k=0 ( t δ(b + k/m) = exp 0 ) δ(b + s) ds The last step in this chain of equalities relates the concept of continuous compounding to that of the Riemann integral. To specify continuous-time varying interest rates in terms of effective or APR rates, instead of the instantaneous nominal rates δ(t), would require the simple conversion ( ) r APR (t) = e δ(t) 1, δ(t) = ln 1 + r APR (t) Next consider the case of deposits s 0, s 1,..., s k,..., s n made at times 0, h,..., kh,..., nh, where h = 1/m is the given compounding-period, and where nominal annualized instantaneous interest-rates δ(kh) (with compounding-period h) apply to the accrual of interest on the interval [kh, (k + 1)h). If the accumulated bank balance just after time kh is denoted by B k, then how can the accumulated bank balance be expressed in terms of s j and δ(jh)? Clearly ( B k+1 = B k 1 + i(m) (kh) ) + s k+1, B 0 = s 0 m The preceding difference equation can be solved in terms of successive summation and product operations acting on the sequences s j and δ(jh), as follows. First define a function A k to denote the accumulated bank balance at time kh for a unit invested at time 0 and earning interest with instantaneous nominal interest rates δ(jh) (or equivalently, nominal rates r m (jh) for compounding at multiples of h = 1/m) applying respectively over the whole compounding-intervals [jh, (j + 1)h), j = 0,..., k 1. Then by definition, A k satisfies a homogeneous equation analogous to the previous one, which together with its solution is given by ( A k+1 = A k 1 + r m(kh) m ), A 0 = 1, A k = k 1 ( 1 + r m(jh) m j=0 ) The next idea is the second basic one in the theory of interest, namely the idea of equivalent investments leading to the definition of present value of an income stream/investment. Suppose that a stream of deposits s j accruing

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