Helpers and calibration

rl.VolatilityHelper(maturity, strike, volatility, kind='call', dayCounter=None)

A European option quoted in Black volatility, as QuantLib’s HestonModelHelper. After setPricingEngine it gives marketValue(), modelValue(), impliedVolatility(), calibrationError() (relative price error) and volatilityError().

rl.calibrate(model, helpers, parameters, engine=None, bounds=None, useVolatilityError=False, **kwargs)

Least squares on the helpers’ calibration errors over the named model attributes (per-regime lists) and "chain" for the off-diagonal switching rates; engine is a factory model -> engine (default the numerical engine). Returns the scipy result and leaves the model at the fitted values.

helpers = [rl.VolatilityHelper(T, K, vol) for T, K, vol in quotes]
model = rl.SwitchingBlackScholesProcess(rl.RegimeChain.twoState(3.0, 3.0), 100.0, 0.02, 0.0, [0.30, 0.20])
result = rl.calibrate(model, helpers, ["sigma", "chain"], engine=lambda m: rl.NumericalSwitchingEngine(m, regime=1))
model.sigma, model.chain.generator
max(abs(h.calibrationError()) for h in helpers)