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Capital Markets and PortfolioTheory
Roland PortaitFrom the class notes taken by Peng Cheng
Novembre 2000

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Table of Contents
Table of Contents
PART I Standard (One Period) Portfolio Theory . . . . . . . . . . . . . . . . . . . . . 1
1 Portfolio Choices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.A Framework and notations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.A.i No Riskfree Asset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.A.ii With Riskfree Asset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.B Efficient portfolio in absence of a riskfree asset . . . . . . . . . . . . . . . . . . . . . . 61.B.i Effi ciency criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.B.ii Effi cient portfolio and risk averse investors . . . . . . . . . . . . . . . . . . . . . . . . . . . 81.B.iii Effi cient set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91.B.iv Two funds separation (Black) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 0
1.C Efficient portfolio with a riskfree asset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111.D HARA preferences and CassStiglitz 2 fund separation . . . . . . . . . . . . . . 14
1.D.i HARA (Hyperbolic Absolute Risk Aversion) . . . . . . . . . . . . . . . . . . . . . . . . 141.D.ii Cass and Stiglitz separation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 5
2 Capital Market Equilibrium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.A CAPM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.A.i The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 7
2.A.ii Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 92.A.iii CAPM as a Pricing and Equilibrium Model . . . . . . . . . . . . . . . . . . . . . . . . . 192.A.iv Testing the CAPM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1
2.B Factor Models and APT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212.B.i K factor models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 12.B.ii APT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 22.B.iii Arbitrage and Equilibrium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 42.B.iv References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 5
PART II Multiperiod Capital Market Theory : theProbabilistic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3 Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273.A Probability Space and Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 .B Asse t Pr ices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.B.i De nitions and Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 83.C Portfolio Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.C.i Notation: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 93.C.ii Discrete Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 93.C.iii Continuous Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 0
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4 AoA, Attainability and Completeness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324.A De n i t i ons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324.B Propositions on AoA and Completeness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.B.i Correspondance between Q and : Main Results . . . . . . . . . . . . . . . . . . . 354.B.ii Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 8
5 Alternative Speci cations of Asset Prices . . . . . . . . . . . . . . . . . . . . . . . . . . 395.A Ito Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395.B Diff us ions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405.C Diff usion state variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5.D Theory in the ItoDi ff usion Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415.D.i Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 15.D.ii Martingales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2
5.D.iii Redundancy and Completeness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 25.D.iv Criteria for Recognizing a Complete Market . . . . . . . . . . . . . . . . . . . . . . . . 44
PART III State Variables Models: the PDE Approach . . . . . . . . . . . . . . . . 45
6 Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
7 Discounting Under Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
7.A Itos lemma and the Dynkin Operator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487.B The FeynmanKac Theorem .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
8 The PDE Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508.A Continuous Time APT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
8.A.i Alternative decompositions of a return . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508.A.ii The APT Model (continuous time version) . . . . . . . . . . . . . . . . . . . . . . . . . . 51
8.B One Factor Interest Rate Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 538.C Discounting Under Uncertainty. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
9 Links Between Probabilistic and PDE Approaches . . . . . . . . . . . . . . . 55
9.A Probability Changes and the RadonNikodym Derivative . . . . . . . . . . . 559.B Girsanov Theorem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569.C Risk Adjusted Drifts: Application of Girsanov Theorem . . . . . . . . . . . . 56
PART IV The Numeraire Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
10 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
11 Numeraire and Probability Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6111.AFramework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
11.A.i Assets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1
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11.A.ii Numeraires . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 111.B Correspondence Between Numeraires and Martingale Probabilities . 62
11.B.i Numeraire Martingale Probabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6211.B.ii Probability Numeraire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3
11.CSummary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
12 The Numeraire (Growth Optimal) Portfolio . . . . . . . . . . . . . . . . . . . . . . . 6512.ADe nition and Characterization ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
12.A.i De nition of the Numeraire (h , H ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 512.A.ii Characterization and Composition of (h , H ) . . . . . . . . . . . . . . . . . . . . . . . . 6512.A.iii The Numeraire Portfolio and RadonNikodym Derivatives . . . . . . . . . . . . 69
12.B First Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6912.B.i CAPM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 012.B.ii Valuation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 0
PART V Continuous Time Portfolio Optimization . . . . . . . . . . . . . . . . . . . . 72
13 Dynamic Consumption and Portfolio Choices (The MertonModel) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7313.AFramework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
13.A.i The Capital Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 313.A.ii The Investors (Consumers) Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 4
13.B The Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7413.B.i Sketch of the Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 413.B.ii Optimal portfolios and L + 2 funds separation . . . . . . . . . . . . . . . . . . . . . . 7713.B.iii Intertemporal CAPM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 8
14 THE EQUIVALENT STATIC PROBLEM (CoxHuang,Karatzas approach) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8014.ATransforming the dynamic into a static problem . . . . . . . . . . . . . . . . . . . . 80
14.A.i The pure portfolio problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 014.A.ii The consumptionportfolio problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2
14.BThe solution in the case of complete markets. . . . . . . . . . . . . . . . . . . . . . . . 8314.B.i Solution of the pure portfolio problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8314.B.ii Examples of speci c utility functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 514.B.iii Solution of the consumptionportfolio problem . . . . . . . . . . . . . . . . . . . . . . 8614.B.iv General method for obtaining the optimal strategy x . . . . . . . . . . . . . . . 87
14.CEquilibrium: the consumption based CAPM . . . . . . . . . . . . . . . . . . . . . . . . 88
PART VI STRATEGIC ASSET ALLOCATION . . . . . . . . . . . . . . . . . . . . . . . 90
15 The problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
16 The optimal terminal wealth in the CRRA, meanvariance
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and HARA cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9216.A Optimal wealth and strong 2 fund separation....................... 9216.B The minimum norm return . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
17 Optimal dynamic strategies for HARA utilities in two cases . . . . 9317.A The GBM case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9317.B Vasicek stochastic rates with stock trading . . . . . . . . . . . . . . . . . . . . . . . . . 93
18 Assessing the theoretical grounds of the popular advice . . . . . . . . . 9418.AThe bond/stock allocation puzzle . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9418.B The conventional wisdom. .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
REFERENCES 95
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PART IStandard (One Period)
Portfolio Theory

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Chapter 1Portfolio Choices
1.A Framework and notations
In all the following we consider a single period or time interval (0 1), hence twoinstants t = 0 and t = 1
Consider an asset whose price is S (t) (no dividends or dividends reinvested).The return of this asset between two points in time (t = 0 , 1) is:
R =S (1) S (0)
S (0)
We now consider the case of a portfolio. and distinguish the case where ariskless asset does not exist from the case where a risk free asset is traded.
1.A.i No Riskfree Asset
There are N tradable risky assets noted i = 1 ,...,N :
The price of asset i is S i (t), t = 0 , 1.
The return of asset i is
R i =S i (1) S i (0)
S i (0)
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The number of units of asset i in the portfolio is n i . The portfolio is describedby the vector n (t); n i can be > 0 (long position) or < 0 (short position).
Then the value of the portfolio, denoted by X (t), is
X (t) = n 0 S (t )
with n (0) = n (1) = n (no revision between 0 and 1), the prime denotes atranspose. S (t ) stands for the column vector (S 1(t),...,S N (t))0
The return of the portfolio is:
RX =X (1) X (0)
X (0)
Portfolio X can also be de ned by weights, i.e.
xi (0) = xi =n i S (0)X (0)
(Note that xi (1) 6= x i ). Besides the weights sum up to one:
x 0 1=1
where x= ( x1, x2,...,x N )0 and 1 is the unit vector .
The return of the portfolio is the weighted average of the returns of itscomponents:
RX = x 0R
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Proof
1 + RX =X (1 )X (0)
=n 0S (1 )X (0)
=N
Xi = 1 n i S i (1 )X (0) S i (0)S i (0)=
N
Xi = 1
xi S i (1 )S i (0)
=N
Xi = 1 xi (1 + R i )= 1 +
N
Xi = 1 xi R iQ.E.D.
De ne i = E [R i] and = ( 1, 2,..., N )0
, then:
X = E (RX ) = x 0
Denote the variancecovariance matrix of returns N N = ( ij ), where ij = cov (R i , R j ), then:
var (RX ) = var (x 0R )= x0 x
=N
Xi=1
N
X j =1
xix j ij
1.A.ii With Riskfree Asset
We now have N +1 assets, with asset 0 being the riskfree asset, and the remainingN assets being the risky assets.
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S 0 (1) = S 0 (0) (1 + r ) with r a deterministic interest rate.
Again we can de ne the portfolio in units, with n = ( n0, n 1, n 2,...,n N )0
The portfolio can be similarly de ned in weights:
x i =n iS (0)X (0)
for the N risky assets (i = 1 , 2,...,N ), and
x0 = 1 N
Xi=1
x i
Note that now
x 0 1 6= 1
where x= ( x1, x2,...,x N )0 denotes the weights in the N risky assets.
The return of the portfolio is:
RX = x0r +N
Xi=1 x i R i = r +N
Xi=1 xi (R i r )The term (R i r ) is the excess return of asset i over r . Moreover:
X = E (RX ) = r + x 0
where is the risk premium vector of the E (R i r ) Also denote N N as the variancecovariance matrix of the risky assets, then:
var (RX ) = x 0 x
is always positive semide nite (meaning that x , x0 x 0). In some casesit is positive de nite (
x 6= 0 , x 0 x > 0).
