Models for Quantifying Risk

7/26/2019 Models for Quantifying Risk

http://slidepdf.com/reader/full/models-for-quantifying-risk 1/40

PREFACE

The analysis and management of financial risk is the fundamental subject matter of thediscipline of actuarial science, and is therefore the basic work of the actuary. In order to

manage financial risk, by use of insurance schemes or any other risk management technique,the actuary must first have a framework for quantifying the magnitude of the risk itself. This isachieved by using mathematical models that are appropriate for each particular type of risk

under consideration. Since risk is, almost by definition, probabilistic, it follows that theappropriate models will also be probabilistic, or stochastic, in nature.

This textbook, appropriately entitled Models for Quantifying Risk , addresses the major typesof financial risk analyzed by actuaries, and presents a variety of stochastic models for the

actuary to use in undertaking this analysis. It is designed to be appropriate for a two-semester university course in basic actuarial science for third-year or fourth-yearundergraduate students or entry-level graduate students. It is also intended to be anappropriate text for use by candidates in preparing for Exam MLC of the Society ofActuaries or Exam 3L of the Casualty Actuarial Society.

One way to manage financial risk is to insure it, which basically means that a second party,generally an insurance company, is paid a fee to assume the risk from the party initiallyfacing it. Historically the work of actuaries was largely confined to the management of risk

within an insurance context, so much so, in fact, that actuaries were thought of as “insurancemathematicians” and actuarial science was thought of as “insurance math.” Although theinsurance context remains a primary environment for the actuarial management of risk, it is

by no means any longer the only one.

However, in recognition of the insurance context as the original setting for actuarial analysisand management of financial risk, we have chosen to make liberal use of insuranceterminology and notation to describe many of the risk quantification models presented in this

text. The reader should always keep in mind, however, that this frequent reference to aninsurance context does not reduce the applicability of the models to risk management

situations in which no use of insurance is involved.

The text is written in a manner that assumes each reader has a strong background in calculus,linear algebra, the theory of compound interest, and probability. (A familiarity with statisticsis not presumed.)

This edition of the text is organized into three sections. The first, consisting of Chapters 1-4, presents a review of interest theory, probability, Markov Chains, and stochastic simulation,respectively. The content of these chapters is very much needed as background to later



iv PREFACE

material. They are included in the text for readers needing a comprehensive review of thetopics. For those requiring an original textbook on any of these topics, we recommend either

Broverman [6] or Kellison [16] for interest theory, Hassett and Stewart [12] for probability,Ross [26] for Markov Chains, and Herzog and Lord [13] for simulation.

The second section, made up of Chapters 5-14, addresses the topic of survival-contingent

payment models, traditionally referred to as life contingencies. The third section, consisting ofChapters 15-17, deals with the topic of models for interest rate risks.

The new material appearing in this edition has been added to the text to meet the new set oflearning objectives for Exam MLC, to be effective with the May 2012 administration of theexam. There are several major areas of expanded material:

(1) Part Three (Chapters 15-17) has been added to address the topic of interest rate risk,including an introduction to variable, or interest-sensitive , insurance and annuities.

(2) The new learning objectives provide that many of the models described in the text shouldalso be presented in the context of multi-state models, using the theory of Markov Chains.

Accordingly, we have inserted new Chapter 3 to provide background on Markov Chains,and have then inserted numerous applications of this theory to our actuarial modelsthroughout the text.

(3) In the prior edition of the text, the topic of contingent contract reserves was presentedin one large chapter. In the new edition, we have separated the topic into two chapters:

Chapter 10 covers net level premium benefit reserves only, and Chapter 11 addressesthe accounting notion of reserves as financial liabilities, as well as other policy values,

as suggested by the new learning objectives.

(4) Similarly, the topic of multiple decrements is now presented in two chapters: Chapter

13 addresses multiple decrement theory and Chapter 14 presents a number of applica-

tions of the theory, many in the context of multi-state models.

(5) Certain actuarial risk models are most efficiently evaluated through simulation, ratherthan by use of closed form analytic solutions. We have inserted new Chapter 4 to

provide theoretical background on the topic of stochastic simulation, and thendeveloped a number of applications of that theory to our actuarial models. Readerswho are preparing for SOA Exam MLC should be aware that this topic is not coveredon that examination. Accordingly we have placed all of the simulation applications inAppendix B in this edition.

(6) A number of minor topics have been deleted from the prior edition, including (a) the

central rate of failure, (b) use of the population functions , , x x

L T and , x

Y and (c) our

presentation of Hattendorf’s Theorem. Several notational changes have also beenmade.

The writing team would like to thank a number of people for their contributions to thedevelopment of this text.



PREFACE v

The original manuscript was thoroughly reviewed by Bryan V. Hearsey, ASA, of LebanonValley College and by Esther Portnoy, FSA, of University of Illinois. Portions of the

manuscript were also reviewed by Warren R. Luckner, FSA, and his graduate student LuisGutierrez at University of Nebraska-Lincoln. Kristen S. Moore, ASA, used an earlier draft asa supplemental text in her courses at University of Michigan. Thorough reviews of theoriginal edition were also conducted by James W. Daniel, ASA, of University of Texas,

Professor Jacques Labelle, Ph.D., of Université du Québec à Montréal, and a committeeappointed by the Society of Actuaries. A number of revisions in the Second Edition werealso reviewed by Professors Daniel and Hearsey; Third Edition revisions were reviewed byProfessors Samuel A. Broverman, ASA (University of Toronto), Matthew J. Hassett, ASA(Arizona State University), and Warren R. Luckner, FSA (University of Nebraska-Lincoln).All of these academic colleagues made a number of useful comments that have contributed

to an improved published text.

The new topics contained in this edition were researched for us by actuaries with consider-able experience in their respective fields, and we wish to acknowledge their valuable

contributions. They include Ronald Gebhardtsbauer, FSA (Penn State University) forSection 14.5, Ximing Yao, FSA (Hartford Life) for Chapter 16, and Chunhua (Amy) Meng,

FSA (Yindga Taihe Life) for Chapter 17.

The new material added to this Fourth Edition was also reviewed by Professor Luckner, as well

as by Tracey J. Polsgrove, FSA (John Hancock USA), Link Richardson, FSA (AmericanGeneral Life), Arthur W. Anderson, ASA, EA (The Arthur W. Anderson Group), Cheryl AnnBreindel, FSA (Hartford Life), Douglas J. Jangraw, FSA (Massachusetts Mutual Life), Robert

W. Beal, FSA (Milliman Portland), Andrew C. Boyer, FSA (Milliman Windsor), and MatthewBlanchette, FSA (Hartford Life), who also contributed to the Chapter 17 exercises.

Special thanks goes to the students enrolled in Math 287-288 at University of Connecticutduring the 2004-05 academic year, where the original text was classroom-tested, and to

graduate student Xiumei Song, who developed the computer technology material presented in

Appendix A.

Thanks also to the folks at ACTEX Publications, particularly Gail A. Hall, FSA, the projecteditor, and Marilyn J. Baleshiski, who did the typesetting and graphic arts for all editions.

Kathleen H. Borkowski designed the cover for the first two editions, and Christine Phelpsdid the same for the latter two editions.

Finally, a very special acknowledgment is in order. When the Society of Actuaries publishedits textbook Actuarial Mathematics in the mid-1980s, Professor Geoffrey Crofts, FSA, then

at University of Hartford, made the observation that the authors’ use of the generic symbol Z as the present value random variable for all insurance models and the generic symbol Y asthe present value random variable for all annuity models was confusing. He suggested that

the present value random variable symbols be expanded to identify more characteristics ofthe models to which each related, following the principle that the present value randomvariable be notated in a manner consistent with the standard International Actuarial Notation

used for its expected value. Thus one should use, for example, : x n Z in the case of the

continuous endowment insurance model and |n x

Y in the case of the n-year deferred annuity-



vi PREFACE

due model, whose expected values are denoted : x n A and | ,

n xa respectively. Professor

Crofts’ notation has been adopted throughout our textbook, and we wish to thank him for

suggesting this very useful idea to us.

We wish you good luck with your studies and your exam preparation.

