A powerful test for conditional heteroscedasticity for financial time series with highly persistent volatilities.Reportar como inadecuado




A powerful test for conditional heteroscedasticity for financial time series with highly persistent volatilities. - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

Editor: Universidad Carlos III de Madrid

Issued date: 2003-11

Serie-No.: UC3M Working Papers. Statistics and Econometrics2003-16

Other version: http:-hdl.handle.net-10016-4910

Abstract:Traditional tests for conditional heteroscedasticity are based on testing for significant autocorrelations of squared or absolute observations. In the context of high frequency time series of financial returns, these autocorrelations are often positive and verTraditional tests for conditional heteroscedasticity are based on testing for significant autocorrelations of squared or absolute observations. In the context of high frequency time series of financial returns, these autocorrelations are often positive and very persistent, although their magnitude is usually very small. Moreover, the sample autocorrelations are severely biased towards zero, specially if the volatility is highly persistent. Consequently, the power of the traditional tests is often very low. In this paper, we propose a new test that takes into account not only the magnitude of the sample autocorrelations but also possible patterns among them. This aditional information makes the test more powerful in situations of empirical interest. The asymptotic distribution of the new statistic is derived and its finite sample properties are analized by means of Monte Carlo experiments. The performance of the new test is compared with other alternative tests. Finally, we illustrate the results analysing several real time series of financial returns.+-





Autor: Rodríguez, Julio; Ruiz, Esther

Fuente: http://e-archivo.uc3m.es


Introducción



Universidad Carlos III de Madrid Repositorio institucional e-Archivo http:--e-archivo.uc3m.es Departamento de Estadística DES - Working Papers.
Statistics and Econometrics.
WS 2003-11 A powerful test for conditional heteroscedasticity for financial time series with highly persistent volatilities. Rodríguez, Julio http:--hdl.handle.net-10016-204 Descargado de e-Archivo, repositorio institucional de la Universidad Carlos III de Madrid Working Paper 03-67 Statistics and Econometrics Series 16 November 2003 Departamento de Estadística y Econometría Universidad Carlos III de Madrid Calle Madrid, 126 28903 Getafe (Spain) Fax (34) 91 624-98-49 A POWERFUL TEST FOR CONDITIONAL HETEROSCEDASTICITY FOR FINANCIAL TIME SERIES WITH HIGHLY PERSISTENT VOLATILITIES. Julio Rodríguez and Esther Ruiz* Abstract Traditional tests for conditional heteroscedasticity are based on testing for significant autocorrelations of squared or absolute observations.
In the context of high frequency time series of financial returns, these autocorrelations are often positive and very persistent, although their magnitude is usually very small.
Moreover, the sample autocorrelations are severely biased towards zero, specially if the volatility is highly persistent.
Consequently, the power of the traditional tests is often very low.
In this paper, we propose a new test that takes into account not only the magnitude of the sample autocorrelations but also possible patterns among them.
This aditional information makes the test more powerful in situations of empirical interest.
The asymptotic distribution of the new statistic is derived and its finite sample properties are analized by means of Monte Carlo experiments.
The performance of the new test is compared with other alternative tests.
Finally, we illustrate the results analysing several real time series of financial returns. Keywords: Autocorrelations of non-linear transformations; GARCH; Long-memory; McLeod-Li statistic; Stochastic volati...





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