De nition 1 Assets i = 1 , 2,...,N are redundant if there exist N scalars 1 , 2 ,..., N such that PN i = 1 i R i = k, where k is a constant. Then the portfolio is riskfree.Proposition 1The N assets i = 1 , 2,...,N are not redundant iff is positive de nite (i.e. nonsingular or invertible).
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Proof
Assume that the assets are redundant, then there exist N scalars 1 , 2 ,..., N such that
PN i = 1 i R i = k. Consider the portfolio de ned by the weights . The variance of its return = var (k) = 0 = 0 , i.e. is singular and not positive de nite. Conversely if is singular and not positive de nite there exist a non 0 vector such that 0 = 0 ; Then the return of portfolio has zero variance and PN i = 1 i R i = k
Q.E.D.
Remark 1 In the following sections we will assume that the assets are nonredundant (it is always possible to drop redundant assets if any).
1.B E ffi cient portfolio in absence of a riskfree asset
1.B.i E ffi ciency criteria
De nition 2 Portfolio (x, X ) is e ffi cient if y , Y < X
Y < X and Y =
X Y X
Consider any e fficient portfolio ( x, X ) and let variance (RX ) = kx solves the optimization program (P ) :
maxx
E [RX ] s.t. x 0 x = k ; x 01 = 1
The Lagrangian is:
Lx ,2 , = x
0 2x0 x x 01
The rst order condition L x = 0writes: x 1 = 0or equivalently, for i = 1 , . . ,N :
i = + N
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Remark that these rst order conditions are necessary and also su fficient for thesolution being a maximum since the second order conditions hold ( L(x ) is strictlyconcave  positive de nite).
Theorem 1A portfolio (x , X ) is e ffi cient iff there exist two scalars and such that for all i = 1 , 2,...,N :
i = + cov (Rx , R i )
Proof
The necessary and su ffi cient condition for x to be e ffi cient is that it satis es the rst order condition: for all i: i = + P
N j = 1 x
j ij . We then have:
i = + N
Xj = 1 xj cov (R i , R j )= + cov R i,
N
Xj = 1 xj R j= + cov (R i , RX )
Q.E.D.
Remark 2 The second term can be considered as the additional required rate of return (risk premium), proportional to cov (R i , RX ).
Remark 3 If cov (R i , RX ) = 0 , then i = .
Remark 4 Also note:
var (RX ) =N
Xi = 1N
Xj = 1 x i x j ij=
N
Xi = 1 x i cov R i ,N
Xj = 1 xj R j=
N
Xi = 1 x i cov (R i , R X )The covariance term cov (R i , RX ) indicates the contribution of asset i to the total risk of the portfolio. Therefore, additional required rate of return should be proportional to this induced risk which is what is stated in the theorem. Moreover cov (R i , RX ) appears to be the relevant measure of risk for any asset i embedded in the portfolio X.
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1.B.iii E ffi cient set
De nition 3 The E ffi cient Set is the set of all x that obey the rst order condition. Equivalently, it is the set of all x that solve the optimization program (P 0) 0.
Recall that the rst order condition for (P 0) is:
x
1 = 0De ne risk tolerance
b as the inverse of risk aversion, i.e.
b =1
Then x can be solved as:
x = b 1 1
To nd , use the constraint 10x = 1 , i.e.
1 = 10x
= 10
b 1
1Then:
b10 1 b 1
0 1 1 = 1
or:
b10 1 b 1
0 1 1 = bThis solves for :
=10 1
10 11
Then:
x = b 1 1
= b 1 10 1 10 1 1 1
= 11
10 11+
b 1 10 1 10 1 1 1
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We recognize in the rst term the minimum variance portfolio ( k1) and we callk 2 the second term:
k1 = 11
10 11
k2 = 1 10 1 10 1 1 1
Then the solution of (P ) writes:
x = k 1 +
bk 2
Note that k 011 = 1 and x01 = 1 , therefore k 021 = 0 . Any efficient portfolio is thusthe sum of k 1 (the minimum variance portfolio) and k 2 which is a zero weight(zero investment) portfolio. As it could be expected, an investor with a zero risktolerance will hold only k 1; If he has a positive risk tolerance b he will add a risktaking the form bk2 in order to increase the expected return. The e ffi cient set cannow be caracterized as:
ES = nxx = k1 + bk 2 b > 0oSince the expected return x0 is linear inb and the variance is quadratic in
b, in
the (2, R ) space the effi cient portfolios are represented by the e fficient frontier,which is a parabola. Each point on the e fficient frontier corresponds to a given ,the slope of the parabola at this point being equal to 2 (the shadow price in (P )of the constraint on variance).
In the ( , R ) space the efficient frontier is an hyperbola.
1.B.iv Two funds separation (Black)
Theorem 2Consider any two e ffi cient portfolio x and y :
1. Any convex combination of x and y is effi cient, i.e.u[0, 1] , ux+ (1 u) yES 2. Any efficient portfolio is a combination of x and y (not necessarily a convex
combination)
3. The whole parabola (e fficient and ine ffi cient frontier) is generated by (all)combinations of x and y
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Proof
Since xES and yES , for some positive bX and bY , we have:x = k 1 + bX k 2y = k 1 + bY k 2Let z = ux + (1 u)y , then:z = [uk1 + (1
u) k 1] +
hu
bX + (1
u)
bY
ik 2
= k 1 + bZ k 2With bZ > 0, we can conclude that zES. Let zES , then z = k1 + bZ k2 for some bZ > 0. For any xES andyES :ux + (1 u) y = k 1 + hu bX + (1 u) bY ik2By equatingbZ to u
bX + (1
u)
bY we get:
u = bZ bY bX bY Then the combination ux + (1 u) y = zQ.E.D.
1.C E ffi cient portfolio with a riskfree asset
Consider gure 1 where the upper branch of the hyperbola EFR represents, in the( , E ) space, the efficient portfolios in absence of a riskless asset. Assume now thatexists a risk free asset 0 yielding the certain return r. M stands for the tangencypoint of the hyperbola EFR with a straight line drown from r representing asset 0.Point M represents a portfolio composed only of risky assets, called the tangentportfolio.
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Efficient frontier in presence of a riskless asset
E
r
MX
EFR
Figure 1.1.
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Proposition 2
1. Asset 0 is efficient2. Consider any portfolio X . Any combination of 0 and X yielding
R = uR X + (1 u) r , lies on the straight line connecting 0 and X in the ( , E )space3. Any feasible portfolio which representative point is not on r M (such as X )is dominated by portfolios in r M. The straight line r M is the effi cientfrontier and is called the Capital Market Line4. (Tobins Twofund Separation ) Any effi cient portfolio is a combination of any
two efficient portfolios, for instance 0 and M
5. Any efficient portfolio writes:
x = b 1 r 16. The tangent portfolio (m ,M ) is:
m = bM 1 r 1
bM =1
10 1 r 1Proof
1, 2, 3, 4 are standard and easy to prove. Let us proove 5 and 6: xES solves:
max 1r + x0 r 12x0 xThe rst order condition is:
r 1 = x
Then:
x =1
1
r 1= b 1 r 1
The tangent portfolio is an e ffi cient portfolio, therefore, m = bM 1 r1. Also: m 01 = 1 ,then:
bM =1
10 1 r 1Q.E.D.
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Remark 5 Given a risk tolerance
b:
b < bM , the portfolio is long in 0 and m b > bM , the portfolio shorts 0Remark 6 We de ne later the market portfolio as a portfolio containing all the risky assets present in the market (and only risky assets). In absence of riskless asset the market portfolio is e ffi cient iif its representative point belongs to the hyperbola EFR. In presence of a risk free asset the necessary and su ffi cient condition for the market portfolio to be e ffi cient is that it coincides with the tangent portfolio m (which is the only e ffi cient portfolio of EFR, in presence of a risk free asset). Would all investors face the same e ffi cient frontier (it would be the case under
homogeneous expectations and horizon) and would they all follow the meanvariance criteria,they would all hold combinations of 0 and M and the tangent portfolio M would necessarily coincide with the market portfolio.
1.D HARA preferences and CassStiglitz 2 fund separation
A rational agent (in the sense of Von NeumannMorgenstern) should maximizethe expected utility of wealth E [U (W )].
1.D.i HARA (Hyperbolic Absolute Risk Aversion)
A utility function U (W ) belongs to HARA class if it writes:
U (W ) =
1 b +W 1
Some restrictions are imposed on the coe fficients and
b and the domain of
de nition.
