QuantLib Python Cookbook by Luigi Ballabio and Goutham Balaraman

Author:Luigi Ballabio and Goutham Balaraman [Luigi Ballabio and Goutham Balaraman]
Language: eng
Format: epub
Publisher: leanpub.com
Published: 2016-06-14T00:00:00+00:00

Model Price Market Price Implied Vol Market Vol Rel Error Price Rel Error Vols

0.008775 0.009485 0.106198 0.1148 -0.074854 -0.074928

0.009669 0.010078 0.106292 0.1108 -0.040610 -0.040688

0.008663 0.008716 0.106343 0.1070 -0.006138 -0.006138

0.006490 0.006226 0.106442 0.1021 0.042367 0.042525

0.003542 0.003323 0.106612 0.1000 0.065817 0.066122

Calibrating Volatility With Fixed Reversion

There are times when we need to calibrate with one parameter held fixed. QuantLib allows you to perform calibration with constraints. However, this ability is not exposed in the SWIG wrappers as of version 1.6. I have created a github issue and provided a patch to address this issue. This patch has been merged into QuantLib-SWIG version 1.7. If you are using version lower than 1.7, you will need this patch to execute the following cells. Below, the model is calibrated with a fixed reversion value of 5%.

The following code is similar to the Hull-White calibration, except we initialize the constrained model with given values. In the calibrate method, we provide a list of boolean with constraints [True, False], meaning that the first parameter a is constrained where as the second sigma is not constrained.

In [7]: constrained_model = ql.HullWhite(term_structure, 0.05, 0.001); engine = ql.JamshidianSwaptionEngine(constrained_model) swaptions = create_swaption_helpers(data, index, term_structure, engine) optimization_method = ql.LevenbergMarquardt(1.0e-8,1.0e-8,1.0e-8) end_criteria = ql.EndCriteria(10000, 100, 1e-6, 1e-8, 1e-8) constrained_model.calibrate(swaptions, optimization_method, end_criteria, ql.NoConstraint(), [], [True, False]) a, sigma = constrained_model.params() print("a = %6.5f, sigma = %6.5f" % (a, sigma)) Out[7]: a = 0.05000, sigma = 0.00586 In [8]: calibration_report(swaptions, data) Out[8]: Cumulative Error Price: 0.11584 Cumulative Error Vols : 0.11615 Out[8]:

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.