Chapter 1: The binomial asset pricing model


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1 Chapter 1: The binomial asset pricing model Simone Calogero April 17, 2015 Contents 1 The binomial model dimensional stock markets 4 3 Arbitrage portfolio 8 4 Implementation of the binomial model with Matlab 11 1 The binomial model The binomial asset pricing model, or simply the binomial model, is a model for the evolution in time of the price of a financial asset. It is typically applied to stocks, hence we denote by S(t) the price of the asset at time t. We are interested in monitoring the price in some finite time interval [0, T ], where T > 0 could be for instance the expiration date of an option on the stock. The price of the stock at the present time t = 0 is denoted by S 0 and is assumed to be known. In the binomial model it is imposed that the price of the stock can only change at some given predefined times 0 = t 0 < t 1 < t 2 < < t N = T ; moreover the price at time t i+1 depends only on the price at time t i and the result of tossing a coin. Precisely, letting u, d R, u > d, and p u (0, 1), we assume S(t i+1 ) = S(t i )e u, with probability p u, S(t i+1 ) = S(t i )e d, with probability p d = 1 p u, for all i = 0,..., N 1. Here we may interpret p u as the probability to get a head in a coin toss (p u = p d = 1/2 for a fair coin). We restrict to the standard binomial model, which assumes that the parameters u, d, p u are timeindependent and that the stock pays no dividend in the interval [0, T ] (see Chapter 4 for a generalization of the binomial model). In the applications one typically chooses u > 0 and d < 0 (e.g., d = u is quite common), hence u stands for up, since S(t i+1 ) = S(t i )e u > S(t i ), while d stands for down, for 1
2 S(t i+1 ) = S(t i )e d < S(t i ). In the first case we say that the stock price goes up at time t i+1, in the second case that it goes down at time t i+1. Next we introduce a number of assumptions which simplify the analysis of the model without compromising its generality. Firstly we assume that the times t 0, t 1,... t N are equidistant, that is t i+1 t i = h > 0. In the applications the value of h must be chosen much smaller than T. Without loss of generality we can pick h = 1, and so t 1 = 1, t 2 = 2,... t N = T = N, with N >> 1. For instance, if N = 67 (the number of trading days in a period of 3 months), then h = 1 day and S(t), for t {1,... N}, may refer to the closing price of the stock at each day. It is convenient to denote I = {1,..., N}. Hence, from now on, we assume that the price of the stock in the binomial model is determined by the rule S(0) = S 0 and { S(t 1)e u, with probability p u S(t) =, t I. (1) S(t 1)e d, with probability p d = 1 p u Each possible sequence (S(1),..., S(N)) of the future stock prices determined by the binomial model is called a path of the stock price. Clearly, there exists 2 N possible paths of the stock price in a Nperiod model. Letting {u, d} N = {x = (x 1, x 2,..., x N ) R N : x t = u or x t = d, t I} be the space of all possible Nsequences of ups and downs, we obtain a unique path of the stock price (S(1),..., S(N)) for each x {u, d} N. For instance, for N = 3 and x = (u, u, u) the corresponding stock price path is given by S 0 S(1) = S 0 e u S(2) = S(1)e u = S 0 e 2u S(3) = S(2)e u = S 0 e 3u, while for x = (u, d, u) we obtain the path S 0 S(1) = S 0 e u S(2) = S(1)e d = S 0 e u+d S(3) = S(2)e u = S 0 e 2u+d. In general the possible paths of the stock price for the 3period model can be represented as 2
3 u S(3) = S 0 e 3u S(0) = S 0 u d S(1) = S 0 e u S(1) = S 0 e d u d u d S(2) = S 0 e 2u S(2) = S 0 e u+d S(2) = S 0 e 2d d u d u S(3) = S 0 e 2u+d S(3) = S 0 e u+2d d S(3) = S 0 e 3d which is an example of binomial tree. Note that the admissible values for the price S(t) at time t are given by S(t) {S 0 e ku+(t k)d, k = 0,..., t}, for all t I, where k is the number of times that the price goes up up to the time t. Definition 1.1. Given x = (x 1,... x N ) {u, d} N, the binomial asset price S(t, x) at time t I corresponding to x is given by S(t, x) = S 0 exp(x 1 + x x t ). The vector S x = (S(1, x),... S(N, x)) is called a path of the stock price. Moreover we define the probability 1 of the path S x as P(S x ) = (p u ) Nu(x) (p d ) N d(x), where N u (x) is the number of u s in the sequence x and N d (x) = N N u (x) is the number of d s. The probability that the stock follows one of the two paths S x, S y is given by P(S x )+P(S y ) (and similarly for any number of paths). Exercise 1. Prove that x {u,d} N P(S x ) = 1. 1 We shall say more about the probabilistic interpretation of the binomial model in Chapter 4. 3
