Calculation of market price for option at underlying strike price at some point in future

External question — Quantitative Finance Stack Exchange Author: morleyc Original post: https://quant.stackexchange.com/questions/61133 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. Would appreciate clarification on the below scenario. If a put option was sold at the start of the week, when the broker (Interactive Brokers) calculates the cost basis (the premium collected) are the option greeks fixed at that point of writing the option and used for this cost basis (along with commission)? I need to be able to calculate for any specific time of the day, from the current underlying price and its current volatility and time to expiration, what the option value would be should it move to the strike within the next hour or so (to define this time movement would be a amazing, but for sake of example 60 minutes would suffice). I am using the strike price of the underlying as the exit condition as opposed to the stop loss on the contract itself - knowing what the option value would become should it hit the strike in the near future I can put additional hedging in place to offset the cost when buying to close the option position. Would this be along the lines of Black-Scholes Merton (BSM), or would I need to subscribe to live option market data (as that's only giving current price of options) or a combination thereof? As a side note any recommendations for paid commercial options data would be appreciated.

External answer — Quantitative Finance Stack Exchange Author: justasking Original post: https://quant.stackexchange.com/a/61295 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. A theoretical answer to your question is provided by Black-Scholes but remember that actual option prices are set by supply and demand (with guidance from models for different market participants.) You could also try to fit a model to the actual data to solve this problem, though that would be a much more complex (and costly) solution.