ORCID Profile
0000-0002-5656-4556
Current Organisation
University of Sydney
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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.
Applied Statistics | Dynamical Systems | Econometric And Statistical Methods | Econometric and Statistical Methods | Commercial Services | Econometrics | Banking, Finance and Investment | Control Engineering | Mechanical Engineering | Applied Economics Not Elsewhere Classified | Financial Econometrics | Real Estate And Valuation | Financial Econometrics
Mathematical sciences | Telecommunications | Application packages | Finance and investment services | Property and business services | Expanding Knowledge in Economics | Expanding Knowledge in the Mathematical Sciences |
Publisher: Elsevier BV
Date: 04-2020
Publisher: Elsevier
Date: 2008
Publisher: Springer Science and Business Media LLC
Date: 17-01-2022
DOI: 10.1007/S41060-021-00306-9
Abstract: Graph-based approaches are revolutionizing the analysis of different real-life systems, and the stock market is no exception. In idual stocks and stock market indices are connected, and interesting patterns appear when the stock market is considered as a graph. Researchers are analyzing the stock market using graph-based approaches in recent years, and there is a need to survey those works from multiple perspectives. We discuss the existing graph-based works from five perspectives: (i) stock market graph formulation, (ii) stock market graph filtering, (iii) stock market graph clustering, (iv) stock movement prediction, and (v) portfolio optimization. This study contains a concise description of major techniques and algorithms relevant to graph-based approaches for the stock market.
Publisher: Elsevier BV
Date: 10-2006
Publisher: Elsevier BV
Date: 2006
Publisher: SAGE Publications
Date: 10-2007
DOI: 10.1177/1471082X0700700303
Abstract: We consider the problem of variable selection for logistic regression when the dependent variable is measured imperfectly, under both differential and non-differential misclassification. An MCMC s ling scheme is designed, incorporating uncertainty about which explanatory variables affect the dependent variable and which affect the probability of misclassification. We assume that a small gold standard perfectly measured s le is available to augment the imperfectly measured s le, under the differential misclassification framework. A simulation study illustrates favourable results both in terms of variable selection and parameter estimation. Ex les analysing the risk of violence against young women by their partner and the risk of injury in highway motor accidents are considered.
Publisher: Institute of Mathematical Statistics
Date: 03-2009
DOI: 10.1214/09-BA405
Publisher: Wiley
Date: 23-10-2014
DOI: 10.1002/CJS.11228
Publisher: Wiley
Date: 26-05-2011
DOI: 10.1002/FOR.1237
Publisher: Wiley
Date: 21-03-2016
DOI: 10.1002/FOR.2408
Publisher: Wiley
Date: 24-09-2019
DOI: 10.1002/ASMB.2410
Publisher: Informa UK Limited
Date: 09-2020
Publisher: Informa UK Limited
Date: 09-2000
Publisher: Informa UK Limited
Date: 21-06-2021
Publisher: IEEE
Date: 18-07-2021
Publisher: Informa UK Limited
Date: 09-2011
Publisher: Elsevier BV
Date: 03-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Wiley
Date: 08-2009
Publisher: Springer Science and Business Media LLC
Date: 17-04-2007
Publisher: Informa UK Limited
Date: 05-2009
Publisher: Elsevier BV
Date: 11-2019
Publisher: Informa UK Limited
Date: 05-01-2023
Publisher: Elsevier BV
Date: 2017
DOI: 10.2139/SSRN.2999451
Publisher: Wiley
Date: 25-06-2013
DOI: 10.1002/FOR.2255
Publisher: Wiley
Date: 29-05-2014
DOI: 10.1002/JAE.2329
Publisher: Springer Science and Business Media LLC
Date: 04-2010
Publisher: Elsevier BV
Date: 06-2014
Publisher: Elsevier BV
Date: 03-2007
Publisher: MDPI AG
Date: 07-05-2018
DOI: 10.3390/RISKS6020052
Publisher: Elsevier BV
Date: 04-2020
Publisher: Elsevier BV
Date: 02-2008
Publisher: Elsevier BV
Date: 07-2012
Publisher: Elsevier BV
Date: 08-2014
Publisher: Informa UK Limited
Date: 12-2005
Publisher: Elsevier BV
Date: 11-2012
Publisher: Informa UK Limited
Date: 30-10-2019
Publisher: Elsevier BV
Date: 12-2006
Publisher: Springer International Publishing
