DOI QR코드

DOI QR Code

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

  • 투고 : 2016.01.01
  • 심사 : 2016.02.17
  • 발행 : 2016.03.31

초록

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.

키워드

과제정보

연구 과제 주관 기관 : National Research Foundation of Korea

참고문헌

  1. Ait-Sahalia Y (2002). Telling from discrete data whether the underlying continuous-time model is a diffusion, Journal of Finance, 57, 2075-2112. https://doi.org/10.1111/1540-6261.00489
  2. Ait-Sahalia Y and Jacod J (2009). Testing for jumps in a discretely observed process, Annals of Statistics, 37, 184-222. https://doi.org/10.1214/07-AOS568
  3. Ait-Sahalia Y, Jacod J, and Li J (2012). Testing for jumps in noisy high frequency data, Journal of Econometrics, 168, 207-222. https://doi.org/10.1016/j.jeconom.2011.12.004
  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. https://doi.org/10.1111/1540-6261.00460
  5. Barndorff-Nielsen OE and Shephard N (2004). Power and bipower variation with stochastic volatility and jumps, Journal of Financial Econometrics, 2, 1-37. https://doi.org/10.1093/jjfinec/nbh001
  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. https://doi.org/10.1016/S0304-4076(01)00141-5
  8. Carr P and Wu L (2003). What type of process underlies options? A simple robust test, Journal of Finance, 58, 2581-2610. https://doi.org/10.1046/j.1540-6261.2003.00616.x
  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. https://doi.org/10.1016/j.jeconom.2012.08.003
  11. Goncalves S and Meddahi N (2009). Bootstrapping realized volatility, Econometrics, 77, 283-306. https://doi.org/10.3982/ECTA5971
  12. Huang X and Tauchen G (2005). The relative contribution of jumps to total price variance, Journal of Financial Econometrics, 3, 456-499. https://doi.org/10.1093/jjfinec/nbi025
  13. Hwang E and Shin DW (2012). Strong consistency of the stationary bootstrap under $\psi$-weak depen-dence, Statistics & Probability Letters, 82, 488-495. https://doi.org/10.1016/j.spl.2011.12.001
  14. Hwang E and Shin DW (2013a). Stationary bootstrapping realized volatility under market microstructure noise, Electronic Journal of Statistics, 7, 2032-2053. https://doi.org/10.1214/13-EJS834
  15. Hwang E and Shin DW (2013b). Stationary bootstrapping realized volatility, Statistics & Probability Letters, 83, 2045-2051. https://doi.org/10.1016/j.spl.2013.05.005
  16. Hwang E and Shin DW (2014). A bootstrap test for jumps in financial economics, Economics Letters, 125, 74-78. https://doi.org/10.1016/j.econlet.2014.08.024
  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. https://doi.org/10.1214/13-AOS1179
  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. https://doi.org/10.1214/14-AOS1213
  19. 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. https://doi.org/10.1080/07350015.2014.906350
  20. Lee SS and Mykland PA (2008). Jumps in financial markets: a new nonparametric test and jump dynamics, Review of Financial Studies, 21, 2535-2563. https://doi.org/10.1093/rfs/hhm056
  21. Nordman DJ (2009). A note on the stationary bootstrap's variance, Annals of Statistics, 37, 359-370. https://doi.org/10.1214/07-AOS567
  22. Podolskij M and Vetter M (2009). Estimation of volatility functionals in the simultaneous presence of microstructure noise and jumps, Bernoulli, 15, 634-658. https://doi.org/10.3150/08-BEJ167
  23. Politis DN and Romano JP (1994). The stationary bootstrap, Journal of the American Statistical Association, 89, 1303-1313. https://doi.org/10.1080/01621459.1994.10476870
  24. Shin DW and Hwang E (2013). Stationary bootstrapping for cointegrating regressions, Statistics & Probability Letters, 83, 474-480. https://doi.org/10.1016/j.spl.2012.10.007