The absolute risk tolerance (ART) and absolute risk aversion (ARA) are:
ART =1
ARA
= U 0
U 00
=
b +
W
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Chapter 1 Portfolio Choices
and the relative risk tolerance (RRT) is:
RRT = bW + 1 In particular:
1. b = 0 U (W ) = W 1 1
We obtain CRRA, i.e. constant relative risk aversion.A limit case of CRRA is obtained for = 1 which can be showed to beequivalent to the Log utility
2. = 1U (W ) = W
W 2
2 bi.e. the quadratic utility function.3. Using a quadratic utility function implies a meanvariance criteria; Indeed:
min var (RX ) s.t. E [RX ] = bE (and x01 = 1)
min E [R2X ] s.t. E [RX ] = bE (and x
01 = 1)
min E [X 2 (1)] s.t. E [X (1)] = X (0) h1 + bE imin E [X 2 (1)] E [X (1)]
max E X (1) 1 X 2 (1)4. Three undesirable features of the quadratic utility: Saturation at W = b (for that wealth U (W ) = W
W 2
2 bis maximum; U (W ) decreases
for W >
b!)
ARA increasing with wealth (it is commonly admitted that ARA decreases for most agents). Indi ff erence to skewness (only the two rst moments of W matter), whereas most
investors actually like skewness.
1.D.ii Cass and Stiglitz separation
Cass and Stiglitz showed that all HARA investors sharing the same exponential
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parameter can build their optimal portfolios by mixing the two same funds.When a risk free asset exists it can be chosen as one of the two funds. Since allquadratic (meanvariance) investors exhibit the same (= 1) Tobin and Black2 fund separation are particular cases of Cass and Stiglitz separation. Cass andStiglitz conditions on the utility functions for separation to hold for investorssharing the same exponential parameter are summarized in the following table
Complete Market Incomplete Market @r (under complete markets r ) quadratic or CRRA
2
r class wider than HARA HARA
2 in the particular case of CRRA one fund su ffi ces (for a given the portfolio is the same for all W
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Chapter 2 Capital Market Equilibrium
Chapter 2Capital Market Equilibrium
2.A CAPM
2.A.i The Model
Consider again N risky assets (a risk free asset may exist or not). The marketvalue of asset i is V i , then (by de nition of the market portfolio) its weight in themarket portfolio is:
m i =V i
PN i=1 V iThe return of the market portfolio is:
RM = m0
R
Hypothesis 1 (H ) : The market portfolio M is e ffi cient.
Remark 7 The market portfolio would be e ffi cient if all investors would hold e ffi cient port folios (since a combination of e ffi cient portfolios is e ffi cient).
Theorem 3(General CAPM )
1. If (H ) is true, then there exist and such that, for i = 1 ,...,N :
i = E [R i ]= + cov (RM , R i )
2. Conversely, if there exist and such that, for i = 1 ,...,N : i = + cov (RM , R i), then (H ) is true.
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Proof
The proof comes directly from Theorem 1.
Q.E.D.
Remark 8 can be interpreted as the risk aversion of the average (representative) investor.
Remark 9 CAPM holds for any portfolio ( x , X ).
Indeed, call RX its return and consider the case where no risk free asset exists(x01 = 1) :
E [RX ] =N
Xi=1 xi i=
N
Xi=1 xi ( + cov (RM , R i ))=
N
Xi=1 xi + N
Xi=1 x i cov (RM , R i)= + covR
M ,
N
Xi=1 xiR i!= + cov (RM , R X )
Remark 10 The proof follows the same lines when the portfolio contains a risk free asset with weight x0
Remark 11 and are the same for all assets or portfolios
Remark 12 For the market portfolio:
M = + cov (RM , RM )= + 2M
Therefore:
=M
2M
Then:
i = + cov (RM , R i )
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De ne:
i =cov (RM , R i )
2M
Then we may write the CAPM equation in the alternative form:
E [R i ] = + i (M )Consider any portfolio z with Z = 0 :
Z = 0
cov (RM , RZ ) = 0
cov (m0
R , z0
R ) = z0 m = 0
z m
z[vect ( m )]
vect [v 1, v 2, ..., v N ] is the set of all linear combinations of v 1, v 2, ..., v N , or linearsubspace generated by v 1, v 2, ..., v N . The dimension of [vect ( m )]
is thus N 1and there are an in nity of 0beta portfolios. Now, from the general CAPM, wewould have: = Z ; Thus:
Corollary 1 ( 0beta CAPM) If M is e ffi cient, for any zero beta portfolio or asset Z : E [R i ] =Z + i (M
Z )
Corollary 2 (Standard CAPM ) : If there exists a riskfree asset yielding r (which is a particular zero beta asset)
E [R i ] = r + i (M r )Note that Z = r for any zero beta portfolio or asset.
2.A.ii Geometry
missing
2.A.iii CAPM as a Pricing and Equilibrium Model
For a security delivering eV (1) at time 1(the pdf of eV (1) is given, thusE (eV (1)) and cov(
eV (1) , RM ) are known), what is its price V (0) at time 0?
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Lets assume that there exists a riskfree asset, then:
E heV (1)iV (0) = E [1 + R] = 1 + r + coveV (1)V (0) , R M !with
=M r
2M
Then:
E heV (1)i= (1 + r ) V (0) + coveV (1) , RM
andV (0) =
E heV (1)icoveV (1) , R M 1 + ri.e. V (0) is the present value of its certainty equivalent at time 1 discountedat the riskfree rate.However this asset may be an element of the market portfolio M (unless thisclaim is in zero net supply ..) and therefore the previous pricing formula isnot a closed form general equilibrium relation.
In fact CAPM is an equilibrium condition stemming from the demand side;The equilibrium price can only be otained by specifying the supply side (inthe previous example the supply was a right on an exogeneous cash ow X ).General equilibrium requires a speci cation of the supply of all securitiestraded in the market.
Consider the N risky assets together and we look for their equilibrium prices. We assume rst an inelastic supply. Assume that asset i delivers
eV i (1 ), an exogenous cash
ow, at time 1 , what is its price at time 0?
E heV i (1 )iV i (0) = 1 + r + M r 2M coveV i (1 )V i (0) , RM !For i = 1 , 2,...,N . We have N equations with N unknowns V i (0) ( i = 1 ,...,N ).( 1 + RM = PN i = 1 eV i (1)PN i = 1 V i (0) allows to compute M ,
2M ,coveV i (1)V i (0) , R M as functions of the V i (0))
Consider again the N risky assets and an elastic supply with constant returns to scale,where the joint pdf of the R i is given and independent of the scale V i (0) to be invested in technology i. The CAPM determines the scale V i (0) of investment in technology i
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by the equations:
i = E [R i ] = r + M r2M cov (RM , R i )and
1 + RM = PN i = 1 V i (0) (1 + R i )PN i = 1 V i (0)2.A.iv Testing the CAPM
One remark about this important empirical topic.Testing the CAPM is equivalent to testing (H ). However, how should we de nethe market portfolio and how to measure the market return?Usually the market portfolio is proxied by stock (plus bond) indices. But resultson stock indices do not include all assets in M (non tradable assets, art,..). Hencewe test the e fficiency of the index and not that of M (Rolls Critique).
2.B Factor Models and APT
2.B.i K factor models
Hypothesis 2 There exist K factors, F k , k = 1 , 2,...K with
1. F iF j
2. E [F k ] = 0
3. var (F k ) = 2ksuch that for i = 1 , 2,...,N :
R i = i +K
Xk = 1 ik F k + iwhere E [ i ] = 0 and i j F k . In vector form:
R N 1 = + N K F K 1 + = +K
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with k being the kth row of .
In practice, we should have large N and small K , so that in estimating thevariancecovariance matrix,
cov (R i , R j ) =K
Xk =1 ik jk 2kwe only need to estimate K terms of 2k and run N regressions for estimatingthe
ik.
In CAPM or in the Markowitz model, without the factor decomposition, weneed to estimate N (N 1) / 2 terms.
A Particular case: K = 1 boils down into the market model that writes:
R i = i + iF + i
Then:
RM =N
Xi=1
m i i + F N
Xi=1
m i i +N
Xi=1
m i i
= M + F
Since the innovation terms diversify and M = PN i=1 m i i = 1 :F = RM M and
R i = i + i [RM M ] + i
Note that the R i are linked through [RM M ] (since cov (R i , R j ) = i j 2M ).Also, i [RM M ] is the systematic risk, and i is the unsystematic (diversi able)risk; only systematic risk should be priced (CAPM).
2.B.ii APT
We assume that the returns are generated by a K factors linear process previouslyde ned that writes:
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R = + + F + = +K
Xk =1 k F k + Recall that
kis an N dimensioned column vector with an i th component equal
to ik
De nition 4 A zero investment portfolio, de ned by the amount of wealth, x , invested in each asset, satis es:
x 01 = 0
V (0) = 0V (1 ) = x 0R
The last equation can be veri ed since:
V (1) =N
Xi=1 xi (1 + R i ) =N
Xi=1 xi +N
Xi=1 x iR i = x 0RDe nition 5 An arbitrage portfolio is a zero investment portfolio with x 0R 0 almost surely and E [x0R ] > 0.Absence of arbitrage (AOA) prevails if no arbitrage portfolio can be constructedi.e:x01 = 0 and x0R 0 a.s. implies x0R = 0 a.s. (or equivalently implies E (x0R ) =0)
Theorem 4(APT ) In AoA there exist K + 1 scalars such that:
= 0 1 + 1 1 + ... + K K
or i = 0 + 1 i 1 + ... + K iK
0 is the required rate of return without systematic risk.
k is the market price of risk k.
k ik is the risk premium imposed to security i because it has a risk k of intensity ik .
