Asset Selection Model Based on the VaR Adjusted High-Frequency Sharp Index
Abstract
This paper uses high frequency intraday data to construct the VaR adjusted high-frequency sharp index model as an efficient method of asset selection and portfolio strategy in optimal portfolio problem. Both asset selection and perfect weight allocation are key processing. This paper constructs the VaR adjusted high-frequency sharp index to choose stocks, and uses several portfolio strategies to allocate stock weight. Through market data of shanghai stock exchange as out-of-sample empirical, we find that VaR adjusted high-frequency sharp index model can have a better result than high-frequency sharp index model and momentum stock choice model, and portfolio strategies based on the VaR adjusted high-frequency sharp index model have a higher risky return.
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Andersen, T., Bollerslev, T., & Diebold, F. X. (2001). The distribution of realized stock return volatility. Journal of Economics, 61, 43-76.
Bandi, F. M., & Russell, J. R. (2006). Separating microstructure noise from volatility. Journal of Financial Economics, 79(3), 655-692.
Best, M. J., & Grauer, R. R. (1991). On the sensitivity of mean-variance- efficient portfolio to changes in asset means: some analytical and computational results. Review of Financial Studies, 4, 315-342.
Chun, W. D., Chen W., & Pan P. (2013). A study on dynamic VaR predicting models for oil futures market of Shanghai. Chinese Journal of Management Science, 21(2), 36-45.
DeMiguel, V., Garlappi, L., & Uppal, R. (2009). Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy? Review of Financial Studies, 22, 1915-1953.
Efron, B., Hastie, T., & Johnstone, I., et al. (2004). Least angle regression (with discussions). The Annals of Statistics, 32, 409-499.
Guo, M. Y., & Zhang, S. Y. (2006). Weighted realized volatility and its long memory and optimal frequency. Journal of Systems Engineering, 21(6), 568-573.
Kan, R., & Zhou, G. (2007). Optimal portfolio choice with parameter uncertainty. Journal of Financial and Quantitative Analysis, 42, 621-656.
Kourtis, A., Dotsis, G., & Markellos, R. N. (2012). Parameter uncertainty in portfolio selection: Shrinking the inverse covariance matrix. Journal of Banking and Finance, 36, 2522-2531.
Ma, F., Wei, Y., & Huang, D. S. (2015). Measurement of dynamic stocks portfolio VaR and its forecasting model based on vine copula. Systems Engineering Theory & Practice, 25(1), 26-36.
Markowitz, H. M. (1952). Portfolio selection. Journal of Finance. 7, 77-91.
Xu, Z. G., & Zhang, S. Y. (2004). The comparative research on volatility prediction ability of adjusted realized volatility, GARCH model and SV model. Systems Engineering, 22(8), 60-63.
Zhang, L., Mykland, P. A., & Yacine, A. (2005). A tale of two time scales: Determining integrated volatility with noisy high-frequency data. Journal of the American Statistical Association, 100(472), 1394-1411.
DOI: http://dx.doi.org/10.3968/n
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