In this acticle the SABR model is first presented in another form ( see equation 7 in the article ) and then extended to the so called ZABR model. I have a couple of questions to help me understand the model.

I just read that SABR model is a stochastic volatility model, which attempts to capture the volatility smile in derivatives markets. The name stands for "stochastic alpha, beta, rho", referring to ...

2020年9月21日 · ZABR classes are currently only in C++ and Python via SWIG, at least to my knowledge. QuantlibXL is generally not as quick to receive updates to developments in the C++ library because a substantial amount of work goes into writing in the XMLs for each new class.

You don't want to use the SABR (or an extension) to price equity options or FX options. The lag of mean-reversion in the model's volatility dynamics leads to explosive behavior and to a implied distribution that is absolutely not in line with empirics -- especially on longer time horizons.

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$\begingroup$ Actually I was careless in my explanation above. The class that does the calibration allows some extra parameters: ql.HestonSLVMCModel(local_vol, heston_model, generator_factory, end_date, timeStepsPerYear, nBins, calibrationPaths), which by default are 365, 201, and 2**15 respectively.

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I would like to know whether there are some model extensions enabling to partly capture the smile/skew, without switching to a different model, such as Cheyette, SABR or ZABR. volatility-smile short-rate

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2021年9月29日 · Edit: You need to specify the keyword arguments in your second example. The poor calibration of your second example comes from the fact, that you didn't define the keyword arguments in the LNsabr-object before the .fit() function.