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Proof
Consider any welldiversi ed zero investment portfolio satisfying:
x 01 = 0 or x1x0 k = 0 or x k for k =
1 ,...,K
hence:
x is any element of hvect1 , 1 , 2 ,..., K iAlso x0 = 0 (since it is well diversi ed); Then:
RX = x 0R
= x 0 +K
Xk = 1 F k x0
k + x0
= x 0
Since x 0 is certain, in AoA x 0 must be zero (if x 0 > 0 then x is an arbitrage portfolioand if x 0 < 0 then x is an arbitrage portfolio). Thus: x0 = 0 or x , which means that is orthogonal to any element x of [vect(1 , 1 , 2 ,..., K )]
, i.e.
vect(1 , 1 , 2 ,..., K )
implying that exist K + 1 scalars such that : = 0 1 + 1 1 + ... + K K
Q.E.D.
In the particular case where there is a riskfree asset, then:
0 = 0 = r
and
i = r + 1 i1 + ... + K iK
2.B.iii Arbitrage and Equilibrium
Equilibrium implies AoA, but the inverse is not true.
AoA conditions do not involve utility functions.
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2.B.iv References
DumasAllaz, 1995 ; DemangeRochet, 1992.
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PART IIMultiperiod CapitalMarket Theory : the
Probabilistic Approach

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Chapter 3 Framework
Chapter 3Framework
3.A Probability Space and Information
We consider the usual probability triplet ( , F , P ), where F is a algebra on representing the observable events at time T .
Information in the period [0, T ] is represented by a ltration {F t }t[0,T ], where F tis the set of observable events at time t (represented by a algebra), and thesequence {F t }t[0,T ] satis es the usual conditions:
F 0 = {null events and a.s. event }(s < t ) (F sF t )
F T = F F s = \t>s F t
In the discrete time setting, all transactions take place at discrete points, i.e.,t = 1 , 2,...,T . In the continuous time setting, transactions take place continuously,i.e., t[0, T ].
We assume a frictionless market, continuously open in the continuous time framework.
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3.B Asset Prices
3.B.i De nitions and Notations
There are N + 1 assets traded in the market, one being the locally riskfree asset, denoted by 0, and the remaining N being the risky assets. The prices of those assets are noted S i (t) ( for i = 0 , 1,...,N ); S(t) = ( S 1(t), . . ,S N (t))0 or(S 0(t), S 1(t), . . ,S N (t))0 (depending on the context) is the N (or N + 1 ) dimen
sional column vector of asset prices. Without loss of generality it will generallybe assumed that S i (0) = 1It is assumed for the time being that there is no dividend, or that a dividend isreinvested in the asset that delivers it.1. In the discrete time case S 0(t) = S 0(t 1)[1 + r (t 1)], with r (t 1) beingthe locally riskfree rate in [t 1, t] , known at time t 1 but unknown before.Remark that S 0(t + 1) = S 0(t)(1+ r (t)) is random at t 1 since r (t) is unknown.2. In the continuous time context:
r (t) is stochastic but F t adapted.
For a riskfree asset:
dS 0 = S 0rdt
or
S 0 (t) = eR t0 r (u )duwith S 0 (0) = 1 .
For a risky asset we will usually assume that prices follow Ito processes:dS i = S i idt + S i i 0dw
with risk induced by w , the vector of standard Brownian Motions.
Technical conditions (e.g., the integrability conditions) apply.If S i follows Ito process, we preclude jumps. If jumps are involved, however, thena rather general assumtion is that S i follows a semimartingale process. A slightlymore speci c assumption is that asset prices follow processes that yield a.s. RightContinuous and Left Limited (RCLL) paths. When considering the possibility of
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jumps we will assume RCLL processes for the asset prices to avoid the so sticationof semi martingales 3.
It is worthwhile to note that Ito processes RCLL Semi martingales . Most of the results of the next chapter (On AOA and completeness) hold in
the semimartingale case.
3.C Portfolio Strategies
3.C.i Notation:
n (N +1) 1 the vector of the N+1 numbers of assets ; xN 1 the vector of N weights on risky assets
S (N +1) 1 the vector of the N+1 asset prices
X (t) = n 0(t)S(t) the value of the portfolio at t
(n ,X ) or (x ,X ) a strategy
3.C.ii Discrete Time
[t 1, t[ is period t 1;at time t S (t) is set and, just after, n(t) is choosen
During period t 1, the value of the portfolio will evolve:X (t) X (t 1) = n 0(t)S(t)n 0(t 1)S(t 1)= n 0(t 1) [S(t) S(t1)] + S0(t) [n (t) n (t 1)]
The rst term in the right hand side of the equation, n 0(t 1) [S(t) S(t1)], isthe gain during the period [t 1, t[ , and is represented as g(t 1, t).3 Consider the integral: R (u ) dS . In a regular integral of this form dS is in nitesmal, while in a jump process it can assume some nite value somewhere.
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The second term can be deemed as the net cash in ow added to the portfolio attime t. Indeed it can be decomposed into two terms: S0(t)n (t 1), the value of assets sold at time t, and S0(t)n (t), the algebric value of assets purchased (maybe < 0 if sales> purchases).
The cumulative gain in [0, t], de ned for t = 1 ,...,T , can be represented as:
G(t) =t
Xu =1 g(u 1, 1)De nition 6 (Self nancing Portfolio ) When at each time t the net in ow is 0, the strategy is said to be self nancing, i.e., if (n ,X ) is self nancing, then:
X (t) X (t 1 ) = g(t 1 , t ) = n 0(t 1 ) [S (t) S(t1)]and
X (t) = X (0) + G(t)
Let S i (t) be the value of asset i at time t, and S 0(t) be the numeraire, thenthe discounted value of i is:
S di =S i (t)S 0(t)
Self nancing is independent of the numeraire used; In particular (n ,X )self nancing implies:
X d(t) X d (t 1) = n 0(t 1)Sd (t) S d (t 1)3.C.iii Continuous Time
The gain process in [t, t + dt) is de ned as:
dG(t) = n 0(t)dS(t)
and
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The change of the portfolio value is found to be:
dX = d (n 0(t)dS(t))= n 0(t)dS(t)+ S0(t)dn 0(t)+ dn 0(t)dS(t)= n 0(t)dS(t)+ dn 0(t) [S(t)+ dS(t)]
with the rst term in the right hand side of the equation being the period gaindG(t) and the second term the net in ow at t + dt.
Again, in a self nancing strategy: dX (t) = dG(t), and X (t) = X (0) + G(t);As in the discrete time case, the self nancing property as well as theexpression of the gain do not depend on the choosen numeraire .
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Chapter 4 AoA, Attainability and Completeness
Chapter 4AoA, Attainability andCompleteness
4.A De nitions
De nition 7 strategy (n ,X ) is admissible if:
1. n (t) is F t adapted and satis es some technical conditions 4.
2. X (t)L1,2.
3. (This is an additional condition imposed sometimes) X (t) is bounded frombelow to avoid doubling Strategies 5 .
De nition 8 A is the set of admissible strategies
De nition 9 A0 = { Self nancing and admissible strategies }
We now work with A0, i.e., (n ,X )A0, dX = n 0dS .
4 Technical conditions on n ( t ) :, when asset prices follow RCLL processes
(a) G(t) = R t0 n 0(u)dS(u) must be de ned for S(t)RCLL(b) (Integrability )i.
R t
0 kn0(u)k2 du< a.s.
ii. R t
0 n0(u) du< a.s.(c) (predictability of n (t))
n (t)LCRL so that if there is a jump in S(t), rebalancing must takeplace in t+ but never in t , the latter being equivalent to insider trading,i.e., a rebalancing, or jump, in n (t) takes the advantage of a jump in S(t)that has just occured. This condition is not necessary when S(t) iscontinuous.
5 In a Doubling Strategy the gambler bets 2 when losing 1 and bets 4 when losing 2...
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It is also possible to de ne a strategy by a vector of weights x N 1. The weight of the riskfree asset in the portfolio is then 1 x01 .De nition 10 (a ,A) is an arbitrage if:
1. (a ,A)A0.
2. A(0) = n 0(0)S(0) =0 , (i.e., zero initial investment).
3. A(T ) 0 a.s. (i.e., nonnegative cash ow at the end).
4. E [A(T )F 0] > 0
There is an arbitrage opportunity each time that a strategy (x, X ) in A0 dominatesanother strategy (y, Y ) in A0 (i.e. X (T ) Y (T ) a.s. and E [X (T )] E [Y (T )] forthe same initial investment X (0) = Y (0); or X (T ) = Y (T ) a.s. with X (0) < Y (0)).Arbitrage is built by being long in (x, X ) and short in (y, Y ).
Example 1 X (T ) S 0 (T ) = eR T 0 r ( u )du a.s. ; E (X (T ) S 0 (T )) > 0 and X (0) = 1Example 2 X (T ) = K , a constant, while X (0) < KB T (0) where BT (0) denotes the value
at time 0 of a zerocoupon bond yielding 1 at time T.
The previous considerations imply:
Proposition 3In AoA, all self nancing and admissible portfolios yielding a.s. the same terminal value must require the same initial investment, i.e. (x ,X ) A
0 and y ,Y A0with X (T ) = Y (T ) a.s. , then in AoA: X (0) = Y (0).De
nition 11 eC T is a contingent claim if
1. eC T is F T measurable.2. eC T L1,2 ( nite mean and variance).
