# Options-Quant: A Beginner’s Walkthrough

A beginner’s guide to using Options-Quant, the options pricing platform for quantitative retail traders.

Basic Options Pricing

This section will go over using the platform to price an option.

Let’s use the Merton Jump-Diffusion model (see why I recommend this model here):

First, head over to the model section and select the Jump Diffusion category. Then select “MertonJumpDiff”:

Now, we must enter the parameters for pricing our option. Here is a run-down of what each should go into each parameter.

Price: The price of the underlying (e.g. if you’re modeling an AAPL option and the stock price is 110.78, use that value for the price parameter).

Interest Rate: The interest rate of the benchmark bond that has a maturity closest to your options expiration (e.g. if you are pricing an option that expires in 30 days, use the 1-Month U.S. T-Bill Rate; if that rate is 1.49%, enter 0.0149).

Gamma: The rate of change of an option’s delta per \$1 move in the underlying price. For this parameter, I recommend using the gamma provided by the broker (you are not always required to use this parameter, but this model requires it). So, if your broker quotes the option as having a gamma of 0.1, enter that value.

Standard Deviation: The implied volatility of the option. This input can be increased or decreased to represent your view on the direction of volatility. Normally, you can use the value quoted by your broker, but in the following section, we show how to quickly derive your own implied volatility. If your broker quotes implied volatility at 25%, enter 0.25 in this field.

Days to Expiration: I recommend just using the calendar function and selecting the date that your option expires on.

Once you’ve entered in all of the fields, the program will return generated prices(for the fields not discussed here, leave them as the default):

Tip: Make use of the flexibility of the inputs, especially the standard deviation. If a stock has an upcoming corporate event and the implied volatility is high, reduce the standard deviation to simulate the event passing and see what the price of the option should look like. Can you put on a trade based on the value of the option after the high IV event has passed since you know the price before anyone else?

Having your own volatility that differs from the market’s is useful in establishing your edge as a trader. For pricing volatility, all of the parameters are the same, except you don’t enter volatility — you enter the option price.

Since the IV quoted by brokers are variables backed out by their own option pricing models, the program does the same except with a model of your choice:

Head over to the Implied Volatility section and for this example, we’ll use the BAWbisection model (more on why here):

Since the parameters are the same, we only need to enter option price and the expected dividend near or before the expiration date.

The program will now return a complete chain of implied volatilities:

Tip: A strategy described here involves calculating IV with the method described, then using that calculated IV as the standard deviation parameter in an options pricing model. This can be considered the ‘true’ value of an option.

You can also reverse this and price the option first, then enter the calculated options price to derive a better IV level. You can even go a step further and use the newer IV level to price the option again.

Final Considerations

This guide is not fully comprehensive of the platform as there are dozens more asset classes and hundreds of other models. What was discussed was enough for most traders to get on their feet and get the ball rolling. In those two steps we calculated our own implied volatility and generated better option prices than the ones quoted by the market.