I'm currently pricing American and European options on an underlying with sparse data (interpolated), high annual volatility and returns over the last year around 300%. The product isn't similar to anything quoted in the market at the moment, nor does the underlying have anything particularly correlated to it. Does anyone have any suggestions on how to go about this please? I've started with Heston's stochastic volatility model but as there's no option data available and calibrating it on price data doesn't seem to work due to the high sensitivity of the parameters probably so won't give such an accurate price. I realise this will be hard to get a good price on but need a decent idea of what it should be. Thanks!

  • $\begingroup$ How frequently does the underlying trade ? $\endgroup$ – Antoine Conze Jun 13 '18 at 12:43
  • $\begingroup$ @AntoineConze It depends, each block maybe 10/20 times a day $\endgroup$ – AlexAbrahams Jun 13 '18 at 12:49
  • $\begingroup$ tx. so why do you say that the there is sparse data (interpolated) for the underlying ? $\endgroup$ – Antoine Conze Jun 13 '18 at 13:04
  • $\begingroup$ Some of the year there’s very little traded but for the real reason is that at moment I don’t have much data, maybe a price every week or so. Working on cleaning up some more but need to come up for an option pricing model now with this little data. Any ideas? $\endgroup$ – AlexAbrahams Jun 13 '18 at 13:08
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    $\begingroup$ Only a general remark that when an underlying doesn't trade much it makes sense to incorporate a liquidity premium in the model, because the option cannot be properly delta hedged so the "risk neutral pricing" does not necessarily represent the price at which an option transaction would be done. But then it depends in what context you need to price the option (e.g. for reporting, or because you envision an actual trade, etc.). $\endgroup$ – Antoine Conze Jun 13 '18 at 13:21

I would take one step back and focus on the underlying.

Dissecting the underlying to identify components that you can find replacements with liquidity, that can be used in your pricing model. This will give you a baseline, with more data points. Since this sounds like some really exotic stuff, add a nice liquidity premium (as also suggested by Antoine Conze, in the comments of the question). Doing it this way obviously require good monitoring of your model over time, as the model fit might slide as the liquidity and 'characteristics' change over time.

Once you have a good / better pricing source, your options opens up and it's time to start the option modeling (pardon the pun).

In general dealing with modeling, there's the saying: Garbage in, garbage out.

  • $\begingroup$ Hi @chjortlund, thanks for your answer. Yes, liquidity premium will be considered for sure. My question was more about which model I should use, it should include SV as this really isn't constant but as I mentioned parameters seem hard to calibrate well without option data and with the price data. I need to price both American and European, the underlying doesn't return dividends so theoretically the prices should be the same, however in this case the buy can't (or would find it pretty hard) to sell the option meaning that the American price should be higher, any advice or references on this? $\endgroup$ – AlexAbrahams Jun 15 '18 at 15:58

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