Robin J. Cunningham, FSA Winsted, ConnecticutSan Rafael, California June 2011

Thomas N. Herzog, ASAReston, Virginia

Richard L. London, FSA

Storrs, Connecticut



267

CHAPTER TWELVE

MODELS DEPENDENT ON MULTIPLE SURVIVALS

(MULTI-LIFE MODELS)

All the discussion to this point in the text has focused on the complementary concepts of sur-vival and failure of a single entity, such as a single person under a life insurance policy or a lifeannuity. We consistently referred to the entity whose survival was being observed as our statusof interest , and we defined what constituted survival (and therefore failure) in each particularcase. This generic approach to the concept of survival will be continued in this chapter.

We now consider the case where the status of interest is itself made up of two or more entities,such as two separate individual lives. In actuarial science such models are said to involve multi-

ple lives and are known as multi-life models. We will develop the theory of multi-life models inthe two-life case first, and then show how it is easily extended to more than two lives.

We will also need to distinguish whether the two individual lives comprising a multi-lifestatus have independent or dependent future lifetimes. The assumption of independence willsimplify our work to some extent, and is presumed wherever needed throughout the chapter.Independence is not presumed for the discussion in Sections 12.5 and 12.6.

12.1 THE JOINT-LIFE MODEL

A joint -life status is one for which survival of the status requires the survival of all (or both,

in the two-life case) of the individual members making up the status. Accordingly, the status

fails upon the first failure of its component members.

12.1.1 THE TIME-TO-FAILURE R ANDOM VARIABLE FOR A JOINT-LIFE STATUS

Consider a two-life joint status, made up of lives that are ages x and y as of time 0. We use

the notation ( ) xy to denote such a status,1 and we use xyT to denote the random variable for

the future lifetime (or time-to-failure) of the status. From the definition of failure it is clear

that xyT will be the smaller of the individual future lifetimes denoted by xT and . yT That is,

min( , ). xy x yT T T (12.1)

Our analysis of the future lifetime random variable xy

T will parallel that for the individual

life xT presented in Section 5.3. Indeed, by using the generic concept of a status or entity,

the two cases of xyT and xT are really the same except for the different notation.

1 If numerical ages are used instead of the letters x and y, such as if 20 x and 25, y for example, the status is

denoted (20:25). The colon would also be used to denote the status ( : ). x n y n



268 CHAPTER TWELVE

12.1.2 THE SURVIVAL DISTRIBUTION FUNCTION OF xyT

We begin our analysis of the random variable xyT with its SDF, given by

( ) ( ) xy xyS t Pr T t (12.2)

and denoted by t xy p in standard actuarial notation. Since survival of the status itself requiresthe survival of both component members of the status, then assuming independence of theindividual lifetimes we have

,t xy t x t y p p p (12.3)

which is the bridge between joint-life and individual life functions. Equation (12.3) will al-

low us to evaluate many joint-life functions from a single-life tabular survival model.

12.1.3 THE CUMULATIVE DISTRIBUTION FUNCTION OF xyT

The CDF of xyT is given by

( ) 1 ( ) ( ), xy xy xy F t S t Pr T t (12.4)

and is denoted by t xyq in actuarial notation. It follows that

1t xy t xyq p (12.5a)

for all t . If the individual lifetimes are independent, then we can write

1

1 (1 )(1 ) .

t xy t x t y

t x t y t x t y t x t y

q p p

q q q q q q

(12.5b)

Equation (12.5b) illustrates a very basic concept in probability. t xyq denotes the probability

that the joint status fails before (or at) time t , which occurs if either or both of the individual

lives fail before (or at) time t . Since the events ( ) xT t and ( ) yT t are not mutually exclu-

sive, the probability of the union event, which is ,t xyq is given by the general addition rule

reflected in Equation (12.5b).

As in the individual life case discussed earlier in the text, the pre-subscript t is suppressed in

the special case of 1.t Thus we have

(1) ( 1) xy xy xy p S Pr T (12.6a)

and

1 (1) ( 1). xy xy xy xyq p F Pr T (12.6b)



MODELS DEPENDENT ON MULTIPLE SURVIVALS 269

EXAMPLE 12.1

If xT and yT are independent, and each is uniformly distributed over each year of age sepa-

rately, show that, for 0 1,t

2

(1 ) (1 ) .t xy x y y x x yq t q t q t q t q t q q SOLUTION

With independent lifetimes we have, from Equation (12.5b),

.t xy t x t y t x t yq q q q q

Under UDD this becomes

2

2 2 2

2(1 ) (1 ) ,

t xy x y x y

x x y y x y x y

x y y x x y

q t q t q t q q

t q t q q t q t q q t q q

t q t q t q t q t q q

as required.

12.1.4 THE PROBABILITY DENSITY FUNCTION OF xyT

The PDF of xyT is defined by

( ) ( ) ( ). xy xy xyd d f t F t S t dt dt

(12.7)

In the special case of independence, we have

( ) ( )

( )

[ ( ) ( )]

( ),

xy xy

t x t y

t x t y t y t x

t x t y y t t y t x x t

t x t y y t t y t x x t

t xy x t y t

d f t S t dt

d p pdt

d d p p p pdt dt

p p p p

p p p p

p

(12.8)

where we have used Equation (6.21) for t xd dt

p and .t yd dt

p



270 CHAPTER TWELVE

12.1.5 THE HAZARD R ATE FUNCTION OF xyT

Recall that a hazard rate function (HRF) measures the conditional instantaneous rate of fail-

ure at precise time t , given survival to time t . For individual lives in a life insurance context

we refer to the hazard rate as the force of mortality; in the context of a joint status it is more

appropriate to view the hazard rate as a force of failure rather than a force of mortality. Re-

gardless of the terminology used, the HRF is defined as

( )( ) .

( )

xy xy

xy

f t t

S t (12.9a)

In the special case of independent lives we have

( )( ) .

t xy x t y t

xy x t y t t xy

pt

p

(12.9b)

In standard actuarial notation the HRF is denoted by : , x t y t 2 so we have

: . x t y t x t y t (12.9c)

This shows that the force of failure acting on the joint status is the sum of the forces of fail-

ure (or forces of mortality) acting on the individual components (lives) in the case of inde-

pendent lives.

12.1.6 CONDITIONAL PROBABILITIES

The conditional failure probability, denoted by |n xq in the single-life case, has its counter-

part in the joint-life case. We define

| ( 1),n xy xyq Pr n T n (12.10)

the probability that the time of failure of the joint status occurs in the ( 1) st n time interval.3

Since failure of the status occurs with the first failure of the individual components, then

|n xyq denotes the probability that the first failure occurs in the ( 1) st n interval. Thus we

have

1

:

|

(1 )

n xy n xy n xy

n xy x n y n

q p p

p p

(12.11a)

: .n xy x n y n p q (12.11b)

2 Note that the joint status HRF is a function of t , with the identifying characteristics of its component members( x) and ( y) fixed at time 0.t To reinforce the idea that the joint status HRF is a function of t only, some texts

prefer the notation ( ). xy t (See, for example, Section 9.3 of Bowers, et al . [4] .)3 Recall the discrete random variable *, x K defined in Chapter 5 as the time interval of failure for the status ( x). Ifwe now define *

xy K as the random variable for the interval of failure of the joint status ( ), xy we have*

| ( 1).n xy xyq Pr K n




In the case of independent lives we can evaluate |n xyq from the life table using

1 1| .n xy n x n y n x n yq p p p p (12.12)

EXAMPLE 12.2

If xT and yT are independent, calculate the value of 2 | xyq given the following values:

1 2

1 2

.08 .09 .10

.10 .15 .20

x x x

y y y

q q q

q q q

SOLUTION

2 | xyq denotes the probability that the joint-life status fails in the third year, which is also

denoted by *( 3). xy Pr K Here we have

2 2 2: 2

2 2 2 2

|(1 )

(.92)(.91)(.90)(.85)[1 (.90)(.80)] .17932.

xy xy x y

x y x y

q p q p p p p

12.1.7 MOMENTS OF xyT

The expected value of xyT gives the complete expectation of future “lifetime” of the joint

status. It is denoted by xye

in actuarial notation and defined by

0[ ] ( ) xy xy xye E T t f t dt

(12.13a)

