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Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity

Martens, Martin; Dijk, Dick van; Pooter, Michiel de - Tinbergen Instituut - 2004

This discussion paper resulted in a publication in the 'International Journal of Forecasting', 2009, 27, 282-303.<P> The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post volatility. In this paper we develop a nonlinear Autoregressive Fractionally...</p>

Persistent link: https://www.econbiz.de/10011257135

Long Memory and Level Shifts: Re-Analyzing Inflation Rates

Bos, Charles S.; Franses, Philip Hans; Ooms, Marius - Tinbergen Instituut - 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/10011257369

A Fractionally Integrated Wishart Stochastic Volatility Model

Asai, Manabu; McAleer, Michael - Tinbergen Instituut - 2013

There has recently been growing interest in modeling and estimating alternative continuous time multivariate stochastic volatility models. We propose a continuous timefractionally integrated Wishart stochastic volatility (FIWSV) process. We derive the conditional Laplace transform of the FIWSV...

Persistent link: https://www.econbiz.de/10011257492

Forecasting Volatility with Copula-Based Time Series Models

Sokolinskiy, Oleg; Dijk, Dick van - Tinbergen Instituut - 2011

This paper develops a novel approach to modeling and forecasting realized volatility (RV) measures based on copula functions. Copula-based time series models can capture relevant characteristics of volatility such as nonlinear dynamics and long-memory type behavior in a flexible yet parsimonious...

Persistent link: https://www.econbiz.de/10011257654

Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance

Asai, Manabu; McAleer, Michael - Tinbergen Instituut - 2014

Modelling covariance structures is known to suffer from the curse of dimensionality. In order to avoid this problem for forecasting, the authors propose a new factor multivariate stochastic volatility (fMSV) model for realized covariance measures that accommodates asymmetry and long memory....

Persistent link: https://www.econbiz.de/10011272593

Efficient Bayesian Estimation and Combination of GARCH-Type Models

Ardia, David; Hoogerheide, Lennart F. - Tinbergen Instituut - 2010

estimation strategies available for the Bayesian inference of GARCH-type models. The emphasis is put on a novel efficient …-nested GARCH-type models are estimated and combined to predict the distribution of next-day ahead log-returns. …

Persistent link: https://www.econbiz.de/10011255484

Adaptive Polar Sampling with an Application to a Bayes Measure of Value-at-Risk

Bauwens, Luc; Bos, Charles S.; Dijk, Herman K. van - Tinbergen Instituut - 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/10011256462

Daily Exchange Rate Behaviour and Hedging of Currency Risk

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

ofseveral modelcharacteristics (unit roots, GARCH, stochastic volatility, heavy taileddisturbance densities) areinvestigated in …

Persistent link: https://www.econbiz.de/10011256653

Stock Index Returns' Density Prediction using GARCH Models: Frequentist or Bayesian Estimation?

Hoogerheide, Lennart F.; Ardia, David; Corre, Nienke - Tinbergen Instituut - 2011

-known GARCH models for density prediction of daily S&P 500 and Nikkei 225 index returns, a comparison is provided between …

Persistent link: https://www.econbiz.de/10011256766

Herding, A-synchronous Updating and Heterogeneity in Memory in a CBS

Diks, Cees; Weide, Roy van der - Tinbergen Instituut - 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/10011256802