The Options-Quant software features hundreds of quantitative pricing models, so choosing just one can be understandably tricky. Since most of our models are derived from research papers and other institutions, finding more information about a specific model is as simple as a Google search.
For pricing options, we recommend the "AmericanBAW 2" model, as it is extraordinarily good at estimating the fair price of an option. For pricing/calculating implied volatility, we recommend the "BAWbisection" model.
Relative Value Trading
The Options-Quant platform allows you to price virtually any option for any asset. Because of this, a popular strategy used by our traders is relative value (RV) trading. RV trading involves comparing one asset's worth to a similar asset's worth.
An example of this would be pricing an at-the-money call option on Apple, then pricing an at-the-money call for Microsoft. In this scenario, the two options should have relatively similar characteristics like implied volatility and price, so a trader would look for significant differences in the two options.
If, for example, when pricing the volatility using the "BAWbisection" model, the trader sees that one option has a significantly higher implied volatility than the other, they can short the option with the over-priced volatility, then buy the option with the under-priced volatility. Since the underlying assets are similar, the position is market-neutral and the main risk exposure becomes the targeted volatility. In the example, A profit is realized when the implied volatilities either converge, decrease, or increase.
Another popular strategy used by our traders involves finding mis-priced options based on special events. Generally, in times of high volatility, the chance that prices will be out-of-whack increases significantly. An example of this can be found before/during earnings events.
In the days leading up to an earnings event, the prices of all options of a stock greatly increase as the market prepares for large post-earnings movements. Using the "MertonJumpDiffusion" model for example, the trader can price individual options for stocks with approaching earnings then, based on the model results, can determine whether the market is over or under pricing a particular option/event.
This form of trading is most optimal from a discretionary perspective (e.g., a trader with their own estimate of implied volatility can use the platform to arrive at a "target" price based on that estimate by plugging in their IV estimate into one of our pricing models).
Options-Quant is a great tool for anyone managing a portfolio or multiple option positions. One applicable use-case for risk management is using event simulations to see how certain changes would have an effect on a portfolio.
For example, a trader who owns a basket of options wants to know how their position will perform if the next day's volatility is 5% higher or lower. To do this, they simply choose the pricing model of their liking, and just change the implied volatility parameters.
Given the extraordinary accuracy of our models, assuming the trader simulates a 10% increase in implied volatility, the theoretical price shown would be nearly-identical to the price that would actually be seen if markets made that move.
Knowing what the near-exact price of an option will be under any given event is extremely useful for positional risk management and finding opportunities. An example of this in practice would be if markets made the simulated move, but market prices were far from what the model implied, so a relative-value trade is put on.