0,t xy p dt

(12.13b)

the joint status counterpart to Equation (5.48d). The second moment is given by

2 2

0[ ] ( ) , xy xy E T t f t dt

(12.14)

and the variance of xyT follows as

22( ) [ ] [ ] . xy xy xyVar T E T E T (12.15)

Similarly, we can define the curtate expectation of future lifetime for the joint status, the

counterpart to the single-life function of Section 6.3.4, as

1

xy k xy

k

e p

(12.16)



272 CHAPTER TWELVE

and the temporary curtate lifetime as

:1

.n

k xy xy nk

e p

(12.17)

Note that: xy n

e represents the average number of whole years of survival within the next n

years for the joint status ( ). xy

EXAMPLE 12.3

Let xT and yT be independent time-to-failure random variables, each with exponential dis-

tributions with hazard rates x and , y respectively. Find an expression for . xye

SOLUTION

We know that xt t x p e and ,

t t y p e

so under independence we have

( ). x yt t xy t x t y p p p e

This shows us that xyT has an exponential distribution with hazard rate , x y so

1[ ] . xy xy x y

e E T

12.2 THE LAST-SURVIVOR MODEL

A last - survivor status is one for which survival of the status requires the survival of any one

(or more) of its component members. That is, the status is said to survive as long as at least

one of its members survives, so that it fails only when all of its members have failed. Then thetime of failure of the status is the time of the last failure among its components, or the second

failure in the two-life case. Note that the n-year certain and continuous annuity, defined in Ex-

ample 8.14, is a special case of a last-survivor status, since the annuity pays until the second

failure out of (40) and 10 . The APV of this annuity is denoted40:10

a in actuarial notation.

12.2.1 THE TIME-TO-FAILURE R ANDOM VARIABLE

FOR A LAST-SURVIVOR STATUS

For a two-life last-survivor status composed of the individual lives ( ) x and ( y), we denote

the status itself by ( ) xy and the random variable for the future lifetime of the status by . xyT

Since the status fails on the last failure, then xyT will be the larger of the individual futurelifetimes xT and . yT That is

max( , ). xy x yT T T (12.18)




12.2.2 FUNCTIONS OF THE R ANDOM VARIABLE xyT

If xT and yT are independent, then it follows that the CDF of xyT is

( ) ( ) ( ) ( ). xy xy x y F t Pr T t F t F t (12.19a)

In actuarial notation we would write

.t xy t x t yq q q (12.19b)

The SDF of xyT can then be found as

( ) ( ) 1 ( ). xy xy xyS t Pr T t F t (12.20a)

In actuarial notation, again presuming the independence of xT and , yT we have

1

1

1 (1 )(1 )

.

t xy t xy

t x t y

t x t y

t x t y t x t y

t x t y t xy

p q

q q

p p

p p p

p p p

(12.20b)

Equation (12.20b) is very important, as it allows us to express last-survivor survival proba-

bilities in terms of single-life and joint-life functions, so we will be able to evaluate last-

survivor functions from a single-life survival model or life table. Although Equation

(12.20b) was derived here assuming the independence of xT and , yT the identity is also true

for dependent lifetimes xT and , yT as we shall see in Section 12.2.3.

The PDF of xyT is then found from the SDF as

:

( ) ( )

( )

,

xy xy

t x t y t xy

t x x t t y y t t xy x t y t

d f t S t dt

d p p pdt

p p p

(12.21)

and the HRF follows as

:( )( ) .

( )

xy t x x t t y y t t xy x t y t

xy xy t x t y t xy

f t p p pt

S t p p p

(12.22)



274 CHAPTER TWELVE

The conditional probability for failure of the status in the (n+1) st time interval is

1

1 1 1

| ( 1)

( ) ( )

| | | .

n xy xy

n xy n xy

n x n y n xy n x n y n xy

n x n y n xy

q Pr n T n

p p

p p p p p p

q q q

(12.23)

As with the joint-life function (see Footnote 3), we can define * xy K as the random variable

for the interval of failure of the last-survivor status ( ), xy so we have

*| ( 1).n xy xyq Pr K n (12.24)

EXAMPLE 12.4

Find the probability that the second failure out of independent lives ( ) x and ( ) y occurs in

the fifth year, given the following values:

4 4 5 5.85 .68 .81 .60 x y x y p p p p

SOLUTION

We seek the value of

4 4 5| . xy xy xyq p p

Due to independence we have

4 .85 .68 (.85)(.68) .952 xy p

and

5 .81 .60 (.81)(.60) .924. xy p

Then

4 | .952 .924 .028. xyq

The expected value of xyT gives the complete expectation of future lifetime for the last-

survivor status. We have

0 0[ ] ( ) . xy xy xy t xye E T t f t dt p dt

(12.25a)

Then using Equation (12.20b) for t xy p we have

. xy x y xye e e e

(12.25b)




The corresponding curtate expectation is

1

xy k xy x y xy

k

e p e e e

(12.26)

and the temporary curtate expectation is

: : : :1

.n

k xy xy n x n y n xy nk

e p e e e

(12.27)

EXAMPLE 12.5

Calculate 50:60 ,e

given that 50T and 60T are independent and each follows a survival distri-

bution given by 1100

( ) , x

x

for 0 100. x

SOLUTION

We recognize the given survival model to be uniform with 100, so we have

5050

50,t

t p

for 0 50,t 6040

40,t

t p

for 0 40,t and 50:6050 40

50 40,t

t t p

for

0 40.t We also know that 50e

25 and 60e

20. We find 50:60e

from

40

50:60 50:600

40

0

2 3 400

13

1 (50 )(40 )

20001 2000 45 14.67.

2000

t e p dt

t t dt

t t t

Then

50:60 25 20 14.67 30.33.e

12.2.3 R ELATIONSHIPS BETWEEN xyT AND xyT

Recall that xyT is the time of the first failure between ( ) x and ( ), y and xyT is the time of

the second failure. Then it follows that xyT is one of xT or yT and xyT is necessarily the

other one. Without knowing which one is which, it will still follow that

xy xy x yT T T T (12.28a)

and

. xy xy x yT T T T (12.28b)



276 CHAPTER TWELVE

From Equation (12.28a) we have

, xy x y xyT T T T (12.29)

which, upon taking expectations, leads directly to Equation (12.25b). By similar reasoning

we have ,t xy t xy t x t y p p p p (12.30)

from which Equation (12.20b) follows without assuming independence.

Even if xT and yT are independent, it should be clear that xyT and xyT are not. We can use

Equation (12.28b), along with other earlier results, to find the covariance of xyT and . xyT

We have

( , ) [ ] [ ] [ ] [ ] [ ] [ ], xy xy xy xy xy xy x y xy x y xyCov T T E T T E T E T E T T E T E T T T

by use of Equations (12.28b) and (12.29). But if xT and yT are independent, then

[ ] [ ] [ ], x y x y E T T E T E T

so we have

2

( , ) [ ] [ ] [ ] [ ] [ ] [ ]

.

xy xy x y xy x y xy

x y xy x y xy

x y x xy y xy xy

x xy y xy

Cov T T E T E T E T E T E T E T

e e ee e e

e e e e e e e

e e e e

(12.31)

Note that this covariance is always positive.