4 2 1+1 dimensional stock markets A 1+1 dimensional stock market is a market that consists of a stock and a bond. We model the price of the stock with the binomial model (1), while for the bond price we postulate a constant (instantaneous) interest rate r > 0. Hence the value at time t of the bond is given by B(t) = B 0 e rt, t I, (2) where B 0 is the present (at time t = 0) value of the bond. A portfolio process invested in a 1+1 dimensional stock market is a finite sequence {(h S (t), h B (t))} t I, where h S (t) is the (real 2 ) number of shares invested in the stock and h B (t) the number of shares invested in the bond during the time interval (t 1, t], t I. The initial position of the investor is given by (h S (0), h B (0)). Without loss of generality we assume that h S (0) = h S (1), h B (0) = h B (1), (3) i.e, (h S (1), h B (1)) is the investor position on the closed interval [0, 1] (and not just in the semiopen interval (0, 1]). Remark 2.1. It is clear that the investor will change the position on the stock and the bond according to the path followed by the stock price, and so (h S (t), h B (t)) is in general pathdependent. When we want to emphasize the dependence of a portfolio position on the path of the stock price we shall write h S (t) = h S (t, x), h B (t) = h B (t, x). The value of the portfolio process at time t is given by V (t) = h S (t)s(t) + h B (t)b(t). (4) We write V (t) = V (t, x) if we want to emphasize the dependence of the portfolio value on the path of the stock price. Clearly, V (t, x) = h S (t, x)s(t, x) + h B (t, x)b(t). We say that the portfolio process is selffinancing if purchasing more shares of one asset is possible only by selling shares of the other asset for an equivalent value (and not by infusing new cash into the portfolio), and, conversely, if any cash obtained by selling one asset is immediately reinvested to buy shares of the other asset (and not withdrawn from the portfolio). To translate this condition into a mathematical formula, recall that (h S (t), h B (t)) is the investor position on the stock and the bond during the time interval (t 1, t]. Let 2 Recall that we assume that the agent can invest in any fractional number of shares of an asset. Of course in the applications this has to be rounded to an integer number. 4
5 V (t) be the value of this portfolio at the time t, given by (4). At the time t, the investor sells/buys shares of the assets. Let (h S (t + 1), h B (t + 1)) be the new position on the stock and the bond in the interval (t, t + 1]. Then the value of the new portfolio at time t is given by V (t) = h S (t + 1)S(t) + h B (t + 1)B(t). The difference V (t) V (t), if not zero, corresponds to cash withdrawn or added to the portfolio as a result of the change in the position on the assets. In a selffinancing portfolio, however, this difference must be zero. We thus must have V (t) V (t) = 0, which leads to the following definition. Definition 2.2. A portfolio process {(h S (t), h B (t))} t I invested in a dimensional stock market is said to be selffinancing if holds for all t I. h S (t)s(t 1) + h B (t)b(t 1) = h S (t 1)S(t 1) + h B (t 1)B(t 1) (5) Remark 2.3. Constant (i.e., timeindependent) portfolio processes are clearly selffinancing. In fact, (5) holds if h S (t) = h S (t 1) and h B (t) = h B (t 1), for all t I. In the next theorem we show that the selffinancing property of a portfolio process is very restrictive: the value of a selffinancing portfolio at time t = N determines the value of the portfolio at any earlier time t = 0,..., N 1. This result is crucial to justify our definition of fair price to European derivatives in the next chapter. Theorem 2.4. Let {(h S (t), h B (t))} t I be a selffinancing portfolio process with value V (N) at time t = N. Define q u = er e d e u e d, Then for t = 0,... N 1, V (t) is given by V (t) = e r(n t) In particular, at time t = 0 we have V (0) = e rn q d = eu e r e u e d. (6) (x t+1,...,x N ) {u,d} N t q xt+1 q xn V (N, x). (7) x {u,d} N (q u ) Nu(x) (q d ) Nd(x) V (N, x). Proof. We prove the statement by induction on t = 0,... N 1. Step 1. We first prove it for t = N 1. In this case the sum in (7) is over two terms, one for which x N = u and one for which x N = d. Hence the claim becomes V (N 1) = e r [q u V (N, (x 1,..., x N 1, u)) + q d V (N, (x 1,..., x N 1, d))]. (8) 5