3. (goes with hypothesis on admissible strategies) eC T is bounded from below.De nition 12 C , {the set of contingent claims }33

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Example 3 The terminal values of N + 1 primitive assets are contingent claims.
Example 4 AF T , the indicator function 1 A is a contingent claim.
De nition 13 eC T C is attainable if (c ,C )A0 with C (T ) = eC T a.s. . We say eC T is attained by (c,C ) or (c,C ) yields eC T .De nition 14 Ca = {attainable contingent claims }
De nition 15 Cn = {nonattainable contingent claims }
De nition 16 The market is (dynamically) complete when all contingent claims are attainable, i.e., Ca = C or Cn = .
Remark 13 Market completeness is unrealistic in discrete time, but less unrealistic in continuous time. In continuous time the possibility of rebalancing at each point of time allows a much larger spanning. When completeness is obtained through continuous rebalancing, the market is said dynamically complete.
De nition 17 A pricing formula maps C onto R. To be viable, must satisfy:
1. is linear, i.e., 1, 2, eC T C, and eC 0T
C:
1eC T + 2eC 0T = 1 eC T + 2eC
0T
2. eC T C(a)eC T 0 a.s.
eC T 0
(b) eC T = 0 a.s.
eC T
= 03. (Viability or Compatibility Condition) (x ,X ) attaining eC T (i.e., X (T ) = eC T a.s. ):eC T = X (0)
De nition 18 = {  viable }
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De nition 19 Two probability measures P and Q are equivalent if they have the same null sets (the impossible as well as the certain events are the same for P and Q)
De nition 20 An adapted stochastic process is a martingale if at each point of time the (conditional) expectation of a future value is the current value i.e:
Z (t) is a martingale if E [Z (t)/F s ] = Z (s) for any s and t such that 0 s t T De nition 21 Q = nQQP and (x ,X )A0, E Q hX (T )S 0 (T ) F 0i= X (0 )S 0 (0) o. Equivalently,Q is a set of P equivalent probability measures Q under which the asset 0 discounted asset prices X d (T ) = X ( T )S 0 ( T ) are martingales.
It should be noted that X (0) 6= E P hX (T )S 0 (T ) F 0ibecause investors are riskaverseand expect a return di ff erent than the riskfree rate r (usually higher since, ingeneral, holding a risky asset increases the risk of their portfolio). However, this
does not mean that there is no such a probability measure as Q that yields Qmartingale discounted prices.
In the following we will consider the problems:
Are Q and empty?
What is the relation between Q and ?
4.B Propositions on AoA and Completeness
Recall in the following that S 0 (0) = 1
4.B.i Correspondance between Q and : Main Results
Theorem 5Assume Q and are not empty. There exists a onetoone relation between Q and .
Q Q , de ned by:
eC T C : Q eC T = E Q "eC T S 0 (T ) F 0#
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Q , de ned by:
AF T : Q (A) = E Q [1A ] = (1A S 0 (T ))
Proof
Let us begin by showing that Q is a viable pricing system. Indeed:
1. Q is linear (because the expectation operator E is linear), i.e.,
eX T
Ca
and
eY T
Ca :
Q eX T + eY T = E Q " eX T + eY T S 0 (T ) #
= E Q " eX T S 0 (T )#+ E Q "eY T S 0 (T )#= Q eX T + Q eY T
2.
eC T 0 a.s. , Q
eC T 0;Indeed:
eC T > 0 Q eC T = E Q "eC T S 0 (T )#> 0
and
eC T = 0 a.s. Q eC T = E Q "eC T S 0 (T )#= 0
3. (Compatibility Condition) (x ,X ) attaining
eC T , i.e., X (T ) =
eC T a.s. .,
Q
eC T
= X (0); Indeed:
Q eC T = E Q "eC T S 0 (T )#
= E Q X (T )S 0 (T )= E Q X d(T )F 0= X (0)
since Q yields martingale discounted prices
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4. It has been shown that Q is a viable pricing formula that maps Q into .Moreover, this mapping is injective, i.e., Q
0 6= Q and Q0, QQ, Q 0 6= Q .
Indeed:
Q0 6= Q
AF T s.t. Q0(A) 6= Q (A)E
Q 0 [1A ] 6= E Q [1A ]
Consider a contingent claim 1A S 0 (T ):
Q (1A S 0 (T )) = E Q
1A S 0 (T )
S 0 (T )
= E Q [1A ]
and
Q 0 (1A S 0 (T )) = E Q0 1A S 0 (T )S 0 (T )
= E Q0
[1A ]
Therefore we obtain di ff erent prices for this particular contingent claim,hence, Q 0 6= Q .
5. The proof ends by checking that when is a viable price systemQ (A) = (1A S 0 (T )) de nes a probability measure which has the samenull sets than P .
Q.E.D.
Corollary 3 Q = =
Corollary 4 Q is a singleton
is a singleton
Theorem 61. In AoA, a viable pricing formula on Ca exists and is unique.
2. Market is complete and AOA Q is a singleton
is a singleton
3. AoAQ 6=
6= Proof :
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1. Assume AoA and consider any
eC T
Ca attained by (x ,X ). Because thecompatibility condition it is only possible to de ne the price of eC T by eC T =X (0). If another strategy y ,Y attains fC T , X (0) = Y (0) because AOA, Hencethere is only one viable pricing of an attainable claim under AOA.2. Under AOA, if the market is complete all claims are attainable, hence there isone and only one viable price for any contingent claim in C .3. Under AOA and incomplete markets there are an in nite number of viableprices for a nonattainable contingent claim ( 6= but is not a singleton). WhenAOA does not prevail no pricing system meets the compatibility condition, hence is empty.
4.B.ii Extensions
4.B.ii.a Extension I.
Consider eC T Ca attained by (x ,X ) and consider Q
Q:
At time 0, eC T = E Q heC T S 0 ( T ) F 0i S 0 (0) = E Q heC T S 0 ( T ) F 0i= X (0)
At time s[0, T ]:
s eC T = E Q X (T )S 0 (T ) F s S 0 (s)
=X (s)S 0 (s)
S 0 (s)
= X (s)
4.B.ii.b Extension II.
The portfolio is not self
nancing:
Assume an adapted and integrable dividend payment (t) in [t, t + dt], then:
X (0) = E Q0 (Z T 0 h (t) e R t0 r ( u ) du idt + X (T ) e R T 0 r ( u ) du ) Assume a cumulative dividend stream dD (t) in [t, t + dt], then:
X (0) = E Q0 (Z T 0 hdD (t) e R t0 r (u )du idt + X (T ) e R T 0 r ( u ) du )38

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Chapter 5 Alternative Speci cations of Asset Prices
Chapter 5Alternative Speci cations of AssetPrices
5.A Ito Process
There are N + 1 assets in the market:
r (t) being the adapted, locally riskfree rate, asset 0 is the correspondingriskfree asset with:
dr = r (t) dt + 0r (t) dw
dS 0 (t) = S 0 (t) r (t) dtS 0 (t) = eR t0 r (u )duAt t : dr (t) is not known, but dS 0 is known.
The N risky assets follow the process:
dS = (t) dt + (t) dw
S (t) = S (0) + Z t0 (u) du + Z t0 (u) dwor for the i th asset
dS i= i (t) dt + 0i (t) dw
where w M 1 is the vector of standard Brownian Motions and N 1 (t) and
N M (t) are the two adapted processes 0
i is the ith
row of
. The coeffi
cientsof all these Ito processes are stochastic processes that satisfy integrabilityconditions.
In terms of returns:
dR i =dS iS i
= i () dt + 0i () dw
or in vector form:
dR = () dt+ () dw
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where 0i is the ith row of , the diff usion matrix.Equivalently:
S i (t) = S i (0) eR t0 [ i (.) 12 k i (.)k2 ]du +R t0 0i ( .)dwThe integrability conditions on the coe fficients are:
(IC ) R t0 i(.) du and R t0 k i (.)k2 du de ned a.s fori = 1 ,...,N They will be refered as the integrability conditions (IC ) in the followingchapters
Ito process yields continuous sample paths, but they are not necessarily
Markovian.
5.B Di ff usions
S(t) follows a diff usion process if:
dS = (t, S (t) , r (t)) dt + (t, S (t) , r (t)) dw
or
dS i= i (t, S (t) , r (t)) dt + 0i (t, S i (t) , r (t)) dw
or
dR = (t, S (t) , r (t)) dt+ (t, S (t) , r (t)) dw
or, equivalently
S i (t) = S i (0) eR t0 [ i 12 k i k2 ]du +R t0 0i dwThe process for the riskfree rate is:
dr = r (t,r, S) dt + 0r (t,r, S) dw
with the coe fficients ( () , () , () , ..), being a deterministic function of stochastic variables r, S and the deterministic t.
The di ff usion process is an Ito process, hence it exhibits continuous samplepaths. Moreover it is Markovian since the next increment depends on t andS(t) , r (t) only.
Technical conditions to be satis ed bythe coefficients of a diff usion process arethe Lipschitz condition and the linear growth condition.
