Editing Binomial options pricing model (section)

==Use of the model==
The Binomial options pricing model approach has been widely used since it is able to handle a variety of conditions for which other models cannot easily be applied. This is largely because the BOPM is based on the description of an [[underlying instrument]] over a period of time rather than a single point. As a consequence, it is used to value [[American option]]s that are exercisable at any time in a given interval as well as [[Bermudan option]]s that are exercisable at specific instances of time. Being relatively simple, the model is readily implementable in computer [[software]] (including a [[spreadsheet]]).

Although higher in computational complexity and computationally slower than the [[Black–Scholes model|Black–Scholes formula]], it is more accurate, particularly for longer-dated options on securities with [[dividend]] payments. For these reasons, various versions of the binomial model are widely used by practitioners in the options markets.{{Citation needed|date=May 2016}}

For options with several sources of uncertainty (e.g., [[real option]]s) and for options with complicated features (e.g., [[Asian option]]s), binomial methods are less practical due to several difficulties, and [[Monte Carlo option model]]s are commonly used instead. When simulating a small number of time steps [[Monte Carlo simulation]] will be more computationally time-consuming than BOPM (cf. [[Monte Carlo methods in finance]]). However, the worst-case runtime of BOPM will be [[Exponential time|O(2<sup>n</sup>)]], where n is the number of time steps in the simulation. Monte Carlo simulations will generally have a [[Polynomial time|polynomial time complexity]], and will be faster for large numbers of simulation steps. [[Monte Carlo simulation]]s are also less susceptible to sampling errors, since binomial techniques use discrete time units. This becomes more true the smaller the discrete units become.