Stochastic Volatility Models: Faking a Smile

Dean Diavatopoulos and Oleg Sokolinskiy

Chapter 33 in Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning:(In 4 Volumes), 2020, pp 1271-1293 from World Scientific Publishing Co. Pte. Ltd.

Abstract: Stochastic volatility models of option prices treat variance as a variable. However, the application of such models requires calibration to market prices that often treats variance as an optimized parameter. If variance represents a variable, option pricing models should reflect measure-invariant features of its historic evolution. Alternatively, if variance is a parameter used to generate desired features of the implied volatility surface, stochastic volatility models lose their connection to the historic evolution of variance. This chapter obtains evidence that variance in stochastic volatility models is an artificial construct used to confer desired properties to the generated implied volatility surface.

Keywords: Financial Econometrics; Financial Mathematics; Financial Statistics; Financial Technology; Machine Learning; Covariance Regression; Cluster Effect; Option Bound; Dynamic Capital Budgeting; Big Data (search for similar items in EconPapers)
JEL-codes: C01 C1 G32 (search for similar items in EconPapers)
Date: 2020
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