Browse > Article
http://dx.doi.org/10.5351/CSAM.2016.23.2.163

Stationary bootstrap test for jumps in high-frequency financial asset data  

Hwang, Eunju (Department of Applied Statistics, Gachon University)
Shin, Dong Wan (Department of Statistics, Ewha University)
Publication Information
Communications for Statistical Applications and Methods / v.23, no.2, 2016 , pp. 163-177 More about this Journal
Abstract
We consider a jump diffusion process for high-frequency financial asset data. We apply the stationary bootstrapping to construct a bootstrap test for jumps. First-order asymptotic validity is established for the stationary bootstrapping of the jump ratio test under the null hypothesis of no jump. Consistency of the stationary bootstrap test is proved under the alternative of jumps. A Monte-Carlo experiment shows the advantage of a stationary bootstrapping test over the test based on the normal asymptotic theory. The proposed bootstrap test is applied to construct continuous-jump decomposition of the daily realized variance of the KOSPI for the year 2008 of the world-wide financial crisis.
Keywords
stationary bootstrap; jump diffusion process; ratio test; realized variation; realized bipower variation;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Ait-Sahalia Y (2002). Telling from discrete data whether the underlying continuous-time model is a diffusion, Journal of Finance, 57, 2075-2112.   DOI
2 Ait-Sahalia Y and Jacod J (2009). Testing for jumps in a discretely observed process, Annals of Statistics, 37, 184-222.   DOI
3 Ait-Sahalia Y, Jacod J, and Li J (2012). Testing for jumps in noisy high frequency data, Journal of Econometrics, 168, 207-222.   DOI
4 Andersen TG, Benzoni L and Lund J (2002). An empirical investigation of continuous-time equity return models, The Journal of Finance, 57, 1239-1284.   DOI
5 Barndorff-Nielsen OE and Shephard N (2004). Power and bipower variation with stochastic volatility and jumps, Journal of Financial Econometrics, 2, 1-37.   DOI
6 Barndorff-Nielsen OE and Shephard N (2006). Econometrics of testing for jumps in financial economics using bipower variation, Journal of Financial Econometrics, 4, 1-30.
7 Bollerslev T and Zhou H (2002). Estimating stochastic volatility diffusion using conditional moments of integrated volatility, Journal of Econometrics, 109, 33-65.   DOI
8 Carr P and Wu L (2003). What type of process underlies options? A simple robust test, Journal of Finance, 58, 2581-2610.   DOI
9 Dovonon P, Gonc ̧alves S, Hounyo U, and Meddahi N (2014). Bootstrapping high-frequency jump tests, In Proceedings of International Association for Applied Econometrics (IAAE), London, 1-36.
10 Dovonon P, Goncalves S, and Meddahi N (2013). Bootstrapping realized multivariate volatility measures, Journal of Econometrics, 172, 49-65.   DOI
11 Goncalves S and Meddahi N (2009). Bootstrapping realized volatility, Econometrics, 77, 283-306.   DOI
12 Huang X and Tauchen G (2005). The relative contribution of jumps to total price variance, Journal of Financial Econometrics, 3, 456-499.   DOI
13 Hwang E and Shin DW (2012). Strong consistency of the stationary bootstrap under $\psi$-weak depen-dence, Statistics & Probability Letters, 82, 488-495.   DOI
14 Hwang E and Shin DW (2013a). Stationary bootstrapping realized volatility under market microstructure noise, Electronic Journal of Statistics, 7, 2032-2053.   DOI
15 Hwang E and Shin DW (2013b). Stationary bootstrapping realized volatility, Statistics & Probability Letters, 83, 2045-2051.   DOI
16 Hwang E and Shin DW (2014). A bootstrap test for jumps in financial economics, Economics Letters, 125, 74-78.   DOI
17 Jacod J and Reiss M (2014). A remark on the rates of convergence for integrated volatility estimation in the presence of jumps, Annals of Statistics, 42, 1131-1144.   DOI
18 Jacod J and Todorov V (2014). Efficient estimation of integrated volatility in presence of infinite variation jumps, The Annals of Statistics, 42, 1029-1069.   DOI
19 Lee SS and Mykland PA (2008). Jumps in financial markets: a new nonparametric test and jump dynamics, Review of Financial Studies, 21, 2535-2563.   DOI
20 Jing BY, Liu Z, and Kong XB (2014). On the estimation of integrated volatility with jumps and microstructure noise, Journal of Business & Economic Statistics, 32, 457-467.   DOI
21 Nordman DJ (2009). A note on the stationary bootstrap's variance, Annals of Statistics, 37, 359-370.   DOI
22 Podolskij M and Vetter M (2009). Estimation of volatility functionals in the simultaneous presence of microstructure noise and jumps, Bernoulli, 15, 634-658.   DOI
23 Politis DN and Romano JP (1994). The stationary bootstrap, Journal of the American Statistical Association, 89, 1303-1313.   DOI
24 Shin DW and Hwang E (2013). Stationary bootstrapping for cointegrating regressions, Statistics & Probability Letters, 83, 474-480.   DOI