Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/9612
Title: Assessing the performance of symmetric and asymmetric implied volatility functions
Authors: Andreou, Panayiotis 
Charalambous, Chris 
Martzoukos, Spiros H. 
Major Field of Science: Social Sciences
Field Category: Economics and Business
Keywords: Deterministic volatility functions;Implied volatility forecasting;Model selection;Option pricing;Stochastic volatility
Issue Date: 1-Apr-2014
Source: Review of Quantitative Finance and Accounting, 2014, vol. 42, no. 3, pp. 373-397
Volume: 42
Issue: 3
Start page: 373
End page: 397
DOI: 10.1007/s11156-013-0346-z
Journal: Review of Quantitative Finance and Accounting 
Abstract: This study examines several alternative symmetric and asymmetric model specifications of regression-based deterministic volatility models to identify the one that best characterizes the implied volatility functions of S&P 500 Index options in the period 1996-2009. We find that estimating the models with nonlinear least squares, instead of ordinary least squares, always results in lower pricing errors in both in- and out-of-sample comparisons. In-sample, asymmetric models of the moneyness ratio estimated separately on calls and puts provide the overall best performance. However, separating calls from puts violates the put-call-parity and leads to severe model mis-specification problems. Out-of-sample, symmetric models that use the logarithmic transformation of the strike price are the overall best ones. The lowest out-of-sample pricing errors are observed when implied volatility models are estimated consistently to the put-call-parity using the joint data set of out-of-the-money options. The out-of-sample pricing performance of the overall best model is shown to be resilient to extreme market conditions and compares quite favorably with continuous-time option pricing models that admit stochastic volatility and random jump risk factors.
ISSN: 1573-7179
DOI: 10.1007/s11156-013-0346-z
Rights: © Springer Nature
Type: Article
Affiliation : Cyprus University of Technology 
Durham University 
University of Cyprus 
Appears in Collections:Άρθρα/Articles

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