ORCID Profile
0000-0002-4232-0323
Current Organisations
Queensland University of Technology (QUT)
,
Queensland University of Technology
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Time-Series Analysis | Economic Models And Forecasting | Investment and Risk Management | Econometrics | Banking, Finance and Investment | Financial Econometrics | Applied Economics Not Elsewhere Classified
Microeconomic issues not elsewhere classified | Electricity services and utilities | Investment Services (excl. Superannuation) | Electricity transmission |
Publisher: Walter de Gruyter GmbH
Date: 10-01-2011
Publisher: Elsevier BV
Date: 2008
Publisher: Elsevier BV
Date: 10-2018
Publisher: Springer Singapore
Date: 2021
Publisher: Wiley
Date: 26-01-2020
DOI: 10.1002/FOR.2647
Abstract: The ability to improve out‐of‐s le forecasting performance by combining forecasts is well established in the literature. This paper advances this literature in the area of multivariate volatility forecasts by developing two combination weighting schemes that exploit volatility persistence to emphasise certain losses within the combination estimation period. A comprehensive empirical analysis of the out‐of‐s le forecast performance across varying dimensions, loss functions, sub‐s les and forecast horizons show that new approaches significantly outperform their counterparts in terms of statistical accuracy. Within the financial applications considered, significant benefits from combination forecasts relative to the in idual candidate models are observed. Although the more sophisticated combination approaches consistently rank higher relative to the equally weighted approach, their performance is statistically indistinguishable given the relatively low power of these loss functions. Finally, within the applications, further analysis highlights how combination forecasts dramatically reduce the variability in the parameter of interest, namely the portfolio weight or beta.
Publisher: Elsevier BV
Date: 09-2016
Publisher: Elsevier BV
Date: 2023
DOI: 10.2139/SSRN.4404527
Publisher: Informa UK Limited
Date: 04-2008
Publisher: Elsevier BV
Date: 06-2016
Publisher: Elsevier BV
Date: 09-2015
Publisher: Elsevier BV
Date: 06-2009
Publisher: Informa UK Limited
Date: 12-2003
Publisher: Elsevier BV
Date: 03-2018
Publisher: Springer Science and Business Media LLC
Date: 29-11-2018
Publisher: Elsevier BV
Date: 09-2017
Publisher: Wiley
Date: 09-09-2023
DOI: 10.1002/FOR.3029
Publisher: Elsevier BV
Date: 10-2016
Publisher: Elsevier BV
Date: 2011
DOI: 10.2139/SSRN.1917208
Publisher: Elsevier BV
Date: 07-2015
Publisher: MDPI AG
Date: 17-02-2018
Publisher: Elsevier BV
Date: 09-2021
Publisher: Springer Science and Business Media LLC
Date: 13-06-2019
Publisher: Scientific Research Publishing, Inc.
Date: 2013
Publisher: Elsevier BV
Date: 04-2020
Publisher: Elsevier BV
Date: 06-2019
Publisher: Elsevier BV
Date: 10-2018
Publisher: Elsevier BV
Date: 08-2020
Publisher: Elsevier BV
Date: 12-2021
Publisher: International Association for Energy Economics (IAEE)
Date: 10-2017
Publisher: Elsevier BV
Date: 05-2023
Publisher: Elsevier BV
Date: 02-2023
Publisher: Springer Science and Business Media LLC
Date: 13-07-2020
Publisher: Elsevier BV
Date: 12-2017
Publisher: Elsevier BV
Date: 12-2007
Publisher: Elsevier BV
Date: 06-2015
Publisher: Elsevier BV
Date: 2019
DOI: 10.2139/SSRN.3369484
Publisher: AIP Publishing LLC
Date: 2015
DOI: 10.1063/1.4937113
Publisher: Elsevier BV
Date: 07-2017
Publisher: Elsevier BV
Date: 06-2019
Publisher: Oxford University Press (OUP)
Date: 09-07-2022
Abstract: Self- and cross-excitation in point processes are commonly captured in the financial econometrics literature using a multivariate exponential memory kernel. In this article, the exponential assumption is relaxed and the resultant non-parametric memory kernel is estimated by a method based on second-order cumulants. The estimator is shown to be consistent and asymptotically normally distributed and performs well under simulation. An empirical application based on 10 international stock indices is presented. Two different indices of contagion between markets are constructed from the point process models in order to examine interconnection over time. A conclusion which emerges from these results is the assumption that a parametric kernel may be too restrictive as the application reveals interesting features, and in some cases substantial differences, between the exponential and non-parametric kernels.
Publisher: Wiley
Date: 26-08-2013
Publisher: Elsevier BV
Date: 08-2007
Publisher: Wiley
Date: 09-06-2015
DOI: 10.1002/FOR.2357
Publisher: Wiley
Date: 07-06-2022
DOI: 10.1002/FOR.2797
Abstract: This paper considers how information from the implied volatility (IV) term structure can be harnessed to improve stock return volatility forecasting within the state‐of‐the‐art HAR model. Factors are extracted from the IV term structure and included as exogenous variables in the HAR framework. We found that including slope and curvature factors leads to significant forecast improvements over the HAR benchmark at a range of forecast horizons, compared with the standard HAR model and HAR model with VIX as IV information set.
Publisher: Elsevier BV
Date: 10-2018
Publisher: Elsevier BV
Date: 04-2023
Publisher: Wiley
Date: 13-04-2015
DOI: 10.1002/FUT.21724
Publisher: Wiley
Date: 09-2006
DOI: 10.1002/ASMB.647
Abstract: Many traditional econometric methods forecast the conditional distribution of asset returns by a point prediction of volatility. Alternatively, forecasts of this distribution may be generated from a mixture of distributions. This paper proposes a method by which information extracted from the estimation of a standard stochastic volatility model (using non‐linear filtering) can be used to generate mixture distribution forecasts. In general, it is found that forecasts based on mixture distributions are superior to those simply using point predictions of volatility. In terms of mixture distribution forecasts, the method proposed in this paper is found to be superior to a number of competing approaches. Copyright © 2006 John Wiley & Sons, Ltd.
Publisher: Elsevier BV
Date: 08-2006
Publisher: Elsevier BV
Date: 12-2013
Publisher: Elsevier BV
Date: 07-2014
Publisher: Elsevier BV
Date: 11-2018
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 2008
End Date: 06-2011
Amount: $32,700.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2012
End Date: 12-2015
Amount: $100,000.00
Funder: Australian Research Council
View Funded Activity