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Autor
Zhang Xuze (Department of Mathematics and Institute for Systems Research, University of Maryland, College Park, USA), Kedem Benjamin (Department of Mathematics and Institute for Systems Research, University of Maryland, College Park. USA)
Tytuł
Extended Residual Coherence with a Financial Application
Źródło
Statistics in Transition, 2021, vol. 22, nr 2, s. 1 - 14, tab., wykr., bibliogr. 18 poz.
Słowa kluczowe
Finanse, Szeregi czasowe, Zmienność, Rynki finansowe, Analiza rynku
Finance, Time-series, Variability, Financial markets, Market analysis
Uwagi
summ.
Abstrakt
Residual coherence is a graphical tool for selecting potential second-order interaction terms as functions of a single time series and its lags. This paper extends the notion of residual coherence to account for interaction terms of multiple time series. Moreover, an alternative criterion, integrated spectrum, is proposed to facilitate this graphical selection. A financial market application shows that new insights can be gained regarding implied market volatility.(original abstract)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka SGH im. Profesora Andrzeja Grodka
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Pełny tekst
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Bibliografia
Pokaż
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  2. Brillinger, D. R., Rosenblatt, M., (1967). Asymptotic theory of estimates of kth-order spectra. Proceedings of the National Academy of Sciences of the United States of America, 57(2), p. 206.
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  5. Hinich, M. J., (1979). Estimating the lag structure of a nonlinear time series model. Journal of the American Statistical Association, 74 (366a), pp. 449-452.
  6. Kedem, B., (2016). Coherence consideration in binary time series analysis. Handbook of Discrete-Valued Time Series, p. 311.
  7. Kedem, B., Fokianos, K., (2002). Regression models for time series analysis. Wiley, Hoboken.
  8. Kedem-Kimelfeld, B., (1975). Estimating the lags of lag processes. Journal of the American Statistical Association, 70(351a), pp. 603-605.
  9. Khan, D., Katzoff, M., Kedem, B., (2014). Coherence structure and its application in mortality forecasting. Journal of Statistical Theory and Practice, 8(4), pp. 578-590.
  10. Kimelfeld, B., (1974). Estimating the kernels of nonlinear orthogonal polynomial functionals. The Annals of Statistics, pp. 353-358.
  11. Koopmans, L. H., (1974). The spectral analysis of time series. New Yorkn NY: Academic Press.
  12. Maharaj, E. A., D'Urso, P., (2010). A coherence-based approach for the pattern recognition of time series. Physica A: Statistical mechanics and its Applications, 389(17), pp. 3516-3537.
  13. Nikias, C. L., Mendel, J. M., (1993). Signal processing with higher-order spectra. IEEE Signal processing magazine, 10(3), pp. 10-37.
  14. Sanaullah, M., (2013). A review of higher order statistics and spectra in communication systems. Global Journal of Science Frontier Research, Physics and Space Science, 13(4).
  15. Sun, F. T., Miller, L. M., D'Esposito, M., (2004). Measuring interregional functional connectivity using coherence and partial coherence analyses of fmri data. Neuroimage, 21(2), pp. 647-658.
  16. Tick, L. J., (1961). The estimation of "transfer functions" of quadratic systems. Technometrics, 3(4), pp. 563-567.
  17. Whaley, R. E., (1993). Derivatives on market volatility: Hedging tools long overdue. The journal of Derivatives, 1(1), pp. 71-84.
  18. Wiener, N., (1958). Nonlinear problems in random theory. M.I.T. Press, Cambridge.
Cytowane przez
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ISSN
1234-7655
Język
eng
URI / DOI
http://dx.doi.org/0.21307/stattrans-2021-014
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