6 In the right hand side of (8) we replace and V (N, (x 1,..., x N 1, u)) = h S (N)S(N 1)e u + h B (N)B(N 1)e r V (N, (x 1,..., x N 1, d)) = h S (N)S(N 1)e d + h B (N)B(N 1)e r, which follow by the definition of portfolio value. So doing we obtain r.h.s (8) =e r [q u (h S (N)S(N 1)e u + h B (N)B(N 1)e r ) + q d (h S (N)S(N 1)e d + h B (N)B(N 1)e r )] = e r [h S (N)S(N 1)(e u q u + e d q d ) + h B (N)B(N 1)e r (q u + q d )] = e r [h S (N)S(N 1)e r + h B (N)B(N 1)e r ] = h S (N)S(N 1) + h B (N)B(N 1), (9) where we used that e u q u + e d q d = e r and q u + q d = 1. By the definition of selffinancing portfolio process, the expression in the last line of (9) is V (N 1), which proves the claim for t = N 1. Step 2. Now assume that the statement is true at time t + 1, i.e., V (t + 1) = e r(n t 1) q xt+2 q xn V (N, x). (x t+2,...x N ) {u,d} N t 1 Step 3. We now prove it at time t. Let V u (t + 1) V (t + 1) assuming x t+1 = u = h S (t + 1)S(t)e u + h B (t + 1)B(t)e r and similarly we define V d (t + 1). Proceeding exactly as in (9) above gives the identity e r [q u V u (t + 1) + q d V d (t + 1)] = h S (t + 1)S(t) + h B (t + 1)B(t). By the selffinancing property, the right hand side is V (t). Thus V (t) = e r [q u V u (t + 1) + q d V d (t + 1)]. (10) On the other hand, by the induction hypothesis of step 2 we have V u (t + 1) = e r(n t 1) q xt+2 q xn V (N, x 1,..., x t, u, x t+2,..., x N ), (x t+2,...,x N ) {u,d} N t 1 V d (t + 1) = e r(n t 1) q xt+2 q xn V (N, x 1,..., x t, d, x t+2,..., x N ). (x t+2,...,x N ) {u,d} N t 1 6
7 Replacing in (10) we obtain V (t) = e r(n t) q u q xt+2 q xn V (N, x 1,..., x t, u, x t+2,..., x N ) (x t+2,...,x N ) {u,d} N t 1 + q d q xt+2 q xn V (N, x 1,..., x t, d, x t+2,..., x N ) (x t+1,...,x N ) {u,d} N t 1 = e r(n t) q xt+1 q xn V (N, x), (x t+1,...x N ) {u,d} N t which completes the proof. Remark 2.5. As the sum in (7) is over (x t+1,..., x N ), while V (N) depends on the full path x {u, d} N, then V (t) depends on the path of the stock price up to time t, i.e., V (t) = V (t, x 1, x 2,..., x t ). In the process of proving Theorem 2.4 we have also established the important formula (10). As this formula will be used several times in the next chapters, it deserves to be stated in a separate theorem. Theorem 2.6. The value of a selffinancing portfolio invested in a 1+1 dimensional stock market satisfies the recurrence formula where V (t 1) = e r [q u V u (t) + q d V d (t)], for t I, (11) V u (t) = h S (t)s(t 1)e u + h B (t)b(t 1)e r is the value of the portfolio at time t, assuming that the stock price goes up at this time, and V d (t) = h S (t)s(t 1)e d + h B (t)b(t 1)e r is the value of the portfolio at time t, assuming that the stock price goes down at this time. Example. We conclude this section with an example of application of (7) and (11). Assume N = 2 and consider the constant (and then selffinancing) portfolio h S (t) = 1, h B (t) = 1, t = 0, 1, 2. Then V (2) = S(2) B(2), B(2) = B 0 e 2r. The possible paths are x {u, d} 2. Hence S(2) can take 3 possible values: 1. S(2) = S(2, (u, u)) = S 0 e 2u, for x = (u, u); 2. S(2) = S(2, (u, d)) = S(2, (d, u)) = S 0 e u+d, for x = (u, d) or x = (d, u); 7
8 3. S(2) = S(2, (d, d)) = S 0 e 2d, for x = (d, d). The value of the portfolio at time t = 2 along all possible paths is 1. V (2) = V (2, (u, u)) = S 0 e 2u B 0 e 2r, for x = (u, u); 2. V (2) = V (2, (u, d)) = V (2, (d, u)) = S 0 e u+d B 0 e 2r, for x = (u, d) or x = (d, u); 3. V (2) = V (2, (d, d)) = S 0 e 2d B 0 e 2r, for x = (d, d). Hence (7) gives, for N = 2, t = 1, and x 1 = u, V (1, u) = e r (q u V (2, (u, u)) + q d V (2, (u, d)) = e r (q u (S 0 e 2u B 0 e 2r ) + q d (S 0 e u+d B 0 e 2r )). Using the identities q u e u + q d e d = e r and q u + q d = 1, we obtain V (1, u) = S 0 e u B 0 e r, (12) which if of course correct because the right hand