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Bayesian estimation of the GARCH(1,1) model with student-t innovations

Ardia, David; Hoogerheide, Lennart F. - 2010

parsimonious and effective GARCH(1,1) model with Student-t innovations. The estimation procedure is fully automatic and thus avoids …

Persistent link: https://www.econbiz.de/10011380176

Efficient Bayesian estimation and combination of GARCH-type models

Ardia, David; Hoogerheide, Lennart F. - 2010 - This version: January 22, 2010

This paper proposes an up-to-date review of estimation strategies available for the Bayesian inference of GARCH … empirical application to S&P index log-returns. Several non-nested GARCH-type models are estimated and combined to predict the …

Persistent link: https://www.econbiz.de/10011380465

Daily exchange rate behaviour and hedging of currency risk

Bos, Charles S.; Mahieu, Ronald J.; Dijk, Herman K. van - 1999

methods. The effects of several model characteristics(unit roots, GARCH, stochastic volatility, heavy tailed …

Persistent link: https://www.econbiz.de/10011302131

Adaptive polar sampling with an application to a Bayes measure of value-at-risk

Bauwens, Luc; Bos, Charles S.; Dijk, Herman K. van - 1999

applied within a Bayesian analysisof a GARCH-mixture model which is used for the evaluation of theValue-at-Risk of the return …

Persistent link: https://www.econbiz.de/10011302625

The stochastic volatility in mean model

Koopman, Siem Jan; Hol Uspensky, Eugenie - 2000

In this paper we present an exact maximum likelihood treatment forthe estimation of a Stochastic Volatility in Mean(SVM) model based on Monte Carlo simulation methods. The SVM modelincorporates the unobserved volatility as anexplanatory variable in the mean equation. The same extension...

Persistent link: https://www.econbiz.de/10011303314

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

Daily exchange rate behaviour and hedging of currency risk

Bos, Charles S.; Mahieu, Ronald J.; Dijk, Herman K. van - 2001

methods. The effects ofseveral modelcharacteristics (unit roots, GARCH, stochastic volatility, heavy taileddisturbance …

Persistent link: https://www.econbiz.de/10011313921

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