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|Title:||Assessing the performance of symmetric and asymmetric implied volatility functions||Authors:||Andreou, Panayiotis
Martzoukos, Spiros H.
|Keywords:||Deterministic volatility functions;Implied volatility forecasting;Model selection;Option pricing;Stochastic volatility||Category:||Economics and Business||Field:||Social Sciences||Issue Date:||1-Apr-2014||Publisher:||Springer New York||Source:||Review of Quantitative Finance and Accounting, 2014, Volume 42, Issue 3, Pages 373-397||DOI:||10.1007/s11156-013-0346-z||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.||URI:||http://ktisis.cut.ac.cy/handle/10488/9612||ISSN:||0924865X||Rights:||© 2013 Springer Science+Business Media New York.||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
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