side is S(1, (u, u)) B(1), i.e., the value of the portfolio at time t = 1, assuming that the price of the stock goes up at this time. Similarly, applying (7) for x 1 = d we obtain the (correct) formula Finally we compute V (0) with (7): V (1, d) = S 0 e d B 0 e r. (13) V (0) = e 2r [(q u ) 2 V (2, (u, u)) + q u q d V (2, (u, d)) + q d q u V (2, (d, u)) + (q d ) 2 V (2, (d, d))]. After the proper simplifications we obtain V (0) = S 0 B 0, which is of course correct by definition of portfolio value at time t = 0. Note that we obtain the same result by applying the recurrence formula (11). In fact, as V u (1) = V (1, u) and V d (1) = V (1, d), and using (12), (13), we obtain V (0) = e r (q u V u (1) + q d V d (1)) = e r ((q u e u + q d e d )S 0 (q u + q d )e r ) = S 0 B 0. 3 Arbitrage portfolio Our next purpose is to prove that the 1+1 dimensional market introduced in the previous section is arbitrage free, provided its parameters satisfy d < r < u (no condition is required on the probability p u ). To achieve this we first need to introduce the precise definition of arbitrage portfolio. Definition 3.1. A portfolio process {(h S (t), h B (t)} t I invested in a 1+1 dimensional stock market is called an arbitrage portfolio if its value V (t) satisfies 8
9 V (0) = 0; V (N, x) 0, for all x {u, d} N ; There exists y {u, d} N such that V (N, y) > 0. We say that a 1+1 dimensional stock market is arbitragefree if there exist no selffinancing arbitrage portfolio process invested in the stock and the bond. Let us comment on the previous definition. Condition 1) means that no initial wealth is required to set up the portfolio, i.e., the long and short positions on the two assets are perfectly balanced. In particular, anyone can (in principle) open such portfolio. Condition 2) means that the investor is sure not to loose money with this investment: regardless of the path followed by the stock price, the return of the portfolio is always nonnegative. Condition 3) means that there is a strictly positive probability to make a profit, since along at least one path of the stock price the return of the portfolio is strictly positive. We remark that in the definition of arbitragefree market the property selffinancing is sometimes dropped. Theorem 3.2. A 1+1 dimensional stock market is arbitrage free if and only if d < r < u. Proof. We divide the proof in two steps. In step 1 we prove the claim for the 1period model, i.e., N = 1. The (easy) generalization to the multiperiod model (N > 1) is carried out in step 2. Note also the claim of the theorem is logically equivalent to the following: There exists a selffinancing arbitrage portfolio in the 1+1 dimensional stock market if and only if r / (d, u). It is the latter claim which is actually proved below. Step 1: the 1period model. Because of our convention (3), we can set h S (0) = h S (1) = h S, h B (0) = h B (1) = h B, i.e., the portfolio position in the 1period model is constant (and thus selffinancing) over the interval [0, 1]. The value of the portfolio at time t = 0 is V (0) = h S S 0 + h B B 0, while at time t = 1 it is given by one of the following: if the stock price goes up at time t = 1, or V (1) = V (1, u) = h S S 0 e u + h B B 0 e r, V (1) = V (1, d) = h S S 0 e d + h B B 0 e r, if the stock price goes down at time t = 1. Thus the portfolio is an arbitrage if V (0) = 0, i.e., h S S 0 + h B B 0 = 0, (14) 9
10 if V (1) 0, i.e., h S S 0 e u + h B B 0 e r 0, (15) h S S 0 e d + h B B 0 e r 0, (16) and if at least one of the inequalities in (15)(16) is strict. Now assume that (h S, h B ) is an arbitrage portfolio. From (14) we have h B B 0 = h S S 0 and therefore (15)(16) become h S S 0 (e u e r ) 0, (17) h S S 0 (e d e r ) 0. (18) Since at least one of the inequalities must be strict, then h S 0. If h S > 0, then (17) gives u r, while (18) gives d r. As u > d, the last two inequalities are equivalent to r d. Similarly, for h S < 0 we obtain u r and d r which, again due to u > d, are equivalent to r u. We conclude that the existence of an arbitrage portfolio implies r d or r u, that is r / (d, u). This proves that for r (d, u) there is no arbitrage portfolio in the one period model, and thus the if part of the theorem is proved for N = 1. To prove the only if part, i.e., the fact