Subset vector autoregressions for listed property and oil markets using bootstrap model selection
type: CreativeWork
description: Subset Vector Autoregressive (SVAR) models are fitted to the International Listed Property Trust (LPT) market and the global oil market. A General-to-Specific (GetS) model selection algorithm and a Bootstrap model based resampling method are employed to determine the best fitting models from a set of candidate models. Section One presents one of the largest studies to date of the effect of crises on diversification opportunities in the listed property context, spanning 12 markets{u2091}. The analysis demonstrates an application of alternative superior modelling of market integration. Much early research on the diversification benefits of securitised real estate markets uses correlations and/or a simple mean variance framework. These static descriptive statistics, while informative, cannot adequately capture the dynamic behaviour present in the data or information on how the two variables are related on a lead/lag basis. Autocorrelation analysis can give some insight into temporal relationships between the response and the covariates now and into the past, but the more complex (SVAR) model allows us to capture the behaviour of the data series more flexibly and, in particular, model all markets of interest simultaneously. Such an approach captures not only internal dependence, but also complex dependence structures involving multiple markets. This study covers the Asian market crisis and the current global credit crisis. Critically this study includes modelling of the potential for currency effects to impact the diversification environment. Diversification benefits evaporate during the crisis in both hedged and unhedged cases, perhaps a surprising result given the magnitude of the currency effects experienced during the Asian crisis. Interestingly, although diversification benefits vanish during the crisis in both hedged and unhedged cases, the markets that are significant in the model differ between the two cases. While the analysis in Section One demonstrates that S…
abstract: Subset Vector Autoregressive (SVAR) models are fitted to the International Listed Property Trust (LPT) market and the global oil market. A General-to-Specific (GetS) model selection algorithm and a Bootstrap model based resampling method are employed to determine the best fitting models from a set of candidate models. Section One presents one of the largest studies to date of the effect of crises on diversification opportunities in the listed property context, spanning 12 markets{u2091}. The analysis demonstrates an application of alternative superior modelling of market integration. Much early research on the diversification benefits of securitised real estate markets uses correlations and/or a simple mean variance framework. These static descriptive statistics, while informative, cannot adequately capture the dynamic behaviour present in the data or information on how the two variables are related on a lead/lag basis. Autocorrelation analysis can give some insight into temporal relationships between the response and the covariates now and into the past, but the more complex (SVAR) model allows us to capture the behaviour of the data series more flexibly and, in particular, model all markets of interest simultaneously. Such an approach captures not only internal dependence, but also complex dependence structures involving multiple markets. This study covers the Asian market crisis and the current global credit crisis. Critically this study includes modelling of the potential for currency effects to impact the diversification environment. Diversification benefits evaporate during the crisis in both hedged and unhedged cases, perhaps a surprising result given the magnitude of the currency effects experienced during the Asian crisis. Interestingly, although diversification benefits vanish during the crisis in both hedged and unhedged cases, the markets that are significant in the model differ between the two cases. While the analysis in Section One demonstrates that S…
author: Ryan, Laura Simone
language: en_AU
date published: 2011
date modified: 2026-07-24T01:07:35Z
provider: Australian Catholic University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Australian Catholic University

PropertyValue
property ID: Handle
value: http://hdl.handle.net/1885/151229