Abstract
In the recent finance literatures, the model-free volatility measures feature prominently in analyzing the properties of equity market volatility. One strand focuses on the model-free realized volatility calculated by summing intraday high-frequency returns over short time intervals. The volatility constructed in this way is an unbiased and highly efficient estimator. This approach has been popularized by Andersen, Bollerslev, Diebold (Some like it smooth, and some like it rough: Untangling continuous and jump components in measuring, modeling, and forecasting asset return volatility, working Paper, Department of Economics, University of Pennsylvania, 2003a), Andersen et al. (J Financ Econ, 61, 43–76, 2001a), Andersen et al. (J Am Stat Assoc, 96, 42–55, 2001b), Andersen et al. (Multinatl Financ J, 4, 159–179, 2001c), Andersen et al. (Econometrica, 72, 579–625, 2003b), Areal, Taylor (J Futur Mark, 22, 627–648, 2002), Barndorff-Nielsen, Shephard (J R Stat Soc Ser B, 64, 2002), Barndorff-Nielsen, Shephard (Econometrica, 72, 885–925, 2004a), and Ebens (Realized stock volatility, working Paper, Johns Hopkins University, 1999). In parallel to the realized volatility measure, another strand of literature looks at the model-free implied volatility from option prices proposed by Britten-Jones, Neuberger (J Financ, 55, 839–866, 2000), Jiang, Tian (Rev Financ Stud, 18, 1305–1342, 2005), and Lynch, Panigirtzoglou (Option implied and realized measures of variance, working Paper, Bank of England, 2003), which provides a risk-neutral expectation of future volatilities. Contrary to the option implied volatility based on the Black-Scholes model, the model-free implied volatility computed from option prices does not rely on any particular option pricing models.
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Notes
- 1.
Andersen et al. (2003b) show a positive skewness only when they consider DM/$ as the underlying asset.
- 2.
In the numerical experiment in Jiang and Tian (2005), the approximation errors due to discretization and truncation are negligible when m and k increase to 40 and 0.4, respectively.
- 3.
Under the i.i.d assumption, the annualized skewness and excess kurtosis can be obtained by dividing the quarterly skewness and excess kurtosis by \(\sqrt{4}\) and 4, respectively.
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© 2018 Xiamen University Press and Springer Nature Singapore Pte Ltd.
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Chen, J. (2018). Risk Aversion Estimated from Volatility Spread. In: General Equilibrium Option Pricing Method: Theoretical and Empirical Study. Springer, Singapore. https://doi.org/10.1007/978-981-10-7428-8_7
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DOI: https://doi.org/10.1007/978-981-10-7428-8_7
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