12.3 CONTINGENT PROBABILITY FUNCTIONS

For two individual statuses ( ) x and ( ), y we might be interested in the probability that ( ) x

will fail before ( ) y fails. A probability for an event addressing order of failures is called a

contingent probability. The event that ( x) fails before ( ) y is represented by the event. x yT T If xT and yT are independent, then we have

0( ) ( ) ( ) , x y x y Pr T T f t S t dt

(12.32a)




since if ( ) x fails at time t the event x yT T is satisfied if ( ) y has not yet failed at that time,

the probability of which is ( ). yS t In actuarial notation we have

1

0 0( ) . x y xy t x x t t y t xy x t Pr T T q p p dt p dt

(12.32b)

The “upper one” in 1 xyq is used to denote order of failure ( ( ) x must fail before ( y) to satisfy

the event), which we saw earlier in the symbol 1

: x n Z (see Section 7.1). The pre-subscript

indicates that the event is satisfied if ( ) x fails before ( ) y within unlimited time. The proba-

bility that ( ) x fails before ( ) y and within n years is therefore given by

1

0.

n

n xy t xy x t q p dt (12.33a)

Similarly we would have

1

0

.n

n xy t xy y t q p dt

(12.33b)

EXAMPLE 12.6

Show that1 1 .n xy n xy n xyq q q

SOLUTION

The result is intuitive. 1n xyq represents the probability that the first failure occurs before time

n and it is ( ), x and 1n xyq is the same probability with the first failure being ( ). y Together

they represent the probability that the first failure occurs before time n, which is .n xyq

Mathematically, using Equations (12.33a) and (12.33b) we have

1 1

0 0

:0 0( ) .

n n

n xy n xy t xy x t t xy y t

n n

t xy x t y t t xy x t y t n xy

q q p dt p dt

dt p dt q

The event that ( ) x fails after ( ) y is represented by , x yT T and we have

0( ) ( ) ( ) , x y x y Pr T T f t F t dt

(12.34a)

since if ( ) x fails at time t the event x yT T requires that ( ) y has already failed at that time,

the probability of which is ( ). y F t In actuarial notation we have



278 CHAPTER TWELVE

2

0

1

0 0

( ) (1 )

1 ,

x y xy t x x t t y

t x x t t xy x t xy

Pr T T q p p dt

p dt p dt q

(12.34b)

as expected. The probability that ( ) x fails after ( ) y and within n years is then

2 1

0(1 ) .

n

n xy t x x t t y n x n xyq p p dt q q (12.35)

EXAMPLE 12.7

Show that2 2 .n xy n xy n xyq q q

SOLUTION

Again the result is intuitive. Mathematically we have

2 2

0 0

0

(1 ) (1 )

( ) ,

n n

n xy n xy t x x t t y t y y t t x

n

t x x t t y y t t xy x t y t n x n y n xy n xy

q q p p dt p p dt

p p p dt q q q q

since : . x t y t x t y t

12.4 CONTINGENT CONTRACTS INVOLVING MULTI-LIFE STATUSES

All the material presented in Chapters 7 - 11 concerning contingent payment models and

contingent annuities, including their funding and reserving plans, is applicable to the two-

life statuses (joint and last-survivor) defined in this chapter. Indeed, the presentation

throughout Chapters 7 - 11 was made in general terms to facilitate its extension to the multi-

life case. Thus the general concept of a status with identifying characteristic x now can rep-

resent the joint-life status ( ) xy or the last-survivor status ( ). xy Throughout this section we

assume the independence of xT and yT when needed.

It is therefore unnecessary to duplicate the entire presentation in the earlier chapters simply sub-

stituting ( ) xy or ( ) xy for ( ). x Rather we will list only a very small selection of the relationships

we developed earlier, now recast in joint or last-survivor notation, to illustrate this point.

12.4.1 CONTINGENT PAYMENT MODELS (CHAPTER 7 MODELS)

If * xy K denotes the random variable for the time interval in which the joint status ( ) xy fails,

then




* xy K

xy Z v (12.36)

denotes the random variable for the present value of a unit paid at the end of that interval.

Further,

11[ ] |

k

xy xy k xyk A E Z v q

(12.37)

denotes the first moment of xy Z and

2 2 21

1

[ ] ( ) |k xy xy k xy

k

A E Z v q

(12.38)

denotes its second moment, from which the variance follows.

The extension to joint-life term insurance, deferred insurance, pure endowment, and en-

dowment insurance all follow directly from their Chapter 7 counterparts.

Similar remarks hold for continuous contingent payment models. We define

xyT xy Z v (12.39)

as the random variable for the present value of a unit paid at the precise time of failure of

( ), xy with

:0[ ] t

xy xy t xy x t y t A E Z v p dt

(12.40)

and

2

2 2:0

[ ] t xy xy t xy x t y t A E Z v p dt

(12.41)

as its first and second moments.

The same definitions apply for the last-survivor status, with some notational adjustments.

We have, for example,

1

1

[ ] | .k xy xy k xy

k

A E Z v q

(12.42a)

Substituting for 1|k xyq from Equation (12.23) we then have

. xy x y xy A A A A (12.42b)

In the case of the continuous function we have



280 CHAPTER TWELVE

0

:0

[ ] ( )

( )

,

t xy xy xy

t t x x t t y y t t xy x t y t

x y xy

A E Z v f t dt

v p p p dt

A A A

(12.42c)

using Equation (12.21) for the PDF of . xyT

12.4.2 CONTINGENT ANNUITY MODELS (CHAPTER 8 MODELS)

We can again substitute the joint-life status ( xy) or the last-survivor status ( ) xy for the generic

status of interest ( x) used in Chapter 8 to define annuities payable as long as a joint-life or last-

survivor status continues to survive. As in Chapter 8, such annuities can be paid as immediate,

due, or continuous. We would have, for example,

1

,k xy k xy

k

a v p

(12.43)

0

,k xy k xy

k

a v p

(12.44)

: 0

nt

t xy xy na v p dt (12.45)

and

: :

1 , xy n xy n

n xy

s a E

(12.46)

where

.nn xy n xy n x n y n xy E v p E E E (12.47)

The relationship between insurance and annuity functions, presented in Chapter 8, will also

hold in the multi-life cases. We would have, for example,

1 xy xy A d a (12.48)

and

1 . xy xy A a (12.49)

12.4.3 ANNUAL PREMIUMS AND R ESERVES

The annual benefit premium for a whole life joint-life insurance of unit amount is given by

the equivalence principle as

. xy

xy xy

A P

a

(12.50)

Note that since the contingent benefit pays on the first failure, the funding scheme should

stop at the first failure as well. In the case of a last-survivor status, we have




xy xy

xy

A P

a

(12.51)

if the funding continues until the second failure at which point the benefit is paid.

EXAMPLE 12.8

A contingent contract pays a benefit of amount b at the end of the year of the second failure

of independent lives ( ) x and ( ). y The net annual premium is 110 paid at the beginning of

each year while both ( ) x and ( ) y survive and 40 per year after the first failure. Find the

value of b, given the following values:

.80 8 7 .05 xy x y A a a d

SOLUTION

The APV of the benefit is . xyb A The APV of the premium stream is

110 40( ). xy xy xya a a

By the equivalence principle we have

( ) 40 40 30 x y xy x y xyb A A A a a a

or

1 .80[1 (.05)(8) 1 (.05)(7) .80] (40)(8) (40)(7) (30) ,.05

b

which solves for 1600.b

Benefit terminal reserves can easily be defined by the prospective method. For the joint-life

insurance we have

: :t xy x t y t xy x t y t V A P a (12.52)

if the status has not yet failed as of time t . For the last-survivor status, a bit more care is

needed. Although the status ( ) xy has not yet failed at time t , it is possible that one of its

component members may have already failed. Thus there are three cases for the reserve at

duration t , namely

: :t xy xy x t y t x t y t V A P a

(12.53a)

if both components ( ) x and ( ) y still survive,

t xy x t xy x t V A P a (12.53b)



282 CHAPTER TWELVE

if ( ) y has already failed and only ( ) x survives, or

t xy y t xy y t V A P a (12.53c)

if ( ) x has already failed and only ( ) y survives.

12.4.4 R EVERSIONARY ANNUITIES

A special type of two-life annuity is one that pays only after one of the lives has failed, and

then for as long as the other continues to survive. Such annuities are called reversionary an-

nuities.

If payment is made to the status ( y), provided it has not failed, but only after the failure ofthe status ( x), then the total condition for payment at time k is that ( x) has failed but ( y) has

not. The probability of this is

(1 ) ,k x k y k y k x k y k xyq p p p p p (12.54)

so the actuarial present value of a reversionary annuity payable under such circumstances

would be

|

1

( ) .k x y k y k xy y xy

k

a v p p a a

(12.55)

The result is intuitive. ya represents the APV of payment made for the lifetime of ( ), y and

xya is the APV of payment made for the joint lifetime of ( xy). We can read Equation (12.55)

as providing payment as long as ( ) y survives, but taking it away while ( ) x also survives, with

the net effect being payment made while ( ) y survives but after the failure of ( ). x

If the payment is made for n years at most, with the requirement that ( ) y has failed but ( ) x

has not, the APV would be

1

1

| :

: :( ) .

nk

k x k y

k

nk

k x k xy

k

y x n

x n xy n

a v p q

v p p a a

(12.56)

In the continuous case we have

| 0

0( ) .

t x y t y t x

t t y t xy y xy

a v p q dt

v p p dt a a

(12.57)




If a reversionary annuity is funded by annual premiums, the length of the premium-paying

period would be the joint lifetime, since upon failure of the joint status either payments

begin or the contract expires without value. If payments are to be made to ( x) after the fail-

ure of ( y), then use of the equivalence principle leads to the annual benefit premium

|

|( ) . y x x xy

y x xy xy

a a a

P a a a

(12.58)

The benefit reserve at duration t would again depend on what combination of ( x) and ( y) stillsurvive. If both are alive, the reversionary annuity contract is still in premium-paying status

so the reserve is

| | | :( ) ( ) .t y x y t x t y x x t y t V a a P a a (12.59a)

If ( x) only is alive the contract is beyond the premium-paying period and in payout status, so

the reserve is simply

|( ) .t y x x t V a a (12.59b)

If ( y) only is alive, no payment will ever be made so the contract has expired and the reserve

is zero.

