Testing Option Pricing Models

Bates, David S.

doi:10.3386/w5129

Cited by 104 publications

(109 citation statements)

References 141 publications

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“…Brandt and Wu (2002) and Hull and Suo (2002), on the other hand, investigate the usefulness of the PBS model fitted to standard European options in-sample for pricing other options out-ofsample. 5 Possibly the most interesting exercise is to evaluate the different loss functions one day out-of-sample. Consider the second row of pictures in Figure 2.A.…”

Section: Empirical Loss Estimatesmentioning

confidence: 99%

The importance of the loss function in option valuation

Christoffersen

Jacobs

2004

Journal of Financial Economics

328

211

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Section: Empirical Loss Estimatesmentioning

confidence: 99%

The importance of the loss function in option valuation

Christoffersen

Jacobs

2004

Journal of Financial Economics

328

211

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“…By reproducing the smile e ect, a more complex speci cation should lower the pricing errors. Earlier comparative studies 4 document that the errors of the BlackScholes model can indeed be reduced if the correct speci cations for the stochastic process of the underlying instrument are used. Up to now, GARCH and SV have not been compared directly from an option pricing point of view.…”

Section: Introductionmentioning

confidence: 99%

GARCH vs. stochastic volatility: Option pricing and risk management

Lehar

Scheicher²,

Schittenkopf

2002

Journal of Banking & Finance

103

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This paper examines the out-of-sample performance of two common extensions of the Black-Scholes framework, namely a GARCH and a stochastic volatility option pricing model. The models are calibrated to intraday FTSE 100 option prices. We apply two sets of performance criteria, namely out-of-sample valuation errors and Value-at-Risk oriented measures. When we analyze the t to observed prices, GARCH clearly dominates both stochastic volatility and the benchmark Black S c holes model. However, the predictions of the market risk from hypothetical derivative positions show sizable errors. The t to the realized pro ts and losses is poor and there are no notable di erences between the models. Overall we therefore observe that the more complex option pricing models can improve on the Black S c holes methodology only for the purpose of pricing, but not for the Value-at-Risk forecasts.

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“…Bates (1996aBates ( , 1996b points out that the two stochastic processes seem to be incompatible. Moreover, the time was ready to compare the physical distribution to the option-implied risk-neutral distribution.…”

Section: The Initial Studiesmentioning

confidence: 99%