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Chapter 5 Alternative Speci cations of Asset Prices
5.C Di ff usion state variables
The state of the economy is de ned by L state variables Y obeying the di ff usionSDE: dY = Y (t, Y (t)) dt + Y (t, Y (t)) dw(the coefficients meet the integrability conditions). The dynamics of all the nancial variables depend on (t, Y (t)) ,i.e:dR = (t, Y (t)) dt+ (t, Y (t)) dw ; dr = r (t, Y ) dt + 0r (t, Y ) dwRemark that The processes are Markovian The simple di ff usion case is a particular case of the state variable di ff usion case(where S and r are the state variables); the state variable di ff usion case is aparticular case of the Ito case.
5.D Theory in the ItoDi ff usion Case
All the results on AOA, martingale measures, viable prices, completeness,.., presented in the case of RCLL asset prices and LCRL strategies hold of course whenthey follow Ito or di ff usion processes (which are continuous). We present in thefollowing some speci c results valid in these last cases.
5.D.i Framework
Assume the usual probability triplet [ , F , P ] .
Let w M 1 denote the sources of uncertainties. The observable eventsat t are the events w (t0) a for all t0 t and all the real vectors a:roughly, information at t is represented by the path of w between 0 andt. {F t , t[0, T ]} F w is then called the ltration generated by w .
dR = (t)dt+ (t)dw and dS i = S i i dt + S i 0idw ; dr = r (t) dt + 0r (t) dw; the coefficients ( (t), (t), ..) are F w adapted and satisfy the integrabilityconditions.
Let () denote the instantaneous variancecovariance matrix, then:
=dR dR 0
dt
= dw dw 0 0
dt= 0
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Chapter 5 Alternative Speci cations of Asset Prices
Let C be the set of contingent claims de ned as L2, as previously. If X, Y
C,then E [XY ] is a scalar product and C is an Hilbert space
A strategy can be de ned by weights on riskyassets xN 1. The strategy willbe denoted by (x ,X ) and the weight of the riskfree asset in the portfoliowould be x0 = 1 x 01
(x ,X ) is self nancing iff
dX X
= x0rdt + x 0dR
= rdt + x0(dR rdt 1)i.e., the increment in value comes only from returns. Equivalently:
dX X
= X dt + x 0 dw
with
X = rdt + x 0 r15.D.ii Martingales
Theorem 7(martingale representation theorem, stated without proof). Consider any F w adapted Martingale Z (t) : there exists an integrable process M 1 () such that, for t(0T ) :Z (t) = Z 0 + R t0 0(.)dw dZ = 0(t)dw (t)In particular, for any (x , X ) in A0, under any Q
Q: dX d
X d = 0dw (X d = X/S 0)
and dX X = r (t)dt+ 0dw
We will see later that the probability change (from P to Q for instance) changes
only the drift of the process but not the diff
usion term (this follows from Girsanovtheorem stated further on). Since this di ff usion part would be x 0 dw for aportfolio (x , X ),we can write under Q: dX
d
X d = x0 dw and dX X = r (t)dt + x
0 dw
5.D.iii Redundancy and Completeness
De nition 22 The N + 1 assets are redundant at time t if there exists a non zero N dimensional vector (t) = ( 1 , 2 ,..., N )0 such that 0dR = (t) dt a.s. , i.e., a linear combination of risky assets gives a locally riskfree result.
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Chapter 5 Alternative Speci cations of Asset Prices
Without losing generality, assume 01=1
In AoA, (t) = r (t)
Proposition 4The assets are not redundant iff Rank ( ) = N, or, equivalently, iff the N rows of are linearly independent, or iff is a positive de nite (invertible) matrix for all t a.s. .
Proof
Assume that the assets are redundant, i.e., 0dR = PN i = 1 i dR i = (t) dt . Then de ning i = i N gives:dRN = dt +
N 1
Xi = 1 i dR iApply the processes followed by R i :
N dt + 0N dw =
e dt +
N 1
Xi = 1
i 0i dw
For all dw; This implies:
0N =N 1
Xi = 1 i 0ii.e., the N th row of is a linear combination of the other rows. Therefore, Rank ( ) < N .
Q.E.D.
Remark 14 A result follows directly: a necessary condition for the assets to be nonredundant is M N.Theorem 8Assume AOA, that M = N , that the coe ffi cients are adapted w.r.t. the ltration F wgenerated by w and that the N + 1 assets are nonredundant (hence Rank ( ) = N ta.s. .), then the market is complete w.r.t. . the ltration F w .
Proof
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PART IIIState Variables Models:
the PDE Approach

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Chapter 6 Framework
Chapter 6Framework
The state of the economy depends on a vector Y of state variables
Let w M 1 denote the M Brownian Motions vector and Y L 1 denote theLstate variables vector with
dY = Y (t, Y (t))dt + L M t ,Y (t)dwY (t) represent the random variable, Y t will denote a particular realization att
We consider N + 1 primitive securities (one riskfree, N risky). The returnsof the N risky assets follow the di ff usion process:
dR N 1= (t , Y (t))dt + N M (t, Y (t))dw
or, for a single asset:
dR i =dS iS i
= i (t, Y (t)) dt + 0i (t, Y (t)) dw
( 0i is the ith row of )
The riskfree rate follows the di ff usion process:
dr (t) = 0 (t, Y (t)) dt + 0r (t, Y (t)) dw
The price of the locally riskless asset follows:
dS 0 = r (t) S 0 (t) dt
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Chapter 7 Discounting Under Uncertainty
Chapter 7Discounting Under Uncertainty
7.A Itos lemma and the Dynkin Operator
Recall that we consider the variables Y satisfying;
dY = Y (t, Y )dt + L M (t , Y ) dw
Consider v (t, Y ) : [0, T ] RL R, with vC 1 w.r.t. t and vC 1,2 w.r.t. Y .Itos lemma writes in alternative forms:dv =
v t
dt + v Y0dY + 12dY 0 2v Y Y 0dYThis gives:
dv = " v t + LXi=1 v Y i Y i + 12L
Xi=1L
X j =1 2v
Y i Y jV ij#dt + LXi=1 v Y i
M
X j =1 ij dw jwith V ij being the common term of V , 0.
De ne the Dynkin operator as:
D tY v =E t [dv]
dt
= v t
+L
Xi=1 v Y i Y i + 12L
Xi=1L
X j =1 2v
Y i Y jV ij
then the dynamics of v can be simpli ed in notations as:
dv = ( DtY v)dt + v Y0
dw
7.B The FeynmanKac Theorem
Consider Y and v (t, Y ) as previously de ned. For given functions of b (t, Y (t)) , (t, Y (t)), and l (Y ), we try to nd the solution to the following problem (PDE48

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Chapter 7 Discounting Under Uncertainty
with its limit condition):
P DE { DtY v + = bvv (T, Y ) = l (Y )
FeynmanKac theorem: The solution of the previous P DE can be writtenas an expectation:
v (t, Y t ) = E P Z T t (u) e R ut b (x )dx du + l (Y T ) e R Tt b (x )dx Y (t) = Y tThe nancial interpretation of this is:
v is the price of a nancial asset giving a dividend stream of and a terminalvalue of l (Y )
b is the required rate of return D tY v+ v is the expected instantaneous return with ( DtY v)dt being the capitalgain and (t)dt the dividend during the period [t, t + dt].
The PDE states that the expected return is equal to the required
b; Its solution
is the conditional expected value of the discounted stream of dividends + theterminal value, the discount rate being b. This is also CIR(1985), lemma III .FeynmanKac theorem provides a link between the PDE approach and the martingale approach. However since we do not know the required expected return bthe PDE or its solution interpreted as a discounting at rate b does not give thevalue v. But in the following we are going to provide an APT condition on b.
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Chapter 8 The PDE Approach
Chapter 8The PDE Approach
8.A Continuous Time APT
8.A.i Alternative decompositions of a return
Consider an asset yielding a dividend stream of and a terminal value of l (Y ).We have derived that:
dv = ( DtY v)dt + v Y0 dwDivide both sides by v gives:
dv
v=
1
vDtY v
dt +
1
v v
Y0
dw
= v dt + 0vdw
Here v = 1v DtY v can be considered as the expected rate of return and 0v =
1v , 2v , ..., M v the volatility vector or sensitivity w.r.t. . w .Also, de ne
= v
1
v v
Y0
Y
then
dvv
= vdt +1v v Y0 dw
= vdt +1v v Y0dY Y dt
= dt +1v v Y0dY
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Chapter 8 The PDE Approach
More explicitly we get two alternative decompositions of the return:
dvv
= vdt +M
Xi=1 ivdwi= dt +
1v
L
Xi=1 v Y i dY i iv is the sensitivity of the return of asset v w.r.t. w i and 1v
v Y i the sensitivity
w.r.t. Y i .
8.A.ii The APT Model (continuous time version)
In the following () denotes (t, Y (t)) .The following proposition is the continuous time version of APT and can be justi ed as the discrete time version.
Proposition 5(APT)
1. There exist M scalars: 1 () , 2 () , ..., M () such that, for any asset (value vreturn stream = , required expected instantaneous return = ):
v
() + () = r () +M
Xi=1 i () iv () i () is the market price of the risk wi and is the same for all assets. Theequation above can be deemed as a decomposition of the expected rate of return into the riskless rate and M risk premiums: i () is the market price of risk (MPR) wi ; The MPR vector is the same for all assets.