EXAMPLE 12.9

Show that | | . xy x y y x xya a a a

SOLUTION

The result is intuitive. | x ya represents payments made if ( y) is alive but ( x) is not, | y xa repre-

sents payments made if ( x) is alive but ( y) is not, and xya represents payments made if both

are alive. The three cases are mutually exclusive. Together they provide payments if either

( x) or ( y) is alive, which is represented by . xya Mathematically,

| |

,

x y y x xy y xy x xy xy

x y xy

xy

a a a a a a a a

a a a

a

as required.

12.4.5 CONTINGENT INSURANCE FUNCTIONS

A contingent insurance is one for which payment of the unit benefit depends on the order of

failure among its component members. The insurance functions follow from the probability

functions introduced in Section 12.3. Here we consider only insurances with immediate

payment of claims.



284 CHAPTER TWELVE

A contingent insurance benefit paid at the failure of ( x) only if ( x) fails before ( y) has APV

given by

1

0.t

xy t xy x t A v p dt

(12.60)

If the benefit is paid at the failure of ( x) only if ( x) fails after ( y), the APV is

2 1

0(1 ) .t

xy t x x t t y x xy A v p p dt A A

(12.61)

EXAMPLE 12.10

Show that1 1

xy xy xy A A A

and2 2 . xy xy xy A A A

SOLUTION

1 1

0 0

:0

t t xy xy t xy x t t xy y t

t t xy x t y t xy

A A v p dt v p dt

v p dt A

2 2 11

1 1

xy xy x xy y xy

x y xy xy

x y xy xy

A A A A A A

A A A A

A A A A

12.5 MULTI-STATE MODEL R EPRESENTATION

The multi-life models presented thus far in this chapter can be easily represented as multi-

state models. We illustrate this idea in the two-life case, with extension to three or more

lives being apparent.

12.5.1 THE GENERAL MODEL

Consider two persons alive at ages x and y, respectively, at time t . The model is in State 1 as

long as both lives continue to survive. Since both lives are surviving at time t , it follows thatthe process begins in State 1 at that time. The model is illustrated in Figure 12.1 on the fol-

lowing page.




State 1

(Both ( x) and ( y)

Surviving)

State 2

(( x) Surviving

but ( y) Failed)

State 3

(( y) Surviving

but ( x) Failed)

State 4

(Both ( x) and ( y)

Failed)

FIGURE 12.1

Since the only decrement is failure, or death, it follows that any state, once left, cannot be

reentered. Therefore the event of being in State 1 at time ,t r given in State 1 at time t , is

the same as the event of never leaving State 1 over that time interval.

Similarly, the event of being in State i, for 2i or 3, at any time after entering that state is

the same as the event of never leaving that state once it has been entered. Note that transition

from State 2 to State 3, or from State 3 to State 2, is not possible.

Clearly State 4 is an absorbing state, which can never be left once it has been entered. We

also assume, in this model, that the simultaneous failure of ( ) x and ( ) y is not possible, so

direct transition from State 1 to State 4 cannot occur.4

12.5.2 THE JOINT-LIFE MODEL

By definition, the joint-life status continues to survive as long as the process is in State 1,and fails when the process transitions to either State 2 or State 3. For a discrete process

known to be in State 1 at time n, the probability of being in State 1 at time n r is

( ) *( )11 11[ 1 | 1] ,n n

n r n r r Pr X X p p (12.62a)

and for a continuous process known to be in State 1 at time t , the probability of being in

State 1 at time t r is

( ) *( )11 11[ ( ) 1 | ( ) 1] .t t

r r Pr X t r X t p p (12.62b)

In the single-life case, for a person alive (i.e., in State 1) at age x at discrete time n (orcontinuous time t ), the probability of being in State 1 at discrete time n r (or continuous

time )t r is r x p in all cases. That is,

4 This restriction is relaxed in Section 12.8.



286 CHAPTER TWELVE

( ) *( ) ( ) *( )11 11 11 11 .n n t t

r x r r r r p p p p p (12.63a)

In the multi-life case, assuming independence of ( ) x and ( ), y the corresponding probability

is .r xy r x r y p p p That is,

( ) *( ) ( ) *( )11 11 11 11 .n n t t

r xy r x r y r r r r p p p p p p p (12.63b)

For the joint-life model, we can write the APVs for annuities and insurances in multi-state

model notation by analogy to the single-life model as presented in Section 8.6 (for annuities)

and Section 7.6 (for insurances). Two examples of this are presented in Example 12.11, with

others left to the exercises (see Exercise 12-18).

EXAMPLE 12.11

Write the APVs for (a) the joint-life annuity-due and (b) the continuous joint-life insurance

in multi-state model notation.

SOLUTION

(a) For two persons alive at ages x and y, respectively, at time 0, the APV of a unit annuity-

due, by analogy to Equation (8.96), is

(0)11

0

,k xy k

k

a v p

(12.64)

where (0)11 ,k k xy k x k y p p p if ( ) x and ( ) y are independent.

(b) The unit continuous joint-life insurance is payable at the instant of transition from State1 to State 2 or from State 1 to State 3. These events are mutually exclusive, so the APV,

by analogy to Equation (7.80), is

(0)12 13110

( ) ( ) ,r xy r A v p r r dr

(12.65)

where (0)11r is defined in part (a), 12 ( ) , y r r and 13( ) . x r r

12.5.3 R EVERSIONARY ANNUITIES

Recall from Section 12.4.4 that the reversionary annuity with APV given by | y xa is payableto ( ) x after the failure of ( ). y In the multi-state model context, such an annuity is payable

if the process is in State 2. Then we would write




(0)| 12

1

,k y x k

k

a v p

(12.66)

where (0)12 .k k x k y k x k xy p q p p The APV for a reversionary annuity payable to ( ) y

after the failure of ( ) x follows by symmetry. (See Exercise 12-19.)

12.5.4 CONTINGENT INSURANCE FUNCTIONS

The continuous contingent insurance with APV given in standard actuarial notation by 1 xy A

is payable at the instant of transition from State 1 to State 3 in the multi-state model context.

The one with APV given by 2 xy A is payable at the instant of transition from State 2 to State

4. The reader is asked to write the APVs for these, and other, contingent insurances in multi-

state model notation in Exercise 12-20.

12.5.5 THE LAST-SURVIVOR MODEL

The last-survivor status continues to exist while any of its component members survive, so,

in the multi-state model context, the status is surviving while the process is in any of States

1, 2, or 3, and the status fails when the process transitions to State 4 from either State 2 or

State 3. APVs for last-survivor functions can be written in multi-state model notation by

analogy to APVs developed earlier. This is illustrated in the following example and in Exer-cise 12-21.

EXAMPLE 12.12

For two lives known to be surviving at time 0 at ages x and y, write the APVs for (a) the last-

survivor immediate annuity and (b) the last-survivor continuous whole life insurance.

