Implied Volatility Functions: Empirical Tests

Dumas, Bernard; Fleming, Jeff; Whaley, Robert E.

doi:10.3386/w5500

Cited by 177 publications

(314 citation statements)

References 29 publications

Supporting

Mentioning

294

Contrasting

Unclassified

Order By: Relevance

“…However, the pricing improvements are not robust and exhibit biases for the deepest out-of-the-money call options. Deterministic volatility models have also been considered (Dupire, 1994, Rubinstein, 1994, but Dumas et al (1998) reported that the same performance could be achieved by applying an implied volatility smoothing procedure on the Black-Scholes model. Augmenting the underlying price process can produce many different variants of the Black-Scholes model.…”

Section: Introductionmentioning

confidence: 99%

Option Pricing With Modular Neural Networks

Gradojević

Gençay

2007

SSRN Journal

View full text Add to dashboard Cite

This paper applies a non-parametric modular neural network (MNN) model to price the S&P500 European call options. The modules are defined based on time to maturity and moneyness of the options. The option price function of interest is homogenous of degree one with respect to the underlying index price and the strike price. We find that modularity improves the generalization properties of standard feedforward neural network option pricing models (with and without the homogeneity hint), relative to the Black-Scholes model. This improvement is found to be an increasing function of the number of modules used in an MNN.

show abstract

Section: Introductionmentioning

confidence: 99%

Option Pricing With Modular Neural Networks

Gradojević

Gençay

2007

SSRN Journal

View full text Add to dashboard Cite

show abstract

“…Dumas et al (1998) find that estimated parameters are highly unstable over time, allowing only for short time predictions. As Poon and Granger (2003) point out, classical time series-based methods do not perform well in predicting volatility.…”

Section: Introductionmentioning

confidence: 99%

Semi-parametric forecasts of the implied volatility surface using regression trees

Audrino

Colangelo

2009

Stat Comput

View full text Add to dashboard Cite

We present a new semi-parametric model for the prediction of implied volatility surfaces that can be estimated using machine learning algorithms. Given a reasonable starting model, a boosting algorithm based on regression trees sequentially minimizes generalized residuals computed as differences between observed and estimated implied volatilities. To overcome the poor predictive power of existing models, we include a grid in the region of interest, and implement a cross-validation strategy to find an optimal stopping value for the boosting procedure. Back testing the out-of-sample performance on a large data set of implied volatilities from S&P 500 options, we provide empirical evidence of the strong predictive power of our model.

show abstract

“…Furthermore, the countercyclical MPR can be generated from simple microeconomic assumptions about individual agents' preferences. 28 Since this study is the first to introduce the rate of macroeconomic fluctuation, there are no empirical data about the behavior of ρ t . We believe this is an important question deserving a serious empirical analysis.…”

Section: The Sign Of the Hedging Demandmentioning

confidence: 99%