H. Hoek (Henk)
http://repub.eur.nl/ppl/719/
List of Publicationsenhttp://repub.eur.nl/eur_logo_new.png
http://repub.eur.nl/
RePub, Erasmus University RepositoryArbitrage and sampling uncertainty in financial stochastic programming models
http://repub.eur.nl/pub/1589/
Mon, 26 Apr 1999 00:00:01 GMT<div>A.B. Berkelaar</div><div>H. Hoek</div><div>A. Lucas</div>
Asset liability management (ALM) is an important and challenging
problem for institutional investors and financial intermediaries. The
requirement to fulfill its liablilities constrains the institutional
investor in its asset allocation possiblilites. We formulate an ALM
model for pension funds as a multistage stochastic programming model.
Relevant variables such as future inflation rates, stock retruns, and
bond yields are unknown. This is incorporated in the ALM model by
means of an event tree, which represents the expected development of
the economic variables as well as the corresponding uncertainty.
The event tree is constructed by sampling from a time series model
for the variables, and is therefore subject to sampling uncertainty.
Moreover, for the event tree to be realistic, it is required not to
exhibit arbitrage opportunies. In ths paper we examine the effect
of sampling uncertainty and the structure of the event tree on the
optimal policies. Furthermore, we consider the effect of random
sampling and the tree structure on the probability of arbitragefree
trees. We also compare the optimal solutions to the ALM problem for
trees with an without arbitrage. For these purposes, we consider
data from a Dutch pension fund.Testing for Integration using Evolving Trend and Seasonals Models: A Bayesian Approach
http://repub.eur.nl/pub/7799/
Thu, 08 May 1997 00:00:01 GMT<div>G. Koop</div><div>H.K. van Dijk</div><div>H. Hoek</div>
In this paper, we make use of state space models to investigate the presence of stochastic trends in economic time series. A model is specified where such a trend can enter either in the autoregressive representation or in a separate state equation. Tests based on the former are analogous to Dickey-Fuller tests of unit roots, while the latter are analogous to KPSS tests of trend-stationarity. We use Bayesian methods to survey the properties of the likelihood function in such models and to calculate posterior odds ratios comparing models with and without stochastic trends. In addition, we extend these ideas to the problem of testing for integration at seasonal frequencies and show how techniques can be used to carry out Bayesian variants of HEGY test or the Canova-Hansen test.Bayesian Analysis of ARMA Models using Noninformative Priors
http://repub.eur.nl/pub/7822/
Thu, 23 Jan 1997 00:00:01 GMT<div>F.R. Kleibergen</div><div>H. Hoek</div>
Parameters in AutoRegressive Moving Average (ARMA) models are locally nonidentified, due to the problem of root cancellation. Parameters can be constructed which represent this identification problem. We argue that ARMA parameters should be analyzed conditional on these identifying parameters.
Priors exploiting this feature result in regular posteriors, while priors which neglect it result in posteriori favor of nonidentified parameter values. By considering the implicit AR representation of an ARMA model a prior with the desired proporties is obtained. The implicit AR representation also allows to construct easily implemented algorithms to analyse ARMA parameters. As a byproduct, posteriors odds ratios can be computed to compare (nonnested) parsimonious ARMA models. The procedures are applied to two datasets, the (extended) Nelson-Plosser data and monthly observations of US 3-month and 10 year interest rates. For approximately 50% of the series in these two datasets an ARMA model is favored above an AR model.Bayesian analysis of seasonal unit roots and seasonal mean shifts
http://repub.eur.nl/pub/13251/
Wed, 01 Jan 1997 00:00:01 GMT<div>Ph.H.B.F. Franses</div><div>H. Hoek</div><div>R. Paap</div>
In this paper we propose a Bayesian analysis of seasonal unit roots in quarterly observed time series. Seasonal unit root processes are useful to describe economic series with changing seasonal fluctuations. A natural alternative model for similar purposes contains deterministic seasonal mean shifts instead of seasonal stochastic trends. This leads to analysing seasonal unit roots in the presence of mean shifts using Bayesian techniques. Our method is illustrated using several simulated and empirical data.Mean shifts, unit roots and forecasting seasonal time series
http://repub.eur.nl/pub/2102/
Wed, 01 Jan 1997 00:00:01 GMT<div>Ph.H.B.F. Franses</div><div>R. Paap</div><div>H. Hoek</div>
Examples of descriptive models for changing seasonal patterns in economic time series are autoregressive models with seasonal unit roots or with deterministic seasonal mean shifts. In this paper we show through a forecasting comparison for three macroeconomic time series (for which tests indicate the presence of seasonal unit roots) that allowing for possible seasonal mean shifts can improve forecast performance. Next, by means of simulation we demonstrate the impact of imposing an incorrect model on forecasting. We find that an inappropriate decision can deteriorate forecasting performance dramatically in both directions, and hence we recommend the practitioner to take account of seasonal mean shifts when testing for seasonal unit roots.Classical and Bayesian aspects of robust unit root inference
http://repub.eur.nl/pub/11310/
Sun, 01 Jan 1995 00:00:01 GMT<div>H. Hoek</div><div>H.K. van Dijk</div>
This paper has two themes. First, we classify some effects which outliers in the data have on unit root inference. We show that, both in a classical and a Bayesian framework, the presence of additive outliers moves â€˜standardâ€™ inference towards stationarity. Second, we base inference on an independent Student-t instead of a Gaussian likelihood. This yields results that are less sensitive to the presence of outliers. Application to several time series with outliers reveals a negative correlation between the unit root and degrees of freedom parameter of the Student-t distribution. Therefore, imposing normality may incorrectly provide evidence against the unit root.Bayesian Analysis of Seasonal Unit Roots and Seasonal Mean Shifts
http://repub.eur.nl/pub/1354/
Sun, 01 Jan 1995 00:00:01 GMT<div>Ph.H.B.F. Franses</div><div>H. Hoek</div><div>R. Paap</div>
In this paper we propose a Bayesian analysis of seasonal unit roots in quarterly observed time series. Seasonal unit root processes are useful to describe economic series with changing seasonal fluctuations. A natural alternative model for similar purposes contains deterministic seasonal mean shifts instead of seasonal stochastic trends. This leads to analysing seasonal unit roots in the presence of mean shifts using Bayesian techniques. Our method is illustrated using several simulated and empirical data.Bayesian Analysis of ARMA models using Noninformative Priors
http://repub.eur.nl/pub/1363/
Sun, 01 Jan 1995 00:00:01 GMT<div>F.R. Kleibergen</div><div>H. Hoek</div>
Parameters in AutoRegressive Moving Average (ARMA) models are locally nonidentified, due to the problem of root cancellation. Parameters can be constructed which represent this identification problem. We argue that ARMA parameters should be analyzed conditional on these identifying parameters. Priors exploiting this feature result in regular posteriors, while priors which neglect it result in posteriori favor of nonidentified parameter values. By considering the implicit AR representation of an ARMA model a prior with the desired proporties is obtained. The implicit AR representation also allows to construct easily implemented algorithms to analyze ARMA parameters. As a byproduct, posteriors odds ratios can be computed to compare (nonnested) parsimonious ARMA models. The procedures are applied to two datasets, the (extended) Nelson-Plosser data and monthly observations of US 3-month and 10 year interest rates. For approximately 50% of the series in these two datasets an ARMA model is favored above an AR model.