SOLUTION

(a) The annuity is payable at the end of each year while either (or both) of ( x) and ( y) sur-

vive, which means the process is in any one of States 1, 2, or 3. These events are mutu-

ally exclusive, so the APV is

(0) (0) (0)11 12 13

1

,r r r r xy

r

a v p p p

(12.67a)

where (0)11 ,r r x r y p p p (0)

12 ,r r x r y p q and (0)13 ,r r y r x p p q assuming independ-

ence of ( ) x and ( ). y

(b) The insurance is payable at the instant of the second failure, with APV denoted by . xy

A

In the multi-state model context, it is payable at the instant of transition from State 2 to

State 4 or from State 3 to State 4. The APV is



288 CHAPTER TWELVE

(0) (0)24 3412 130

( ) ( ) ,r r r xy

A v p r p r dr

(12.67b)

where (0)12 ,r r x r y p q (0)

13 ,r r y r x p q 24 ( ) , x r r and 34 ( ) . y r r

12.5.6 SOLVING THE K OLMOGOROV FORWARD EQUATION

In this section we consider the Kolmogorov differential equation presented in Section 3.2.2

as it applies to the multi-life model of Section 12.5.1. As shown by the arrows in Figure12.1, not all transitions between states are possible, so the only non-zero forces of transition

are 12 ( ), s 13( ), s 24( ), s and 34 ( ). s

Suppose ( ) x and ( ) y are both alive at those ages at general time t , so that the process is in

State 1. Then ( )11

t r p denotes the probability that the process is still in State 1 (i.e., both are

still surviving) at time ,t r and ( )12

t r p denotes the probability that the process is in State 2

(i.e., ( ) x is surviving but ( ) y has failed) at time .t r (Note that ( )

13

t

r p is the same as ( )

12,t

r p

with the roles of ( ) x and ( ) y reversed.)

If the process is in State 2 at time t , then both ( )22t

r p and ( )24t

r p denote concepts of interest to

us. (Note that ( ) ( )21 23 0.)t t

r r p p Furthermore, both ( )33t

r p and ( )34t

r p are also concepts of

interest, but they are the same as ( )22t

r p and ( )24 ,t

r respectively, with the roles of ( ) x and

( ) y reversed.

It is clear that, for a process in State 4 at time t , ( ) ( ) ( )41 42 43 0t t t

r r r p p p and ( )44 1t

r p for all r .

The above analysis suggests that we need solve the Kolmogorov equation only for ( )11 ,t n p ( )12 ,t

n p ( )22 ,t

n p and ( )24 .t

n p We do this for ( )12

t n p in the following example, and defer the others

three cases to Exercises 12-22 and 12-23.

EXAMPLE 12.13

The symbol ( )12

t n denotes the probability that a process in State 1 at time t will be in State 2

at time ,t n where State 1 means both ( ) x and ( ) y are alive at those ages. To be in State 2

at time t n requires that ( ) y fails in, and ( ) x survives over, the interval ( , ].t t n (In actu-

arial notation this is denoted by ,n x n y p q assuming that ( ) x and ( ) y are independent

lives.) Show that the Kolmogorov equation can be solved for this result.

SOLUTION

In Equation (3.14a), 1i and 2 j so k takes on the values 1, 3, 4 in the summation. Then

we have




( ) ( ) ( )12 2112 11 12

( ) ( )32 2313 12

( ) ( )42 2414 12

( ) ( )12 2411 12

( ) ( ) (at 1)

( ) ( ) (at 3)

( ) ( ) (at 4)

( ) ( ),

t t t r r r

t t r r

t t r r

t t r r

d p p t r p t r k dr

p t r p t r k

p t r p t r k

p t r p t r

since 21 32 23 42( ) ( ) ( ) ( ) 0 s s s s for all s. Then integrating from 0r to r n we

have

( ) ( ) ( )12 2412 11 120 0 0

( ) ( ) .n n nt t t

r r r d p p t r dr p t r dr

The left side integrates to ( )12 ,t

n p as expected, since ( )0 12 0.t p The right side is more easily

understood when written in actuarial notation. We have

0 0,

n n

r xy y r r x r y x r p dr p q dr

since 12 ( )t r is the force of failure for ( ) y at age y r and 24 ( )t r is the force of fail-

ure for ( ) x at age . x r We can represent both integrals in contingent probability notation,

from Section 12.3, obtaining the result

( ) 2112 .t

n n xy n xy p q q

But 2 1 ,n xy n x n xyq q q so we can write our result as

( ) 1 112 ( )

1 1 (1 ) ,

t n n xy n x n xy

n xy n x

n x n y n x n x n x n y n x n y n x n y

p q q q

q q

p p p p p p p p p q

as requested.

12.6 GENERAL R ANDOM VARIABLE ANALYSIS

Consider again the case of two individual lives ( x) and ( y), with time-to-failure (or future

lifetime) random variables xT and , yT respectively. When xT and yT are independent, we

have seen how all discrete multi-life functions can ultimately be evaluated from a single-life

survival model, usually in life table form. But how do we evaluate multi-life functions ifindependence does not hold?



290 CHAPTER TWELVE

In theory we can proceed according to the general rules regarding the joint distribution of

two random variables, as presented in a basic probability course and reviewed in Section 2.4

of this text. We presume that we are given a joint density function of the random variables

xT and , yT denoted by , ( , ), x y x y f t t for 0 xt and 0, yt where xT and yT are not neces-

sarily independent.5 From the joint density function we can then determine all other desired

functions.

12.6.1 MARGINAL DISTRIBUTIONS OF x T AND yT

We can find the marginal densities of xT and yT by integrating the joint density function

across the opposite variable. Thus we have

,( ) ( , ) y

x x x y x y yt f t f t t dt (12.68a)

and

,( ) ( , ) . x

y y x y x y xt t f t t dt (12.68b)

From the separate marginal distributions we can evaluate any probability or other single-lifefunction as described in earlier chapters.

12.6.2 THE COVARIANCE OF x T AND yT

From the joint PDF of xT and yT we can find

,0 0[ ] ( , ) . x y x y x y x y x y E T T t t f t t dt dt

(12.69)

Then from the separate marginal PDF’s of xT and yT we find E [T x] and [ ], y E T and there-

fore

( , ) [ ] [ ] [ ]. x y x y x yCov T T E T T E T E T (12.70)

EXAMPLE 12.14

An electronic device relies on two essential components, denoted ( x) and ( y), for its opera-

tion. The joint density function of the lifetimes of ( x) and ( y), measured in months, is

, ( , ) ,27

x y x y x y

t t

f t t

for 0 3 xt and 0 3. yt

5 The case where xT and yT are independent will be contained within the general analysis as a special case.




(a) Show that xT and yT are not independent.

(b) Find the correlation coefficient of xT and . yT

SOLUTION

First we find the marginal density of xT as

3

,0

33

2

0 0

( ) ( , )

2 31 1 1 9( ) 3 ,27 2 27 2 18

x x x y x y y

x x y y x y y x

f t f t t dt

t t t dt t t t t

for 0 3. xt By the symmetry of the joint PDF it follows that

2 3( ) ,

18

y

y y

t f t

for 0 3. yt Next we find

3

3 32 3 2

0 00

1 1 2 3[ ] ( ) (2 3 ) 1.7518 18 3 2 x x x x x x x x x x E T t f t dt t t dt t t

and

3

3 32 2 3 2 4 3

0 0 0

1 1 1[ ] ( ) (2 3 ) 3.75,18 18 2 x x x x x x x x x x E T t f t dt t t dt t t

so

2( ) 3.75 (1.75) .6875. xVar T

Again by symmetry the same values apply to . yT Directly from the joint PDF we find

3 3

,0 0

3 32 2

0 0

33

3 2 2

0 0

32

0

32 3

0

[ ] ( , )

1 ( )27

1 1 127 3 2

1 9 4.527

1 4.5 1.5 3.27

x y x y x y x y x y

x y x y x y

x y x y y

y y y

y y

E T T t t f t t dt dt

t t t t dt dt

t t t t dt

t t dt

t t



292 CHAPTER TWELVE

(a)

We observe that

,( ) ( ) ( , ), x x y y x y x yt f t f t t

so xT and yT are not independent.

