Calibration of stochastic volatility models: A Tikhonov regularization approach

Min Dai, Ling Tang and Xingye Yue

Journal of Economic Dynamics and Control, 2016, vol. 64, issue C, 66-81

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.

Keywords: Calibration; Stochastic volatility model; Tikhonov regularization; Inverse problem (search for similar items in EconPapers)
JEL-codes: G12 G13 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:64:y:2016:i:c:p:66-81

DOI: 10.1016/j.jedc.2016.01.002

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