A stock trades at today and will be worth or after one period, and a call with strike pays or . What should the call cost today when two analysts disagree about how likely the up move is? If a portfolio of the stock and a cash account pays exactly what the call pays in each state, any other price for the call would allow an arbitrage, and no probability is needed. This lesson writes replication as a linear system, solves its transpose for state prices, and expresses the same prices through a pricing kernel and physical probabilities.
Key terms
One-period model
Definition A one-period model has two dates, and , and a finite set of states , exactly one of which is revealed at date . There are traded assets. Asset costs at date and pays at date in state , both in currency units. The market is frictionless, which means that any real holdings, including short and fractional ones, can be traded at these prices with no transaction costs, taxes or limits on borrowing. A physical probability measure gives every state a probability , so every listed state can occur. In this lesson one traded asset is a cash account with price that pays in every state, where is the gross one-period return. A claim is a payoff vector , with paid in state .
In plain words The model lists everything that can happen by date and what each traded asset pays in each case. Probabilities say how likely the states are, and prices say what the assets cost today. They are separate inputs, and replication uses only the second.
Example The constructed market of this lesson has two states, down and up, always listed in that order. A stock costs and pays or . The cash account costs and pays in both states. The call with strike pays . One physical model gives the up state probability , so , and under it the stock’s expected payoff is .
Payoff matrix
Definition The payoff matrix of a one-period model is the matrix whose row lists the payoffs of all assets in state and whose column is the payoff vector of asset . A portfolio is a vector of holdings, in units of each asset. With the price vector , its payoff and its cost are
A claim is attainable when has a solution, which means that lies in the column space . Every claim is attainable exactly when , and an attainable claim has only one replicating portfolio exactly when , that is, when the columns are linearly independent.
In plain words The rows are the states and the columns are the assets. Whether a payoff can be built depends only on the columns, while what it costs also depends on the prices.
Example For the stock and the cash account, with the price vector written as a column,
Its determinant is , so its rank is . Every claim on the two states is attainable, and in exactly one way. If the stock were replaced by an asset paying in both states, its column would be twice the cash column, the rank would drop to , and only claims that pay the same amount in both states could be built.
Replicating portfolio
Definition A replicating portfolio for a claim is a portfolio with , so that it pays exactly in every state . Its replication cost is . Suppose two portfolios and both replicate but have different costs. Buying the cheaper one, selling the dearer one and placing the difference in the cash account costs nothing today, and since the two payoffs cancel, it pays times that difference in every state, which is an arbitrage. So in a market without arbitrage that trades the cash account, every attainable claim has one replication cost, whichever replicating portfolio is used. This is the law of one price for attainable claims. It is weaker than the absence of arbitrage, because a market can give equal payoffs equal prices and still offer an arbitrage.
In plain words A replicating portfolio is a hedge that matches the claim state by state, not only on average. If the claim also traded at a price other than the hedge’s cost, buying the cheaper of the two and selling the dearer one would lock in the difference.
Example For the call , write for the number of shares and for the currency amount placed in the cash account at date , which grows to . The replication equations, one for each state, are
Subtracting the first from the second gives , so , and then , so . The portfolio holds half a share and borrows about , owing at date in both states. It pays in the down state and in the up state, and it costs
No probability entered the calculation. The amount counts currency, which equals the number of cash-account units only because each unit costs . With a bond priced at that pays , the same borrowing would be a position of bonds.
State price
Definition A state-price vector is a vector that prices every traded asset as the sum of its state payoffs weighted by ,
which says for each asset . The number is the state price of , in date- currency per unit of currency paid in state . When the Arrow security that pays in state and in every other state is attainable, every state-price vector gives it the value , which is its replication cost. The cash account forces , so the state prices add up to the discount factor , not to . A result proved in The fundamental theorem of asset pricing says that this finite market, which trades the cash account, has no arbitrage if and only if it has a state-price vector with for every .
