Abstract
The separating information maximum likelihood (SIML) method was proposed by Kunitomo and Sato (Separating information maximum likelihood estimation of realized volatility and covariance with micro-market noise, 2008 [12]; Math Comput Simul 8:1272–1289, 2011 [13]; N Am J Econ Financ 26:282–309, 2013 [14]) for estimating integrated volatility and covariance using high-frequency data with market microstructure noise. The SIML estimator has reasonable asymptotic properties and finite sample properties even with irregular, non-synchronized, and noisy data, as demonstrated by means of asymptotic analysis and massive Monte Carlo simulations (Kunitomo et al. in Asia-Pac Financ Markets 22(3):333–368, 2015 [11]; Misaki and Kunitomo in Int Rev Econ Financ 40:265–281, 2015 [19]). Misaki (An empirical analysis of volatility by the SIML estimation with high-frequency trades and quotes. Springer, Cham, pp. 66–75 [18]) conducted an empirical study on volatility by employing SIML estimation with data of actually traded individual stocks. In the present study, we analyze multivariate high-frequency financial data to examine usefulness of the SIML method for estimating integrated covariance, correlation, and hedging ratio. Additionally, we test the efficiency of hedging by comparing the performances of simple portfolios constructed based on estimated hedging ratios. Our findings suggest that SIML estimation is useful for analyzing multivariate high-frequency data from actual markets as well as univariate cases.
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Notes
- 1.
OSE is now integrated under the Japan Exchange Group, Inc.
- 2.
The transformation is based on the spectrum decomposition. See [15] for details.
- 3.
The numbers in parentheses denote the security code.
- 4.
We only show the leading four firms because of space limitations.
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Acknowledgements
The author thanks two anonymous reviewers for useful comments and recommendations that improved this manuscript. This research is supported by Grant for Social Science from Nomura Foundation.
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Misaki, H. (2020). Practical Application of the SIML Estimation of Covariance, Correlation, and Hedging Ratio with High-Frequency Financial Data. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2019. Smart Innovation, Systems and Technologies, vol 142. Springer, Singapore. https://doi.org/10.1007/978-981-13-8311-3_5
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