Calibration of stochastic volatility models: A Tikhonov regularization approach

Calibration of stochastic volatility models: A Tikhonov regularization approach We aim to calibrate stochastic volatility models from option prices. We develop a Tikhonov regularization approach with an efficient numerical algorithm to recover the risk neutral drift term of the volatility (or variance) process. In contrast to most existing literature, we do not assume that the drift term has any special structure. As such, our algorithm applies to calibration of general stochastic volatility models. An extensive numerical analysis is presented to demonstrate the efficiency of our approach. Interestingly, our empirical study reveals that the risk neutral variance processes recovered from market prices of options on S&P 500 index and EUR/USD exchange rate are indeed linearly mean-reverting. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Economic Dynamics and Control Elsevier

Calibration of stochastic volatility models: A Tikhonov regularization approach

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Publisher
Elsevier
Copyright
Copyright © 2016 Elsevier B.V.
ISSN
0165-1889
eISSN
1879-1743
D.O.I.
10.1016/j.jedc.2016.01.002
Publisher site
See Article on Publisher Site

Abstract

We aim to calibrate stochastic volatility models from option prices. We develop a Tikhonov regularization approach with an efficient numerical algorithm to recover the risk neutral drift term of the volatility (or variance) process. In contrast to most existing literature, we do not assume that the drift term has any special structure. As such, our algorithm applies to calibration of general stochastic volatility models. An extensive numerical analysis is presented to demonstrate the efficiency of our approach. Interestingly, our empirical study reveals that the risk neutral variance processes recovered from market prices of options on S&P 500 index and EUR/USD exchange rate are indeed linearly mean-reverting.

Journal

Journal of Economic Dynamics and ControlElsevier

Published: Mar 1, 2016

References

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