  This paper aims to provide a practical example on the assessment and
propagation of input uncertainty for option pricing when using tree-based
methods. Input uncertainty is propagated into output uncertainty, reflecting
that option prices are as unknown as the inputs they are based on. Option
pricing formulas are tools whose validity is conditional not only on how close
the model represents reality, but also on the quality of the inputs they use,
and those inputs are usually not observable. We provide three alternative
frameworks to calibrate option pricing tree models, propagating parameter
uncertainty into the resulting option prices. We finally compare our methods
with classical calibration-based results assuming that there is no options
market established. These methods can be applied to pricing of instruments for
which there is not an options market, as well as a methodological tool to
account for parameter and model uncertainty in theoretical option pricing.
