Calibration Risk for Exotic Options

Detlefsen, Kai; Härdle, Wolfgang Karl

doi:10.2139/ssrn.2894410

Cited by 20 publications

(28 citation statements)

References 10 publications

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“…For plain vanilla options, the Merton model was arguably one of the best performing models, and Variance Gamma was on par with Black-Scholes and CEV. Thus their poor ability to explain barrier option values again emphasizes the model risk aspect pointed out by Schoutens, Simons & Tisteart (2004), Hirsa, Courtadon & Madan (2002), Detlefsen & Härdle (2007) and numerous other papers: Models may produce very similar prices of plain vanilla options yet differ markedly for exotic options.…”

Section: Barrier Option Valuationmentioning

confidence: 89%

Empirical performance of models for barrier option valuation

Jessen

Poulsen

2013

Quantitative Finance

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In this paper the empirical performance of five different models for barrier option valuation is investigated: the Black-Scholes model, the constant elasticity of variance model, the Heston stochastic volatility model, the Merton jump-diffusion model, and the infinite activity Variance Gamma model. We use time-series data from the USD/EUR exchange rate market: standard put and call (plain vanilla) option prices and a unique set of observed market values of barrier options. The models are calibrated to plain vanilla option prices, and prediction errors at different horizons for plain vanilla and barrier option values are investigated. For plain vanilla options, the Heston and Merton models have similar and superior performance for prediction horizons up to one week. For barrier options, the continuous-path models (Black-Scholes, constant elasticity of variance, and Heston) do almost equally well, while both models with jumps (Merton and Variance Gamma) perform markedly worse.JEL classification: G13 Keywords: Barrier option valuation, empirical performance. * We are deeply indebted to Morten Nalholm for his help cleaning and organizing the barrier option data, and to Fiodar Kilin for his help calibrating the Bates and VG-CIR models. We also thank three referees for valuable comments.

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Section: Barrier Option Valuationmentioning

confidence: 89%

Empirical performance of models for barrier option valuation

Jessen

Poulsen

2013

Quantitative Finance

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“…Hence, we will propose a method which consists of a mixture of matching data and calibration in order to increase the stability of the model parameters. 2 …”

Section: Calibration Of the Heston Model 21 The Heston's Stochastic mentioning

confidence: 99%

“…Table 3 shows the optimal parameter set under the RMSE objective function for the three particular trading days mentioned in Table 2. The calibration risk was defined by Detlefsen and Hardle in [2] as the difference in the value of the calibrated parameters arising from the different specifications of the objective function. Figures 6 and 7 show the maximum absolute value of the difference between the optimal parameter p * ∈ {v for the two calibration procedures.…”

Section: Calibration With Respect To Different Objective Functionsmentioning

confidence: 99%

Use a Reduced Heston or Reduce the Use of Heston?

Guillaume

Schoutens

2010

Wilmott Journal

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This paper features the calibration performance of the Heston model for two different calibration procedures. The first consists of the standard calibration on the whole parameter set and the second one, called reduced calibration consists of a calibration on the reduced parameter set {κ, λ, ρ} where the spot variance v0 and the long run variance η are inferred beforehand from the time series of the VIX volatility index. It is shown that both calibration procedures lead to an accurate fit of the vanilla option surface. Furthermore both the computation time and the calibration risk of the reduced calibration procedure turns out to be significantly lower, which might turn out to be a considerable advantage for practitioners. This paper also features a comparison of the price of different exotic options for the two calibration procedures.

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“…The discrepancy could be defined as relative, absolute, or in the least-square sense differences and expressed in terms of price or implied volatility. Detlefsen and Härdle [8] introduced the concept of calibration risk (or should we say calibration uncertainty) arising from the different (plausible) specifications of the objective function we want to minimize. Later, Guillaume and Schoutens [10] and Guillaume and Schoutens [11] extended the concept of calibration risk to include not only the choice of the functional but also the calibration methodology and illustrated it under the Heston stochastic volatility model.…”

Section: Introductionmentioning

confidence: 99%

Bid-Ask Spread for Exotic Options under Conic Finance

Guillaume

Schoutens

2015

Innovations in Quantitative Risk Management

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This paper puts the concepts of model and calibration risks into the perspective of bid and ask pricing and marketed cash-flows which originate from the conic finance theory. Different asset pricing models calibrated to liquidly traded derivatives by making use of various plausible calibration methodologies lead to different risk-neutral measures which can be seen as the test measures used to assess the (un)acceptability of risks.

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Calibration Risk for Exotic Options

Cited by 20 publications

References 10 publications

Empirical performance of models for barrier option valuation

Empirical performance of models for barrier option valuation

Use a Reduced Heston or Reduce the Use of Heston?

Bid-Ask Spread for Exotic Options under Conic Finance

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