Artículos de revistas
Slope Influence Diagnostics In Conditional Heteroscedastic Time Series Models
Registro en:
Brazilian Journal Of Probability And Statistics. Brazilian Statistical Association, v. 29, n. 1, p. 34 - 52, 2015.
1030752
10.1214/13-BJPS227
2-s2.0-84911416982
Autor
Zevallos M.
Hotta L.K.
Institución
Resumen
In this paper, we provide useful and simple expressions for slope influence diagnostics of several conditional heteroscedastic time series models under innovative model perturbations. These expressions are obtained by establishing a connection between the local influence and residual diagnostics. Monte Carlo experiments provided good results in terms of the size and power of the proposed statistics. To illustrate the results, we analyze the financial time series returns of the S&P500 and DJIA indexes. 29 1 34 52 Abraham, B., Yatawara, N., A score test for detection of time series outliers (1988) Journal of Time Series Analysis, 9, pp. 109-119. , MR0943001 Billor, N., Loynes, R.M., Local influence: A new approach (1993) Communication in Statistics-Theory and Methods, 22, pp. 1595-1611. , MR1224984 Bollerslev, T., Generalized autoregressive conditional heteroskedasticity (1986) Journal of Econometrics, 31, pp. 307-327. , MR0853051 Charles, A., Darné, O., Outliers and GARCH models in financial data (2005) Economic Letters, 86, pp. 347-352. , MR2124418 Cook, R.D., Assessment of local influence (with discussion) (1986) Journal of the Royal Statistical Society B, 48, pp. 133-169. , MR0867994 Ding, Z., Granger, C.W.J., Engle, R.F., A long memory property of stock market returns and a new model (1993) Journal of Empirical Finance, 1, pp. 83-106 Doornik, J.A., Ooms, M., (2005) Outlier detection in GARCH models, , Tinbergen Institute discussion paper TI 2005-092/4, Vrije Universiteit Amsterdam Engle, R., New frontiers for ARCH models (2002) Journal of Applied Econometrics, 17, pp. 425-446 Franses, P.H., van Dijk, D., (1999) Outlier detection in the GARCH(1, 1) model, , Econometric Institute Research Report EI-9926/A, Erasmus Univ. Rotterdam Franses, P.H., van Dijk, D., (2000) Non-Linear Time Series Models in Empirical Finance, , Cambridge, UK: Cambridge Univ. Press Glosten, L., Jagannathan, R., Runkle, D., On the relation between expected value and the volatility of the nominal excess returns on stocks (1993) Journal of Finance, 48, pp. 1779-1801 Hotta, L.K., Tsay, R., Outliers in GARCH Processes (2012) Economic Time Series: Modeling and Seasonality, pp. 337-358. , (W.R. Bell, S.H. Holan and T.S. McElroy, eds.) Boca Raton, FL: Chapman & Hall/CRC Press. MR3076022 Johnson, N.L., Kotz, S., Balakrishnan, N., (1995) Continuous Univariate Distributions, , 2nd ed. New York: Wiley. MR1326603 Leadbetter, M.R., Extremes and local dependence in stationary sequences (1983) Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete, 65, pp. 291-306. , MR0722133 Liu, S., On diagnostics in conditionally heteroskedastic time series models under elliptical distributions (2004) Journal of Applied Probability, 41 A, pp. 393-405. , MR2057589 Nelson, D.B., Conditional heteroskedasticity in asset returns: A new approach (1991) Econometrica, 59, pp. 347-370. , MR1097532 Schwarzmann, B., A connection between local-influence analysis and residual diagnostics (1991) Technometrics, 33, pp. 103-104 Zevallos, M., Santos, B., Hotta, L.K., A note on influential diagnostics in AR(1) time series models (2012) Journal of Statistical Planning and Inference, 142, pp. 2999-3007. , MR2943772 Zevallos, M., Hotta, L.K., Influential observations in GARCH models (2012) Journal of Statistical Computation and Simulation, 82, pp. 1571-1589. , MR2984562 Zhang, X., King, M.L., Influence diagnostics in generalized autoregressive conditional heteroscedasticity processes (2005) Journal of Business & Economic Statistics, 23, pp. 118-129. , MR2108697 Zivot, E., Wang, J., (2006) Modeling Financial Time Series with S-PLUS, , 2nd ed. New York: Springer. MR2000944