2. There exist L scalars: 1 () , 2 () ,..., L () s.t. , for any asset: v
() + () = r +L
X j =1 j () 1v () v Y j ()This is an alternative decomposition of the expected rate of return with L riskpremia (relative to risks Y ). j is the market price of risk (MPR) Y j , and isalso the same for all assets.
We will drop () in the following for simplicity
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Chapter 8 The PDE Approach
A direct result then follows:
L 1= L M M 1
In the particular case that L = M and then is invertible, can be solved as:
= 1
Furthermore, if we apply APT to the ith primitive asset:
dR i = i dt + 0i dw
i = r + 0i
where 0i is the ith row of .
In vector form:
= r 1+
This equation may be used in two ways: To obtain the required returns for a given MPR .Then, the returns of the primitive risky assts follow:dR = [r (.)1 + (.) (.)]dt + (.))dw
 To obtain (or estimate) the MPR assuming that the risk premiums r 1are known (or estimated). This is only possible when is invertible ( M = N and non redundant assets, implying market completeness), in which case:
= 1 r 1Under incomplete markets an in nite number of MPR vectors are compatiblewith the risk premia on the primitive securities.
It is important to note that:
DtY v + v
= r + 1v v Y
0
This PDE means that the expected rate of return equals the required rate of return. It must be followed by any asset in a world described by Y . The onlydiff erence between assets is the boundary condition v (T, Y ) = l (Y ) speci cto each asset.
Example 5 In the BlackScholes framework Y = S, L = M = N = 1 , for a call: l(Y ) =(Y K )+ , = ( r )/
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Chapter 8 The PDE Approach
Example 6 A one factor interest rate model with a stochastic riskfree rate r , which is also the state variable. Only bonds are considered and one bond is su ffi cient (the others are redundant), therefore, L = M = N = 1 . We consider such models in the following section.
8.B One Factor Interest Rate Models
L = M = 1 , and now Y 1 = r
dS 0 = S 0 r (t) dt Let BT (t, r (t)) be the price of a zero coupon bond at t that delivers 1 at T . The
duration of the bond is then T t Several BT may be traded (but they are redundant). dr = a [br ]dt + r (t, r )dw. In Vasicek model r (t, r ) = constant, and in CIR model r = r
Write the expected rate of return by applying APT:
Dtr BT BT
= r +1
BT BT r
This gives:
BT t
+12
2r 2BT r 2
+ a (br ) BT r
= rB T + BT r
with the boundary condition that BT (T ) = 1 T .
The PDE can be solved in both Vasicek and CIR settings.
8.C Discounting Under Uncertainty
Consider the PDE:
D tY v + = rv + v Y0 ; LC : v(T, Y ) == l(Y )where LC stands for Limit Conditions. We have:
v t
+ v Y0 Y + 12dY 0 2v Y Y 0dY + = rv + v Y053

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Chapter 8 The PDE Approach
or, equivalently:
v t
+ v Y0h Y i+ 12dY 0 2v Y Y 0dY = rv Note that left hand side of this equation can be interpreted as the Dynkin operatorcomputed w.r.t. a drift ( Y ) diff erent from Y . Hence now de ne bY (t) = Y (t)and d bY (t) = h Y idt + dw , we now have an equivalent but simpli edwriting:
D t
bY v + = rv
By FeynmanKac, the solution is:
vt, bY t = E P Z T t e R ut rdx du + l(Y (T )) e R Tt rdx  bY (t) = Y (t) = Y tand bY (t) = h Y idt + dw
Note that we now discount with r instead of ! (CIR 1985, lemma IV) Wecan safely state that the value of any asset is the expected discounted value of future cash ows with r as the discount factor provided that the drift of Y isadjusted by the MPR of the risks Y , which is .
Alternatively we could express the valuation formulae in function of the MPR of the risks w (substitute in the formulae for )
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Chapter 9 Links Between Probabilistic and PDE Approaches
Chapter 9Links Between Probabilistic andPDE ApproachesAs usual, we start from the probability space ( , F , P ). We say that a probabilitymeasure is equivalent to another iff they have the same measure zero sets, i.e.,QP iff Q (A) = 0P (A) = 0 (AF ).
9.A Probability Changes and the RadonNikodym Derivative
Proposition 61. If QP , then there exists a random variable which is F measurable withE P [ ] = 1 and > 0 a.s. such that AF :
Q (A) = Z A ($ ) dP ($ )= E P [1A ]
Then = dQdP and is called the RadonNikodym derivative of Q w.r.t. P .
2. Any F T measurable, with E P [ ] = 1 and > 0 a.s. is a valid RadonNikodym derivative, meaning that a new probability measure Q can be de nedby dQdP = or Q (A) = E
P [1A ]AF (and QP ).
Proof
We proove only part 2. We check rst that Q is a probability measure;Indeed:
Q ( ) = E P [1 ] = E P [ ] = 1
Moreover, for A B = ,Q (AB ) = E
P [1AB ]= E P [(1A + 1 B ) ]= E P [1A ] + E P [1B ]= Q (A) + Q (B )
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Chapter 9 Links Between Probabilistic and PDE Approaches
We check that Q
P ; Indeed, since > 0 a.s. .,
Q (A) = E P [1A ] = 0
P (A) = E P [1A ] = 0
Q.E.D.
The intuition behind the changing of probability is that, by considering the
probability of an event as a mass, the probability, or the mass, is changed bymultiplying a positive ($ ) (dQ ($ ) = ($ ) dP ($ )) .
Also, X with E P [X ] < , E Q [X ] = E P [X ].
9.B Girsanov Theorem
Consider the mdimensional Brownian Motion w under the probability measureP , the ltration F t (t(0, T )) generated by w, and the mdimensional adaptedprocess () that satis es integrability conditions ( R t0 k (s)k2 ds de ned,.. ).Theorem 9(Girsanov Theorem) a) De ne (t) = e
12 R t0 k ( s ) k2 ds R t0 0 ( s )d w , then (T ) is a valid RadonNikodym derivative:
(meaning that: (T ) is F T measurable; E P [ ] = 1 ; 0 a.s. ); Moreover (t) is a Pmartingale.b) De ne Q P by
dQdP = (T ), then ew (t) , w (t) + R
t0 (s) ds is a standard Q
Brownian Motion.
9.C Risk Adjusted Drifts: Application of Girsanov Theorem
Consider:
dR = dt + N M dw
We had:
= r 1 +
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Chapter 9 Links Between Probabilistic and PDE Approaches
Therefore:
dR = [r 1 + ]dt + dw
We now look for a probability measure Q under which the dynamics of R have adrift of r 1 (then the asset 0 denominated values S i (t )S 0 (t ) would be Qmartingales).We know that Q exists and is unique when the market is complete.
Proposition 7Consider the probability Q , equivalent to P , de ned by the RadonNikodym derivative:
dQdP
= e12 R T 0 k ( s )k2 ds R t0 0 ( s ) d w
where () is the market price of risk. Then the instantaneous Qexpected return of any self nancing asset is r . Moreover, asset 0 denominated values S i ( t )S 0 ( t ) (as well asthe asset 0 denominated values of self nancing portfolios X ( t )S 0 ( t ) ) are Qmartingales.Q is thus a risk neutral probability.
Proof
By Girsanov Theorem, ew (t),
w (t) + R t
0 (s) ds is a Qmartingale, then:dew (t) , dw (t) + (t) dt dR = [ r 1 + ]dt + [dew (t) (t) dt] dR = r 1 dt + dew (t)We now de ne:
bS (t) ,S (t)S 0 (t)
with dS 0 (t) = S 0 rdt . Therefore:
d (S/S 0 )S/S 0 = d log
S S 0 +
1
22S dt
=dS S
dS 0S 0
1
22S dt +
1
2 2S dt
=dS S rdt
If the drift of dS S is r , then the drift of d bS ( t ) bS ( t )
will be zero! Thus, by changing the probability
measure from P to Q, the asset 0 denominated values S ( t )S 0 ( t ) are Qmartingales.
Q.E.D.
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Chapter 9 Links Between Probabilistic and PDE Approaches
Remark that when the set of primitive securities is complete ( N = M and is invertible) only one ( = 1
r 1) is compatible with the primitivereturns. Under incomplete markets an in nite number of s are compatible withthe assumed return dynamics and an in nite number of risk free probabilities canbe constructed.
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Chapter 11 Numeraire and Probability Changes
Chapter 11Numeraire and ProbabilityChanges
11.A Framework
11.A.i Assets
Asset returns follow are Ito process following the SDE:
dR = dt + dwdS 0 = rS 0dt
dr = r dt + r dw
The coefficients ( , , r , r ) are stochastic but satisfy Ito technical conditions
Assume N M , where N is the number of nonredundant risky assets, andM is the number of Brownian Motions.
11.A.ii Numeraires
De nition 23 A viable numeraire is an admissible self nancing portfolio with positive values a.s., i.e., (n , N ) is a viable numeraire if:
(n , N ) A0
N (t) > 0 a.s.
