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An Empirical Analysis of Nikkei 225 Options Using Realized GARCH Models

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  • Takeuchi-Nogimori, Asuka

Abstract

This paper analyses whether realized generalized autoregressive conditional heteroscedasticity （GARCH）models are useful for pricing Nikkei 225 options. This model enables us to estimate simultaneously the dynamics of stock returns using both realized volatility（RV）and daily return data. The analysis also examines whether realized GARCH models using realized kernels（RK）and realized ranges（RR）improve the option-pricing performance. Comparing the empirical results, for call options, EGARCH models perform better ; however, for put options, realized GARCH models with RK without nontrading hour returns perform better than those with RV or RR.

Suggested Citation

  • Takeuchi-Nogimori, Asuka, 2017. "An Empirical Analysis of Nikkei 225 Options Using Realized GARCH Models," Economic Review, Hitotsubashi University, vol. 68(2), pages 97-113, April.
    • Handle: RePEc:hit:ecorev:v:68:y:2017:i:2:p:97-113
    DOI: 10.15057/28531
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    References listed on IDEAS

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    1. Takahashi, Makoto & Omori, Yasuhiro & Watanabe, Toshiaki, 2009. "Estimating stochastic volatility models using daily returns and realized volatility simultaneously," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2404-2426, April.
    2. Siem Jan Koopman & Marcel Scharth, 2012. "The Analysis of Stochastic Volatility in the Presence of Daily Realized Measures," Journal of Financial Econometrics, Oxford University Press, vol. 11(1), pages 76-115, December.
    3. Lars Stentoft, 2008. "Option Pricing using Realized Volatility," CREATES Research Papers 2008-13, Department of Economics and Business Economics, Aarhus University.
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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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