In plain words A state price is the cost today of receiving one unit of currency in one particular state and nothing otherwise. Adding the state prices gives the cost of one unit received for sure, which is less than when .
Example For the stock and the cash account the state-price equations are
The first gives . Putting into the second leaves , so and . Both are positive. For a general stock price and payoffs , the same two equations give
so both state prices are positive exactly when , which here reads .
Dual pricing system
Definition The replication system and the state-price system form a dual pricing system. The first combines the columns of to build a payoff, and the second weights the rows of to reproduce the prices. For any solutions of the two systems,
So every state-price vector values every attainable claim at its replication cost. When is square and invertible, both systems have unique solutions, and , and every claim gets the single value . When and the dual system has a solution, it has infinitely many, because then has a nonzero null space. These solutions agree on every attainable claim, but they do not all agree on a claim outside , which therefore has no single price fixed by replication. That case is treated in Market completeness and nonunique prices.
In plain words Building the claim from assets and pricing the states are two views of one linear algebra problem. Transposing the payoff matrix moves from one view to the other, and both give the same number for any payoff that can be built.
Example The call has the state-price value , the replication cost found above. A sure payment of is replicated by in the cash account and no stock, and its state-price value is as well. Now replace the stock by an asset that costs and pays in both states. Its price equation repeats the cash equation, so the dual system reduces to , and every pair of positive numbers with this sum is a positive state-price vector. All these pairs give the sure payment the value , but they value the call, which is no longer attainable, anywhere in the open interval from to .
Pricing kernel
Definition Let be a state-price vector and let give every state a probability . The pricing kernel, also called the stochastic discount factor, is the random variable with . For every claim ,
so for every attainable claim. Applied to the cash account this gives , and therefore
The kernel depends on the market prices and on the chosen , it is positive in every state exactly when the state prices are, and the division needs in every state. Discounting the physical mean payoff at the cash return gives the price of an attainable claim exactly when its covariance with is zero.
In plain words The kernel rewrites each state price as a probability times a discount that depends on the state. Averaging kernel times payoff with the physical probabilities gives back the market price, because the probabilities cancel. It does not say that a physical expected payoff should be discounted at one fixed rate.
Example With ,
Then , the discount factor, and , the call price. Discounting the physical mean payoff instead gives , which misses the price by the covariance term . Under this the stock’s expected payoff is below , so the covariance of with the stock payoff is positive and the kernel is larger in the up state. That ordering follows from these constructed inputs and is not a general statement about investors.
Check yourself
- In the same market, a put with strike pays in the down state and in the up state. Find its price from the state prices, find its replicating portfolio, and check put-call parity against the call price .
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The state prices give the put the value . The replication equations are
so , which gives , and then , which gives . The portfolio is short half a share and lends about , and it costs , the same value. For parity, the call minus the put pays in both states, which one share and a loan of replicate. So the call price minus the put price must be , and indeed .
- A classmate prices the call as its physical mean payoff discounted at the cash return, using , and gets . Suppose the call traded at that price. Build an arbitrage and find what it pays.
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The call would be cheaper than its replication cost . Buy the call for and sell the replicating portfolio, that is, short half a share and place in the cash account, which brings in . The net receipt today is , and it also goes into the cash account, so the whole position costs nothing. At date the call pays and the sold portfolio owes exactly , so these cancel, and the cash account pays in both states. The mistake is the formula itself. The price is , which exceeds here by .
- Keep the stock at with payoffs and , but let the cash account pay . Solve for the state prices and decide whether the market is free of arbitrage. What does the replication cost of the call mean in this market?
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The formulas give
The down-state price is negative, because the condition fails with . The market has an arbitrage. Short one share for and put the in the cash account. The position costs nothing and pays in the down state and in the up state. The payoff matrix is still invertible, so the call can still be replicated, with and , at the cost . That number is only the cost of one portfolio in a market that already offers a riskless profit, not an arbitrage-free price. The Arrow security for the down state, whose payoff is nonnegative, would cost . Solving the linear systems is not the test, and the positivity of the state prices is.