(b) From the results developed above we find

( , ) 3 (1.75)(1.75) .0625 x yCov T T

and therefore

,2

( , ) .0625 .09091.( ) ( ) (.6875)

x y

x y

T T

x y

Cov T T

Var T Var T

12.6.3 OTHER JOINT FUNCTIONS OF x T AND yT

If the individual domains of xT and yT are both all positive values, then their joint domain

will be the entire first quadrant, as illustrated in the following figure.

yt

( , ) x yt t

xt

FIGURE 12.2

The joint CDF of xT and yT is given by

, ,0 0( , ) ( ) ( , )

y xt t

x y x y x x y y x y F t t Pr T t T t f r s dr ds (12.71)

for 0 xt and 0, yt and , ( , ) 0 x y x y F t t for any other values of xt and/or . yt For exam-

ple, consider the case where 0 xt and 0, yt so the point ( , ) x yt t lies in the second

quadrant. Since the joint CDF represents ( ), x x y y Pr T t T t it is easy to see that the

joint CDF is zero, since the event x xT t cannot occur. Similar reasoning applies to a point

( , ) x yt t lying in the third or fourth quadrants.




If the joint domain of xT and yT does not cover the entire first quadrant, the joint CDF can

be non-zero in portions of the first quadrant where the joint PDF is zero. This is illustrated in

the following example.

EXAMPLE 12.15

Find the joint CDF for the joint distribution of Example 12.14.

SOLUTION

Consider the following figure:

yt

C D

3

A B

( , ) x yt t

0 xt

3

FIGURE 12.3

The joint PDF is non-zero only within Region A of the first quadrant. Within Region A the

joint CDF of xT and yT is given by

, ,0 0

0 0

2

0 0

2

0

2 2

2 2

0

( , ) ( , )

1 ( )27

1 127 2

1 127 2

1 1 1 ,

27 2 2 54

y x

y x

x y

y

y

t t

x y x y x y

t t

t t

t

x x

t x y x y

x x

F t t f r s dr ds

r s dr ds

r rs ds

t t s ds

t t t t t s t s

for 0 3 xt and 0 3. yt For a point ( , ) x yt t in Region B, the event x xT t is certain to

occur, since 3 xt in Region B. Therefore the joint CDF, which is ( ), x x y y Pr T t T t is the



294 CHAPTER TWELVE

same as ( ) y y Pr T t since ( ) 1. x x Pr T t Then we see that

, ( , ) ( ) x y x y y y F t t F t

for 3 xt and 0 3. yt By similar reasoning, for any point in Region C we have

, ( , ) ( ) x y x y x x F t t F t

for 0 3 xt and 3. yt In Region D both events x xT t and y yT t are certain to occur

so we have

, ( , ) 1 x y x y F t t

for 3 xt and 3. yt

The joint SDF of xT and yT is given by

, ,( , ) ( , ) , y x

x y x y x x y y x yt t S t t Pr T t T t f r s dr ds

(12.72)

for 0 xt and 0. yt For a ( , ) x yt t point in the second quadrant, the event x xT t is cer-

tain to occur so ( ) x x y y Pr T t T t is the same as ( ) ( ). y y y T y Pr T t S t Similar reasoning

for a ( , ) x yt t point in the fourth quadrant shows that ( ) x x y y Pr T t T t is the same as

( ) x x Pr T t ( ). xT xS t In the third quadrant, both events x xT t and y yT t are certain to

occur so , ( , ) 1 x y x yS t t for 0 xt and 0. yt

12.6.4 JOINT AND LAST-SURVIVOR STATUS FUNCTIONS

If the general joint SDF of xT and yT is evaluated at a common point, say , x yt t n then

we have

, ( , ) ( ) , x y x y xy n xyS n n Pr T n T n Pr T n p (12.73)

in actuarial notation. Therefore all joint life functions presented in Section 12.1 can be eval-

uated from the general joint SDF of xT and . yT Note that the relationship n xy n x n y p p p will

generally hold only if xT and yT are independent.

EXAMPLE 12.16

Suppose the device described in Example 12.14 fails upon the first failure of its two essen-

tial components. Find the probability that the device fails during its first month of operation.




SOLUTION

Since the device fails on the first failure of its components, then the two components form a

joint status. We seek the probability value given by 1 . xy xyq p The joint SDF of xT and

yT in Example 12.14 is given by

3 3

,1( , ) ( ) ,27 y x

x y x y t t S t t r s dr ds

so the joint status probability xy p is given by

3 3

, 1 1

33

2

1 1

3

1

32

1

1(1,1) ( )27

1 127 2

1 (4 2 )27

1 164 .27 27

xy x y p S r s dr ds

r rs ds

s ds

s s

Finally the desired probability is

16 111 1 .27 27 xy xyq p

If the general joint CDF of xT and yT is evaluated at the common point , x yt t n then we

have

, ( , ) ( ) , x y x y xy n xy F n n Pr T n T n Pr T n q

in actuarial notation. Thus we see that both the joint and last-survivor distributions can be

determined from the general joint distribution of xT and , yT where we find the SDF of the

joint status from the joint SDF of xT and yT and we find the CDF of the last-survivor status

from the joint CDF of xT and . yT

12.7 COMMON SHOCK – A MODEL FOR LIFETIME DEPENDENCY

A natural example of two lives ( ) x and ( ) y having dependent lifetimes might be a married

couple or a pair of business partners who travel together, or are otherwise exposed to a common

hazard factor, on a regular basis. We refer to this common hazard as a common shock .

To analyze the common shock model, we begin by hypothesizing that ( ) x is subject to a

hazard rate, or force of failure, denoted * , x t that takes into account only failure forces op-



296 CHAPTER TWELVE

erating at time t that are specific to ( ) x and not to ( ). y Similarly, * y t denotes failure forc-

es operating at time t that are applicable to ( ) y but not to ( ). x With regard only to the fail-

ure forces represented by * x t and * , y t we consider that ( ) x and ( ) y have independent

survival patterns because of the nature of the hazard factors included in * x t and * . y t

Next we assume that the common hazard, or common shock, to which both ( ) x and ( ) y are

subject can be represented by a constant (over time) hazard function, which we denote by

.ct We assume that the hazard forces unique to ( ), x reflected in * , x t and the hazard

forces reflected in the common shock hazard function ct are non-overlapping, so the total

force of failure to which ( ) x is subject is given by

* * .c

x t x t t x t (12.74a)

Similarly, the force of failure to which ( ) y is subject is given by

* * .c

y t y t t y t (12.74b)

The total force of failure operating on the joint-life status ( ) xy is given by

* *: . x t y t x t y t (12.75)

The three forces are additive because each represents hazard factors that are disjoint from each

other. Furthermore, the total joint force of failure contains only one (not two) since failure

of either ( ) x or ( ) y due to the common shock constitutes failure of the joint-life status.

The joint-life survival function in the common shock model can then be found from the joint-life force of failure. We have

0 :

* *0

**00 0

( )

* * .

t

x r y r

t

x r y r

t t t y r x r

dr t xy

dr

dr dr dr

t t x t y

p e

e

e e e

p p e

(12.76)

Note that *t x p is not the same as ,t x p since the latter survival probability takes into account

the common shock hazard factors but the former does not.

There is an alternative, and equivalent, way to develop the common shock model. Let * xT

and * yT denote the future lifetime random variables for ( ) x and ( ), y respectively, without

regard for the common shock hazard factors. Consistent with the discussion above, we as-




sume that * xT and *

yT are independent. Let S denote the future lifetime of either ( ) x or ( ) y

with regard to the common shock hazard factors only, where S is independent of both * xT

and *. yT Then it follows that the overall lifetime random variable for ( ) x is given by

*

min( , ), x xT T S

(12.77a)

and similarly the overall future lifetime random variable for ( ) y is given by

*min( , ). y yT T S (12.77b)

Note that whereas * xT and *

yT are independent, xT and yT are not independent since they

both involve the common shock hazard factors and depend on the common future lifetime

random variable S .

Since * xT and S are independent, however, it follows that the SDF of xT is the product of the

SDF’s of * xT and S . That is,

**( ) ( ) ( ) ,t

t x x x S t x p S t S t S t p e (12.78a)

where ( ) t S S t e since the random variable S has a constant hazard rate and therefore an

exponential distribution. Similarly, the SDF of yT is given by

**( ) ( ) ( ) .t

t y y y S t y p S t S t S t p e (12.78b)

We observe that ,t xy p given by Equation (12.76), is not equal to the product of t x p and

,t y p given by Equations (12.78a) and (12.78b), respectively, which shows that xT and yT

are not independent random variables.

