A computational software leverages a discrete-time framework to find out the theoretical worth of an choice. This framework divides the choice’s life right into a sequence of time steps. At every step, the mannequin assumes the underlying asset value can transfer both up or down by a particular issue. By working backward from the choice’s expiration date, calculating the payoffs at every node on this “tree” of doable value actions, and discounting these payoffs again to the current, the software arrives at an choice’s current worth.
This strategy gives a number of benefits. Its relative simplicity facilitates understanding of choice pricing rules, even for these new to the topic. The strategy readily adapts to choices with early train options, comparable to American-style choices, which pose challenges for different valuation methods. Traditionally, earlier than widespread computational energy, this mannequin provided a tractable technique for pricing choices, paving the way in which for extra advanced fashions later. Its pedagogical worth stays robust right now.
