Dynamics of implied volatility surfaces

Cont, Rama; Fonseca, José Da

doi:10.1088/1469-7688/2/1/304

Cited by 352 publications

(262 citation statements)

References 26 publications

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“…Hence, they are traded less frequently. Cont and da Fonseca (2002) claim that out-of-the-money (OTM) options contain the most information about the IVS. Gonçalves and Guidolin (2006) apply five exclusionary criteria to filter their IVS data.…”

Section: Keeping Extremal IV In the Samplementioning

confidence: 99%

“…For a surface analysis, they only use three "expiry buckets" with 10 to 90, 90 to 180, and 180 to 270 days to expiry. With the same goal, Cont and da Fonseca (2002) apply the Karhunen-Loève decomposition, a PCA method for random surfaces. Fengler et al (2007) combine methods from functional PCA and backfitting techniques for additive models in their dynamic semiparametric factor model (DSFM).…”

Section: Introductionmentioning

confidence: 99%

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

Audrino

Colangelo

2009

Stat Comput

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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.

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Section: Keeping Extremal IV In the Samplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Audrino

Colangelo

2009

Stat Comput

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“…[30] and [8] are early examples of attempts to go beyond static models, but despite the fact that they consider only a cross section of the surface (say for K fixed), the works of Schönbucher [32] and Schweizer and Wissel [34] are more in the spirit of the market model approach which we advocate in this paper.…”

Section: Introduction and Notationmentioning

confidence: 99%

Local volatility dynamic models

Carmona

Nadtochiy

2008

Finance Stoch

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ABSTRACT. This paper is concerned with the characterization of arbitrage free dynamic stochastic models for the equity markets when Itô stochastic differential equations are used to model the dynamics of a set of basic instruments including, but not limited to, the underliers. We study these market models in the framework of the HJM philosophy originally articulated for Treasury bond markets. The approach to dynamic equity models which we follow was originally advocated by Derman and Kani in a rather informal way. The present paper can be viewed as a rigorous development of this program, with explicit formulae, rigorous proofs and numerical examples.

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“…According to Cont and da Fonseca, (2002), due to the inability of an underlying asset model to describe dynamic behavior of option prices or their implied volatilities, it is necessary for each of them to recognize extra sources of randomness specific to the option market and incorporate the statistical features of the their dynamics in the model. Therefore, for any given (K, T), ( )…”

Section: Black-scholes Option Pricing Formula and Implied Volatilitymentioning

confidence: 99%

“…In general, IVS has the shape of smile with the moneyness (ratio of strike price to underlying spot price) and the level of implied volatilities increases or decreases according to the level of the time to maturity. Moreover, according to (Cont and da Fonseca, 2002), implied volatility patterns across moneyness vary less in time than when expressed as a function of the strike price. Since the option prices evaluated by Black-Scholes option pricing model are equal to the real market option prices with implied volatility, modeling the IVS directly becomes a major concern recently.…”

Section: Introductionmentioning

confidence: 99%

Implied Volatility Function Approximation with Korean ELWs (Equity-Linked Warrants) via Gaussian Processes

Han

2014

Management Science and Financial Engineering

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A lot of researches have been conducted to estimate the volatility smile effect shown in the option market. This paper proposes a method to approximate an implied volatility function, given noisy real market option data. To construct an implied volatility function, we use Gaussian Processes (GPs). Their output values are implied volatilities while moneyness values (the ratios of strike price to underlying asset price) and time to maturities are as their input values. To show the performances of our proposed method, we conduct experimental simulations with Korean Equity-Linked Warrant (ELW) market data as well as toy data.

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Dynamics of implied volatility surfaces

Cited by 352 publications

References 26 publications

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

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

Local volatility dynamic models

Implied Volatility Function Approximation with Korean ELWs (Equity-Linked Warrants) via Gaussian Processes

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