that r (d, u) is necessary for the absence of arbitrages, we construct an arbitrage portfolio when r / (d, u). Assume r d. Let us pick h S = 1 and h B = S 0 /B 0. Then V (0) = 0. Moreover (16) is trivially satisfied and, since u > d, h S S 0 e u + h B B 0 e r = S 0 (e u e r ) > S 0 (e d e r ) 0, hence the inequality (15) is strict. This shows that one can construct an arbitrage portfolio if r d and a similar argument can be used to find an arbitrage portfolio when r u. The proof of the theorem for the 1period model is complete. Step 2: the multiperiod model. Let r / (d, u). As shown in the previous step, there exists an arbitrage portfolio (h S, h B ) in the single period model. We can now build a selffinancing arbitrage portfolio process {(h S (t), h B (t))} t I for the multiperiod model by investing at time t = 1 the whole value of the portfolio (h S, h B ) in the bond. Hence the value of this portfolio process satisfies V (0) = 0 and V (N, x) = V (1, x)e r(n 1) V (1, x) 0 along every path x {u, d} N. Moreover, since (h s, h B ) is an arbitrage, then V (1, y) > 0 along some path and thus V (N, y) V (1, y) > 0. Hence the constructed selffinancing portfolio process {(h S (t), h B (t))} t I is an arbitrage and the if part of the theorem is proved. To prove the only if part for the multiperiod model, we use that 3, by Theorem 2.4, V (0) = e rn (q u ) Nu(x) (q d ) Nd(x) V (N, x), x {u,d} N 3 Note that this is the only part of the proof where we need to use that the portfolio is selffinancing. 10
11 where q u, q d are given by (6), N u (x) is the number of ups in x and N d (x) = N N u (x) the number of downs. Now, assume that the portfolio is an arbitrage. Then V (0) = 0 and V (N, x) 0. Of course, the above sum can be restricted to the paths along which V (N, x) > 0, which exist since the portfolio is an arbitrage. But then the sum can be zero only if either one of the factors q u, q d is zero, or if they have opposite sign. Since u > d, the denominator in the expressions (6) is positive, hence q u = 0 resp. q d = 0 r = d resp. u = r, (q u > 0, q d < 0) resp. (q u < 0, q d > 0) u < r resp. r < d. We conclude that the existence of a selffinancing arbitrage portfolio entails r / (d, u), which completes the proof of the theorem. Remark 3.3. When the 1+1 dimensional stock market is arbitragefree, i.e.,, for d < r < u, the parameters q u, q d satisfy q u (0, 1), q d = 1 q u (0, 1), hence the pair q u, q d defines a probability, which is called the riskneutral probability. The reason for this terminology will become clear in Chapter 4. 4 Implementation of the binomial model with Matlab It is a simple matter to implement the binomial asset pricing model with Matlab. The following code defines a function BinomialStock, which generates a binomial tree for the stock price. function S=BinomialStock(u,d,s,N) S=zeros(N+1); S(1,1)=s; for j=1:n S(1,j+1)=S(1,j)*exp(u); for i=1:j S(i+1,j+1)=S(i,j)*exp(d); end end The arguments of the function are the parameters u, d, the initial price of the stock s and the number of step N in the binomial model. The function returns an uppertriangular (N + 1) (N + 1) matrix S. The column j contains, in decreasing order along rows, the possible prices of the stock at time j 1. A path of the stock price is obtained by moving from each column to next one by either stays in the same row (which means that the price went up at this step) or going down one row (which means that the price went down at this step). 11
12 Exercise 2. Define a function RandomPath(S,p), which generates a random path from the binomial tree S created with the function BinomialStock and compute the probability of this path. The argument p is the probability that the price of the stock goes up. Plot the price of the stock as a function of time t {0,..., N}. We remark that it is impossible to generate all possible 2 N paths of the stock price. Even for a relatively small number of steps, like N 50, the number of admissible paths is too big. Note also that the probability of each single path is practically zero for N 10. In Chapter 4 we shall learn how to compute the (much more interesting) probability that the price S(t) lies within a given range [a, b], i.e., P(a < S(t) < b). 12
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