EXAMPLE 12.17

A convenient way to illustrate the common shock model is to assume that both * xT and *

yT

have exponential distributions, and therefore constant hazard rates x and , y respectively.

Under this assumption, find an expression for the APV of a last-survivor whole life insur-

ance, with benefit paid at the moment of failure of the status.

SOLUTION

The overall force of failure for ( ) x is then , x t x where denotes the hazard of the

common shock, and the overall force of failure for ( ) y is . y t y The force of failure

for the joint-life status ( ) xy is : . x t y t x y Using results developed in Chapter 7,



298 CHAPTER TWELVE

State 1

(Both ( x) and ( y)

Surviving)

State 2

(( x) Surviving

but ( y) Failed)

State 3

(( y) Surviving

but ( x) Failed)

State 4

(Both ( x) and ( y)

Failed)

we then have

, x x

x

A

, y

y

y

A

and

, x y

xy x y

A

and xy A then follows as . x y xy A A A

Because of the common hazard factor, such as travel accidents, there is a non-zero probabil-ity of simultaneous failure of ( ) x and ( ). y When represented as a multi-state model, theearlier Figure 12.1 is modified as shown in Figure 12.4.

Figure 12.4

The only change in this model from our Figure 12.1 model is that 14 ( ), s the force of transi-

tion function for transition directly from State 1 to State 4 is now non-zero. This means that

the probability value

( )14 ( ) 4 | ( ) 1t

r p Pr X t r X t

can be satisfied by direct transition from State 1 to State 4. In the Figure 12.1 model, ( )14

t r p

could be satisfied only by transition from State 1 to State 2 to State 4 or by transition from

State 1 to State 3 to State 4.




12.8 EXERCISES

12.1 The Joint-Life Model

12-1 Show that

, xy x x yq q p q

assuming independence of the individual lifetimes xT and . yT

12-2 Let xT and yT be independent future lifetime random variables. Given .080, xq

2.004, 1 , y t x xq p t q and 21 ,t y y p t q both for 0 t 1, evaluate the PDF

of xyT at .50.t

12-3 Two microwave models, denoted Type I and Type II, follow survival models defined by

ln1.25, I x for 0, x and 1

9, II

x x

for 0 9, x respectively. Given that

both models are currently two years old, and that they have independent lifetimes,

find the probability that the first failure will occur between ages 3 and 6.

12-4 At all ages greater than 50, the force of failure for smokers is double that for non-

smokers. Suppose the age-at-failure random variable for nonsmokers has a uniform

distribution with 75. If (65) and (55) have independent lifetimes, where (65) is

a nonsmoker and (55) is a smoker, find the value of 65:55 .e

12-5 Let xT and yT be independent future lifetime random variables, each with an expo-

nential distribution with mean 20. Find the value of ( ). xyVar T

12.2 The Last-Survivor Model

12-6 Let 80T and 85T be independent random variables with uniform distributions with

100. Find the probability that the second failure occurs within five years.

12-7 Derive each of the following identities. Which of them require an assumption of theindependence of xT and ? yT

(a) t xy t x t y t xyq q q q

(b) ( ) xyT t x t y y t t y t x x t f t q p q p

(c) | | | | |n xy n x n y n y n x n x n yq q q q q q q



300 CHAPTER TWELVE

12-8 If xT and yT are independent, and each is uniformly distributed over each year of

age separately, show that

1/3 1/218( ) 12( ) . xy xy xyq q q

12-9 For independent lives ( ) x and ( ), y the force of failure is constant over each year of

age. Find the value of .75 .25: .25,

x y p

given that .08 xq and .06. yq

12-10 For the model of Exercise 12-5, find the value of ( , ). xy xyCov T T

12.3 Contingent Probability Functions

12-11 Coffee Maker I follows a survival model defined by 1.809

, I x x

for 0 9, x and

Coffee Maker II follows a survival model defined by 1.509 , II x x for 0 9. x The

two coffee makers have independent lifetimes. Find the probability that Coffee

Maker I fails before Coffee Maker II.

12.4 Contingent Contracts Involving Multi-Life Statuses

12-12 Let Z denote the present value random variable for a contingent contract that pays a

unit benefit at the end of the year of the first failure between ( ) x and ( ), y and an-

other unit benefit at the end of the year of the second failure. Find the value of

[ ], E Z given the values 9, 13, and .04. x ya a i

12-13 Let Y denote the present value random variable for a contingent annuity-due with

unit payment made during the first 15 years if at least one of ( ) x and ( ) y survive,

but made after the first 15 years only if exactly one of ( ) x and ( ) y survive. Find the

value of [ ], E Y given the following values:

159.80 11.60 7.60 | 3.70 x y xy xya a a a

12-14 Let * xy K denote the year of failure of the joint-life status ( ). xy Find the value of the

discount rate d , given the values *210, 7, and ( ) 27.

xy xy xy K a a Var a

12-15 Two lives ( ) x and ( ) y have independent and identically distributed future lifetimes.

Given the values .10, .06, x y xy P P P and .06,d find the value of . xy P




12-16 A discrete unit benefit contingent contract is issued to the last-survivor status ( ), xx

where the two future lifetime random variables xT are independent. The contract is

funded by discrete net annual premiums, which are reduced by 25% after the first

failure. Find the value of the initial net annual premium, under the equivalence prin-

ciple, given the values .40, .55, and 10.00. x xx x A A a

12-17 A continuous two-life annuity has actuarial present value 1180. The annuity pays at

annual rate 100 while both ( ) x and ( ) y survive, 70 while ( ) x survives after the

failure of ( ), y and 50 while ( ) y survives after the failure of ( ). x Find the value of

, xya given that 12 xa and 10. ya

12.5 Multi-State Model Representation

12-18 Using the multi-state model notation defined earlier, give formulas for each of the

following joint-life functions.(a)

: xy na (b) xy P (c)

: xy n A (d) 1

:( )

xy n P A

12-19 Using the model shown in Figure 12.1, give the formula for | x ya in multi-state model

notation.


following continuous contingent insurance functions.

(a)1

xy A (b)1

xy A (c)2

xy A (d)2

xy A


following last-survival functions.

(a) xy

a (b): xy n

a (c) 1: xy n

A (d) |n xy A

12-22 For two persons alive at ages x and y at time t , solve the Kolmogorov differential

equation for ( )11 ,t

n p writing the result in standard actuarial notation.

12-23 For a person alive at age x at time t , with ( ) y failed, solve the Kolmogorov differen-

tial equation for (a) ( )22t

n p and (b) ( )24 ,t

n p writing the results in standard actuarial no-

tation.



302 CHAPTER TWELVE

12.6 General Random Variable Analysis

12-24 Consider the joint density function of xT and yT given by

, 3

4( , ) ,(1 2 )

x y x y x y

f t t t t

for 0 xt and 0. yt Show that xT and yT are not independent.

12-25 For the survival model of Exercise 12-24, find ( ), xS t the marginal survival function

of . xT

12-26 For the survival model of Exercise 12-24, evaluate , (1,2). x y F

12-27 For the survival model of Exercise 12-24, evaluate , (1,2). x yS

12-28 For the survival model of Exercise 12-24, find an expression for .n xy

12-29 For the survival model of Exercise 12-24, find an expression for .n xyq

12.7 Common Shock – A Model for Lifetime Dependency

12-30 The APV for a last-survivor whole life insurance on ( ), xy with unit benefit paid at

the instant of failure of the status, was calculated assuming independent future life-

times for ( ) x and ( ) y with constant hazard rate .06 for each. It is now discovered

that although the total hazard rate of .06 is correct, the two lifetimes are not independent since each includes a common shock hazard factor with constant force .02.

The force of interest used in the calculation is .05. Calculate the increase in the

APV that results from recognition of the common shock element.

12-31 Lives ( ) x and ( ) y have independent future lifetime exponential random variables

* xT and *

yT with respect to risk factors unique to ( ) x and ( ), y respectively. As well,

both ( ) x and ( ) y are subject to a constant common hazard rate .01. Given that

.96 x p and .97, y p calculate the value of 5 . xy

12-32 For two persons alive at ages x and y at time t , show that the Kolmogorov differen-

tial equation for ( )14

t r p solves for

( )14 ,t

xyn n xy p q e

where is the constant common shock hazard described in Section 12.7.

Models for Quantifying Risk

Documents