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A discrete-time pricing model that builds a recombining tree of possible price paths. Unlike Black-Scholes, the binomial model handles American-style early exercise natively. Cox-Ross-Rubinstein (CRR) is the standard parameterization.
Key takeawayThis is how your broker actually prices American options. Black-Scholes gives the European value; the binomial tree adds the early-exercise premium on top.

The binomial model is how your broker actually prices American-style options. Black-Scholes handles European; binomial adds the early exercise decision at every node. If you trade US equity options, the price you see incorporates a binomial tree (or its numerical equivalent).
Build a tree of possible price paths: at each step, the stock moves up by factor u or down by factor d. At expiration, calculate the option payoff at each terminal node. Work backwards through the tree, comparing the hold value (discounted expected payoff) to the exercise value (intrinsic) at each node. The greater of the two is the node's value.
AAPL at $200, $195 put, 30 DTE, σ = 25%. A 100-step CRR tree prices the put at $3.82. Black-Scholes (European) gives $3.75. The $0.07 difference is the early exercise premium — the value of being able to exercise on any of those 100 nodes if the stock drops far enough.
Thinking more tree steps always means more accuracy. Beyond 100-200 steps, the improvement is negligible. Also: binomial trees can be slow for exotic options with multiple path-dependent features — Monte Carlo is often better for those.
A discrete-time pricing model that builds a recombining tree of possible price paths. Unlike Black-Scholes, the binomial model handles American-style early exercise natively. Cox-Ross-Rubinstein (CRR) is the standard parameterization.
This is how your broker actually prices American options. Black-Scholes gives the European value; the binomial tree adds the early-exercise premium on top.
Build a tree of possible price paths: at each step, the stock moves up by factor u or down by factor d. At expiration, calculate the option payoff at each terminal node. Work backwards through the tree, comparing the hold value (discounted expected payoff) to the exercise value (intrinsic) at each node. The greater of the two is the node's value.
Thinking more tree steps always means more accuracy. Beyond 100-200 steps, the improvement is negligible. Also: binomial trees can be slow for exotic options with multiple path-dependent features — Monte Carlo is often better for those.
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