The analysis of high-frequency data is diffucult, because of the white noise. In this paper, the estimate of RV on financial assets is studied, especially when high frequency asset prices are available. The analysis is based on a pure jump process for prices. The compound Poisson process (CPP) is introduced to describe the dynamics of price.Additionally, a new assumption is made on noise that generalizes the traditional i.i.d.one, which allows the variance of noise varying across the day. With the CPP model, the path of noise is separated from the observed price and then the noise path is used to improve the traditional sampling scheme. For further improvement, our new sampling scheme is combined with the transaction time sampling scheme and the first-order correction RV. Experiments on the stock price data from both stock exchanges in China demonstrate that although our estimator may produce an outlier occasionally, it can pass the robustness tests in general.
ZHAO Jun-li, LIANG Xun
. An Improvement on the Estimate of Realized Variance of Stock Yield Based on Transaction Time Sampling[J]. Chinese Journal of Management Science, 2015
, 23(7)
: 26
-34
.
DOI: 10.16381/j.cnki.issn1003-207x.2015.07.004
[1] Oomen R C A. Properties of bias corrected realized variance under alternative sampling schemes[J]. Journal of Financial Econometrics, 2005, 3(4):555-557.
[2] Barndorff-Nielsen O E, Hansen P R, Lunde A, et al. Designing realized kernels to measure the ex post variation of equityprices in the presence of noise[J].Econometrica, 2008, 76(6): 1481-1536
[3] Bandi F M,Russell J R. Microstructure noise, realized volatility, and optimal sampling[J]. Review of Economic Studies, 2008, 75(2):339-369.
[4] Oomen R C A. Properties of realized variance under alternative sampling schemes[J].Journal of Business & Economic Statistics, 2006, 24(2):219-237.
[5] Oomen R. Properties of realized variance for a pure jump process: Calendar time sampling versus business time sampling[R]. Working Paper,Financial Econometrics Research Centre,2004.
[6] Hansen P R, Lunde A. Realized variance and market microstructure noise[J]. Journal of Business & Economic Statistics, 2006, 24(2):127-161.
[7] Hansen P R, Lunde A. Realized variance and I.I.D. market microstructure noise[J]. Journal of Business & Economics Statistics,2004, 24(2):208-218.
[8] Hansen P R, Lunde A. An unbiased measure of realized variance[R]. Working Paper, University of A arhus 2004.
[9] Bandi F M, Russell J R. Separating microstructure noise from volatility[J]. Journal of Financial Economics, 2005, 79(3):655-692.
[10] Griffin J E, Oomen R C A. Sampling returns for realized variance calculations: Tick time or transaction time[J]. Econometric Reviews, 2008, 27(1): 230-253.
[11] McAleer M,Medeiros M C. Realized volatility: A review[J]. Econometric Reviews, 2008, 27(1): 10-45.
[12] Gatheral J, Oomen R C A. Zero-intelligence realized variance estimation[J]. Finance Stoch, 2010, 14(2): 249-283.
[13] 沈根祥. 股票收益随机波动模型研究[J].中国管理科学, 2003, 11(2): 17-20.
[14] 张世英, 樊智. 协调理论与波动模型——金融时间序列分析及应用[M].清华大学出版社, 2004.
[15] 王春峰, 蒋祥林, 吴晓霖. 随机波动模型的比较分析[J].系统工程学报, 2005, 20(2):216-219.
[16] 王天一, 黄卓. 基于高频数据的波动率建模及应用研究评述[J]. 经济学动态, 2012, (3): 141-146.
