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Long memory and level shifts : re-analyzing inflation rates

Bos, Charles S.; Franses, Philip Hans; Ooms, Marius - 1998

A key application of long memory time series models concerns inflation. Long memory implies that shocks have a long-lasting effect. It may however be that empirical evidence for long memory is caused by neglecting one or more level shifts. Since such level shifts are not unlikely for inflation,...

Persistent link: https://www.econbiz.de/10011283465

Long memory modelling of inflation with stochastic variance and structural breaks

Bos, Charles S.; Koopman, Siem Jan; Ooms, Marius - 2007

We investigate changes in the time series characteristics of postwar U.S. inflation. In a model-based analysis the conditional mean of inflation is specified by a long memory autoregressive fractionally integrated moving average process and the conditional variance is modelled by a stochastic...

Persistent link: https://www.econbiz.de/10011373822

Long memory dynamics for multivariate dependence under heavy tails

Janus, Paweł; Koopman, Siem Jan; Lucas, André - 2011

Persistent link: https://www.econbiz.de/10009720703

Monte Carlo maximum likelihood estimation for generalized long-memory time series models

Mesters, G.; Koopman, Siem Jan; Ooms, Marius - 2011

Persistent link: https://www.econbiz.de/10009720782

Fractional integration and fat tails for realized covariance kernels and returns

Lucas, André; Opschoor, Anne - 2016 - This version: September 1, 2016

We introduce a new fractionally integrated model for covariance matrix dynamics based on the long-memory behavior of daily realized covariance matrix kernels and daily return observations. We account for fat tails in both types of data by appropriate distributional assumptions. The covariance...

Persistent link: https://www.econbiz.de/10011531139

Herding, a-synchronous updating and heterogeneity in memory in a CBS

Diks, Cees G. H.; Weide, Roy van der - 2003

predicts MA(1) structure with a negative coeffient. Asynchronous updating leads to an MA(1) model for returns with GARCH($1 …,1$) innovations, and predicts a relation between the ARCH and GARCH coefficients. Heterogeneity in memory leads to long … coefficient and the relation between the ARCH and GARCH coefficients for exchange rate data. …

Persistent link: https://www.econbiz.de/10011334332

Why frequency matters for unit root testing

Boswijk, Herman Peter; Klaassen, Franc - 2004 - Rev.

It is generally believed that for the power of unit root tests, only the time span and not the observation frequency matters. In this paper we show that the observation frequency does matter when the high-frequency data display fat tails and volatility clustering, as is typically the case for...

Persistent link: https://www.econbiz.de/10011342578

Stock index volatility forecasting with high frequency data

Hol Uspensky, Eugenie; Koopman, Siem Jan - 2002

Volatility (SV) and Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models which are both extended to include … outperforms the GARCH model. …

Persistent link: https://www.econbiz.de/10011326944

A new bootstrap test for the validity of a set of marginal models for multiple dependent time series : an application to risk analysis

Ardia, David; Gatarek, Lukasz; Hoogerheide, Lennart F. - 2014

over a small time frame (e.g., a crisis period). We apply our method to test GARCH model specifications for a large panel …

Persistent link: https://www.econbiz.de/10010250513

GARCH models for daily stock returns : impact of estimation frequency on value-at-risk and expected shortfall forecasts

Ardia, David; Hoogerheide, Lennart - 2013

Persistent link: https://www.econbiz.de/10010191413