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Investor and Software Engineer but very new to quant finance here...

I have the below code (which I'm sure will be helpful for some) and have some questions regarding the function parameters!

  1. Is RF Rate your interest rate on treasuries? Basically your no risk competing return?
  2. How much will dividends influence the greeks? It is additional data I might not have access to?
  3. What are the time steps? I don't understand the wording...

def create_american_process(valuation_date, rf_rate, spot, ivol):
    #set calendar & day count
    calendar = ql.UnitedStates()
    day_counter = ql.ActualActual()

    #set evaluation date
    ql.Settings.instance().evaluationDate = valuation_date    

    #set rate & vol curves
    rate_ts = ql.FlatForward(valuation_date, ql.QuoteHandle(rf_rate), 
                        day_counter)

    vol_ts = ql.BlackConstantVol(valuation_date, calendar, 
                            ql.QuoteHandle(ivol), day_counter)  
    #create process
    process = ql.BlackScholesProcess(ql.QuoteHandle(spot),
                                ql.YieldTermStructureHandle(rate_ts),
                                ql.BlackVolTermStructureHandle(vol_ts))
    return process


def american_px_greeks(valuation_date, expiry, call_or_put, strike, div_dates, 
                       div_values, time_steps, process):
    #create instance as call or put
    if call_or_put.lower() == 'call':
        option_type = ql.Option.Call 
    elif call_or_put.lower() == 'put':
        option_type = ql.Option.Put 
    else:
        raise ValueError("The call_or_put value must be call or put.")        

    #set exercise and payoff
    exercise = ql.AmericanExercise(valuation_date, expiry)
    payoff = ql.PlainVanillaPayoff(option_type, strike)

    #create option instance
    option = ql.DividendVanillaOption(payoff, exercise, div_dates, div_values)

    #set mesh size for finite difference engine    
    grid_points = time_steps - 1                                  

    #create engine
    engine = ql.FDDividendAmericanEngine(process, time_steps, grid_points)
    option.setPricingEngine(engine)
    return option
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    $\begingroup$ Re Point 3. Option Theory involves Differential Equations, where time flows continuously. For the purpose of solving these on a computer they are often approximated by finite difference equations where time flows in "steps". For ex. there might be 10 points in time between now and expiration where you consider whether an American option should be exercised or not. This is what setting time_steps to 10 means. The more steps the more accurate the solution but also the slower the calculations. HTH. $\endgroup$
    – nbbo2
    Oct 23, 2021 at 12:28
  • 1
    $\begingroup$ Risk free is usually not treasury but SOFR (curve) nowadays. Dividends will play a role. I recommend looking at closed form solutions for Greeks of European vanilla black scholes to get and idea how sensitive Greeks are with regards to dividends. $\endgroup$
    – AKdemy
    Oct 25, 2021 at 19:04
  • $\begingroup$ @AKdemy gotcha this is so informative. I had heard about LIBOR but didn't know they moved away to SOFR. I realized that I don't know what the Spot parameter is referencing... it looks like SOFR right now is 0.05 so I would put that as rf_rate but the spot? $\endgroup$
    – Michael Paccione
    Oct 30, 2021 at 4:13
  • $\begingroup$ This may be helpful. For interest rates, it is customary to use stripped interest rate curves. These used to be Libor 3m based but are now mainly SOFR curves, which are fundamentally built in a similar way. $\endgroup$
    – AKdemy
    Oct 31, 2021 at 5:21

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