Date: 2018
Publisher: Elsevier BV
Date: 07-2006
Publisher: Wiley
Date: 17-02-2017
DOI: 10.1002/ASMB.2237
Publisher: Elsevier BV
Date: 04-2009
Publisher: Wiley
Date: 02-05-2008
Publisher: Elsevier BV
Date: 2018
DOI: 10.2139/SSRN.3176288
Publisher: Elsevier BV
Date: 07-2019
Publisher: Elsevier BV
Date: 04-2012
Publisher: Wiley
Date: 16-11-2006
Publisher: Elsevier BV
Date: 10-2005
Publisher: Oxford University Press (OUP)
Date: 12-08-2014
Publisher: Elsevier BV
Date: 10-2013
Publisher: Informa UK Limited
Date: 15-07-2016
Publisher: Elsevier BV
Date: 04-2020
Publisher: Informa UK Limited
Date: 11-04-2022
Publisher: Springer Science and Business Media LLC
Date: 22-06-2010
Publisher: Informa UK Limited
Date: 23-11-2019
Publisher: Informa UK Limited
Date: 10-2011
Publisher: Elsevier BV
Date: 2020
DOI: 10.2139/SSRN.3553270
Publisher: Informa UK Limited
Date: 02-2008
Publisher: Wiley
Date: 24-11-2008
DOI: 10.1111/J.1541-0420.2008.01134_1.X
Abstract: Highest posterior density intervals are common in Bayesian inference, but as noted by Agresti and Min (2005, Biometrics 61, 515-523) they are not invariant under transformations. Agresti and Min suggested central or "tail" intervals as preferable in the context of the relative risk and odds ratio. A modification to this is proposed for extreme outcomes, as invariance is maintained when replacing central intervals by one-sided intervals. Bayes-Laplace priors for the binomial parameters appear preferable here, compared to Jeffreys priors, contrary to Agresti and Min's suggestion based on frequentist coverage.
Publisher: Springer Science and Business Media LLC
Date: 05-08-2016
Publisher: Elsevier BV
Date: 02-2006
Publisher: Informa UK Limited
Date: 26-10-2016
Publisher: Springer Science and Business Media LLC
Date: 10-04-2015
Publisher: Informa UK Limited
Date: 09-07-2019
Publisher: Elsevier BV
Date: 07-2015
Publisher: Elsevier BV
Date: 10-2016
Publisher: Wiley
Date: 24-09-2015
DOI: 10.1002/ASMB.2062
Publisher: Informa UK Limited
Date: 09-07-2021
Publisher: Wiley
Date: 2005
DOI: 10.1002/ASMB.600
Publisher: Wiley
Date: 2010
DOI: 10.1002/ASMB.765
Publisher: Springer Science and Business Media LLC
Date: 25-07-2012
Publisher: Informa UK Limited
Date: 07-03-2016
Publisher: Elsevier BV
Date: 12-2010
Publisher: Wiley
Date: 12-2005
Publisher: Wiley
Date: 2004
DOI: 10.1002/FUT.20117
Publisher: Wiley
Date: 06-2006
Publisher: Springer Science and Business Media LLC
Date: 23-04-2008
Publisher: Elsevier BV
Date: 04-2009
Publisher: MDPI AG
Date: 20-05-2016
DOI: 10.3390/RISKS4020014
Publisher: Elsevier BV
Date: 04-2009
Publisher: Oxford University Press (OUP)
Date: 27-05-2020
Abstract: A new model framework called Realized Conditional Autoregressive Expectile is proposed, whereby a measurement equation is added to the conventional Conditional Autoregressive Expectile model. A realized measure acts as the dependent variable in the measurement equation, capturing the contemporaneous dependence between it and the latent conditional expectile it also drives the expectile dynamics. The usual grid search and asymmetric least squares optimization, to estimate the expectile level and parameters, suffers from convergence issues leading to inefficient estimation. This article develops an alternative random walk Metropolis stochastic target search method, incorporating an adaptive Markov Chain Monte Carlo s ler, which leads to improved accuracy in estimation of the expectile level and model parameters. The s ling properties of this method are assessed via a simulation study. In a forecast study applied to several market indices and asset return series, one-day-ahead Value-at-Risk and Expected Shortfall forecasting results favor the proposed model class.
Publisher: Elsevier BV
Date: 02-2022
Publisher: Elsevier BV
Date: 12-2008
Publisher: Elsevier BV
Date: 11-2011
Start Date: 09-2009
End Date: 09-2012
Amount: $148,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2010
End Date: 12-2011
Amount: $120,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 02-2020
End Date: 12-2024
Amount: $280,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2003
End Date: 12-2011
Amount: $13,749,290.00
Funder: Australian Research Council
View Funded Activity