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Czasopismo
2015 | 11 | nr 1 | 32--43
Tytuł artykułu

Selected Techniques of Detecting Structural Breaks in Financial Volatility

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We investigate several promising algorithms, proposed in literature, devised to detect sudden changes (structural breaks) in the volatility of financial time series. Comparative study of three techniques: ICSS, NPCPM and Cheng's algorithm is carried out via numerical simulation in the case of simulated T-GARCH models and two real series, namely German and US stock indices. Simulations show that the NPCPM algorithm is superior to ICSS because is not over-sensitive either to heavy tails of market returns or to their serial dependence. Some signals generated by ICSS are falsely classified as structural breaks in volatility, while Cheng's technique works well only when a single break occurs. (original abstract)
Czasopismo
Rocznik
Tom
11
Numer
Strony
32--43
Opis fizyczny
Twórcy
  • Cracow University of Technology
Bibliografia
  • Aggarwal, R., Inclan, C., Leal, R. (1999). Volatility in Emerging Stock Markets. Journal of Financial and Quantitative Analysis 34, 33-55.
  • Andreou, E., Ghysels, E. (2002). Detecting Multiple Breaks in Financial Market Volatility Dynamics. Journal of Applied Econometrics 17, 579-600.
  • Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics 37, 307327.
  • Cheng, T. L. (2009). An Efficient Algorithm for Estimating a Change-point. Statistics and Probability Letters 79, 559-565.
  • Covarrubias, G., Ewing, B. T., Hein, S. E., Thompson, M. A. (2006). Modeling Volatility Changes in the 10-year Treasury. Physica A 369, 737-744.
  • Eckley, I. A., Killick, R., Evans, K., Jonathan, P. (2010). Detection of Changes in Variance of Oceanographic Time-series Using Changepoint Analysis. Ocean Engineering 37, 1120-1126.
  • Inclan, C., Tiao, G. C. (1994). Use of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance. Journal of the American Statistical Association 89, 913-923.
  • Kang, S. H., Cho, H. G., Yoon, S. N. (2009). Modeling Sudden Volatility Changes: Evidence from Japanese and Korean Stock Markets. Physica A 388, 3543-3550.
  • Kokoszka, P., Leipus, R. (2000). Change-point Estimation in ARCH Models. Bernoulli 6 (3), 513-539.
  • Mood, A. M. (1954). On the Asymptotic Efficiency of Certain Nonparametric Two-sample Tests. Annals of Mathematical Statistics 25(3), 514- 522.
  • Rapach, D., Strauss, J. K. (2008). Structural Breaks and GARCH Models of Exchange Rate Volatility. Journal of Applied Econometrics 23, 65-90.
  • Ross, G. J. (2013). Modeling Financial Volatility in the Presence of Abrupt Changes. Physica A. Statistical Mechanics and its Applications 192(2), 350-360.
  • Sansó, A., Aragó, V., Carrion-i- Silvestre, J. L. (2004). Testing for Changes in the Unconditional Variance of Financial Time Series. Revista de Economia Financiera 4, 32-53.
  • Xu, K.-L. (2013). Powerful Tests for Structural Changes in Volatility. Journal of Econometrics 173 (1), 126-142.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171386839

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