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Specification Tests of Calibrated Option Pricing Models

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  • Jarrow, Robert
  • Kwok, Simon

Abstract

In spite of the popularity of model calibration in finance, empirical researchers have put more emphasis on model estimation than on the equally important goodness-of-fit problem.This is due partly to the ignorance of modelers, and more to the ability of existing statistical tests to detect specification errors. In practice, models are often calibrated by minimizing a loss function of the differences between the modelled and actual observations. Under this approach, it is challenging to disentangle model error from estimation error in the residual series. To circumvent the difficulty, we study an alternative way of estimating the model by exact calibration. Unlike the error minimization approach, all information about dynamic misspecifications is channeled to the parameter estimation residuals under exact calibration.In the context of option pricing, we illustrate that standard time series tests are powerful in detecting various kinds of dynamic misspecifications. Compared to the error minimization approach, exact calibration yields more reasonable model comparison result, and delivers more accurate hedging performance that is robust to both gradual and abrupt structural shifts of state variables.

Suggested Citation

  • Jarrow, Robert & Kwok, Simon, 2013. "Specification Tests of Calibrated Option Pricing Models," Working Papers 2013-08, University of Sydney, School of Economics, revised Dec 2014.
  • Handle: RePEc:syd:wpaper:2123/9191
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    1. Torben G. Andersen & Nicola Fusari & Viktor Todorov, 2017. "Short-Term Market Risks Implied by Weekly Options," Journal of Finance, American Finance Association, vol. 72(3), pages 1335-1386, June.
    2. Emese Lazar & Shuyuan Qi & Radu Tunaru, 2020. "Measures of Model Risk in Continuous-time Finance Models," Papers 2010.08113, arXiv.org, revised Oct 2020.
    3. Ling, Shiqing & McAleer, Michael & Tong, Howell, 2015. "Frontiers in Time Series and Financial Econometrics: An overview," Journal of Econometrics, Elsevier, vol. 189(2), pages 245-250.
    4. Obydenkova, Svetlana V. & Pearce, Joshua M., 2016. "Technical viability of mobile solar photovoltaic systems for indigenous nomadic communities in northern latitudes," Renewable Energy, Elsevier, vol. 89(C), pages 253-267.
    5. Torben G. Andersen & Nicola Fusari & Viktor Todorov, 2015. "The Pricing of Short-Term market Risk: Evidence from Weekly Options," NBER Working Papers 21491, National Bureau of Economic Research, Inc.
    6. Fabozzi, Frank J. & Paletta, Tommaso & Tunaru, Radu, 2017. "An improved least squares Monte Carlo valuation method based on heteroscedasticity," European Journal of Operational Research, Elsevier, vol. 263(2), pages 698-706.
    7. Ling, S. & McAleer, M.J. & Tong, H., 2015. "Frontiers in Time Series and Financial Econometrics," Econometric Institute Research Papers EI 2015-07, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

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