I'm trying to understand how the authers of a paper calibrated their model.
We got data on European type options on the S&P500-index period from early 2005 to mid-2009. We have daily data on option prices; 182 implied volatilities for each day in our data-set with moneyness from -30% to +30% (of current underlying) and between one month and three years to expiry (See the image attached below as an example for 31-jul-2009)
I want to calibrate a Heston Model to this data by minimizing
\begin{align} \sum_{t,k}(IV_{t,k}-IV_{t,k}^{\Theta})^2 \end{align} where $IV_{t,k}$ and $IV_{t,k}^\Theta$ are the quoted and model implied volatilities, respectively. (t,k) are the maturity-strike combinations.
My question is:
How do I compute $IV_{t,k}^{\Theta}$ using the data given in the screenshot?
EDIT:
I want to use the first method and calculate the BS prices.
So for example consider the call from 31-Jul-2009 with strike 1283.72 and tenor = 3M.
I use the following function:
blackscholes(call, S0, K, r, time till expiration in years, volatility, dividend_yield)
blackscholes(call, 987.48, 1283.72, 0.47%, 0.25, 16.8%, 2.36%)
Are these the correct values for this example? (I took the ZeroRate as risk-free interest rate)
And then do the same for every combination of strikes and tenors.