Example 7 S 0 (t) = S 0(0)eR t0 r (u )duExample 8 BT (t) = price at t of a zerocoupon bond yielding $1 at T
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Chapter 11 Numeraire and Probability Changes
11.B Correspondence Between Numeraires and MartingaleProbabilities
11.B.i Numeraire Martingale Probabilities
We have studied 0 P0, now consider any viable numeraire n and the correspondence n Pn .
De nition 24 Pn stands for the set of probabilities equivalent to P yielding ndenominated martingale prices: Pn = nP n P (x , X )A0,
X ( t )N ( t ) is a P n martingale o
Proposition 8For any admissible numeraire (n , N ) and Pn :
1. There is a onetoone correspondence between P0 (the set of riskneutralprobabilities) and Pn . Moreover, P 0P0, P 0 P n is given by:
dP ndP 0 =
N (T )S 0(T )
S 0(0)N (0)
and P n Pn , P n P 0 is given by:
dP 0dP n
=S 0(T )N (T )
N (0)S 0(0)
2. Pn 6= AoA ; Pn is a singleton market is complete
Proof
1. Consider a viable numeraire (n , N ). Without loss of generality assume N (0) =
S (0) = 1 .Let = dP ndP 0 =N (T )S 0 (T )
S 0 (0)N (0) . is a viable numeraire; indeed it satis es the three
requirements:(i) is F T measurable (since N (T ) and S 0(T ) are F T measurable);(ii) > 0 a.s. (since N (T ) and S 0(T ) are > 0 a.s.);(iii) E P 0 ( ) = 1; Indeed, since N (t )S 0 (t ) is a P 0 martingale: E P 0 (
N (T )S 0 (T ) ) =
N (0)S 0 (0) . There
fore E P 0 ( ) = E P 0 N (T )S 0 (T ) S 0 (0)N (0) = 1 .Then de nes uniquely a probability P n P 0. Moeover such P n belongs to Pn ;indeed, for any (x , X ) in A
0
:
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E P n (X (T )N (T ) ) = E P 0 (
X (T )N (T ) ) = E P 0 (
X (T )N (T )
N (T )S 0 (T )
S 0 (0)N (0) ) = E P 0 (
X (T )S 0 (T ) )
S 0 (0)N (0) =
X (0)S 0 (0)
S 0 (0)N (0) =
X (0)N (0) , hence
X N is a P N martingale. An analogous argument shows that 1/ =
dP 0dP n
de nes the correspondance between Pn and P0 and the two sets are thus in a oneto one relation through this procedure.2. Follows from 1. and from the fact that 2. is true for P0
Q.E.D.
More generally, given any (n , N ) and (n 0, N 0)N , there is a onetoone correspondence between Pn and Pn 0 de ned by:
dP n 0dP n
= N 0(T )
N (T ) N (0)
N 0(0)
with the possible assumption that N (0) = N 0(0) = 1
11.B.ii Probability Numeraire
Let us state the following without proof
Proposition 9For any probability Qto P there exists a numeraire n
Q such that the nQ denominated values of all admissible and self nanced assets or portfolios are Qmartingales.This numeraire is unique (up to a scale factor). The uniqueness of this martingale numeraire prevails even in incomplete markets and when asset prices follow semimartingales
11.C Summary
In AoA: There exists a set P0 of probabilities equivalent to P (the true or historical probability)
such that P 0P0 and (x ,X )A
0 (the set of admissible and self nancing strategies),the S 0(t) denominated price X ( t )S 0 ( t ) is a P 0 martingale.
P0 is a singleton when the market is complete. De ne N as the set of all admissible numeraires. Then N N iff (n ,N )A
0 and N (t) > 0 a.s. .
(n ,N )N there exists Pn P such that P n Pn and (x ,X )A0, the N (t)
denominated price X ( t )N ( t ) is a P n martingale.
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In the general case, given any (n , N ) and (n 0, N 0)
N , there is a onetoone correspondence between Pn and Pn 0 de ned by:
dP n 0dP n
=N 0(T )N (T )
N (0)N 0(0)
with the possible assumption that N (0) = N 0(0) = 1
Conversely, QP , there exists a unique numeraire N Q such that the N Q denominated prices are Qmartingales.
Then why not consider a portfolio (h ,H ) that yields martingale prices underthe true probability P ? This question is addressed in the following section.
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Chapter 12 The Numeraire (Growth Optimal) Portfolio
Chapter 12The Numeraire (Growth Optimal)Portfolio
12.A De nition and Characterization
12.A.i De nition of the Numeraire (h , H )
De nition 25 The numeraire portfolio is the unique portfolio (h , H ) N such that (x , X )A0, X ( t )H ( t ) is a P martingale, i.e.:
E t X (T )H (T )= X (t)H (t)where E t [] = E [F t ] is the conditional expectation computed with the true probability.
The price of the asset is then given as:
X (t) = H (t) E t X (T )H (T )with H (t )H (T ) often being referred to as the discount factor or the pricing kernel.
(h , H ) is also called the growth optimal portfolio (Merton), the logoptimalportfolio, and the numeraire portfolio (Long).
12.A.ii Characterization and Composition of (h , H )
Theorem 101. (h , H ) is the strategy that maximizes the expected log of the terminal wealth
W (T ), i.e. it solves the program:
max(x ,X )A 0
E 0 [logX (T )]
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Maximizing E [R0,T 0 ] then implies a twostep maximization procedure:max E [R0,T ] and max E [RT,T 0 ].
Q.E.D.
The previous results are also valid for semimartingale prices, but in the following,it is assumed that prices and rates of return follow Ito process, i.e.
dR = dt + dwdr = r dt + r dw
with R an N 1 vector, an N M matrix with rank N (there is no redundantasset, so N M ), and the coe fficients obey the usual technical conditions so thatthere exists a solution for the SDE.Theorem 11The composition of h is:
h = 1 r 1with = 0 being an invertible N N matrix.Proof
Because the Log utility is myopic, optimizing over ( t,T ) implies optimizing over [t, t + dt] ;In [t, t + dt]
maxx
E t [logX (t + dt)]
maxx E t [logX (t + dt) log X (t)] maxx E t [d log X ]
By Itos lemma, d log X = dXX 12
dXX
2. It is also known that:
dX X
= ( 1 x 0 1) rdt + x 0 dw= r + x 0 r 1dt + x 0 dwTherefore, the maximization program is now:
maxx
E t [d log X ]maxx r + x 0 r 1dt 12x0 dw dw 0 0xWith dw dw 0 = I dt
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Leave out dt and apply the rst order condition gives:
r 1 h = 0h = 1 r 1Q.E.D.
Corollary 5 (h ,H ) is instantaneously meanvariance e ffi cient, hence homothetical (proportional) to the tangent portfolio (m ,M ), i.e.
h =1
ktm
= 10 1 r 1mwith
kt =1
10 1 r 1and
m = kt 1
r 1
(the weights in m sum up to one while in h they dont; h is a combination of m and asset 0).We have not excluded the possibility that N < M . But now assume that N = M ; then:Corollary 6 When the market is complete and N N is invertible, the market price of risk can be derived as a function of the risk premia 6 :
= 1 r 1The composition of the numeraire portfolio can now be expressed in terms of as:
h = 1
r 1= 0 1 1 r 1= 0 1
6 When the market is not complete, cannot be explicitly speci ed. For the i t h asset, for instance,
i r =M
X j = 1 i j jWith N < M , the system of N equations does not yield an unique solution for 1 ,..., M
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12.B.i CAPM
Theorem 12Consider S A
0 with:
dS S
= S dt + S dwS
dH H
= nr + k k2odt + H dwH (CAPM) In AoA:
S r = HS =1
kt MS 1
kt=
M r 2M
HS = S H dwS dwH
Proof S ( t )H ( t ) is a P martingale, therefore it should have zero drift. By Itos lemma:
dS (t)dH (t)
=dS S
dH H
+ dH H 2 dS S dH H The drift term is:
S nr + k k2o+ k k2 HS = 0Therefore:
S r = HS (The rest remains to be proved).
Q.E.D.
12.B.ii Valuation
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H (t)H (T )
= e R Tt {r (u )+ 12 k k2 }du R Tt 0 dw (u )is called as the pricing kernel or the state price de ator. Its product withP ($ ), i.e. H (t )H (T ) P ($ ), is the ArrowDebreu price.
For a security with terminal cash ow X T and a dividend stream (t)dt, thepricing formula is:
X (0) = E o
1
H (T )X T
+ E o
Z T
0
(t)H (t)
dt
with the discount factor1H (t)
= e R t0 {r (u )+ 12 k k2 }du R t0 0 dw (u )In the certainty (riskless) case,
1H (t)
= e rt
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Chapter 13 Dynamic Consumption and Portfolio Choices (The Merton Model)
Chapter 13Dynamic Consumption andPortfolio Choices (The MertonModel)
13.A Framework
13.A.i The Capital Market
We consider a di ff usionstate variables model.
Let L be the number of state variables and Y be the vector describing the statesof the economy, with
dY L 1 = Y (t, Y (t )) dt + L M (t, Y (t )) dw M 1
There are N + 1 assets in the economy. The returns of the N risky assetsfollow the diff usion process:
dR N 1 = (t, Y (t )) dt + N M (t, Y (t )) dw M 1
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