ORCID Profile
0000-0003-0697-0684
Current Organisation
Macquarie University
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Time-Series Analysis | Financial Economics | Investment and Risk Management | Econometrics | Applied Economics | Financial Econometrics |
Human Capital Issues | Finance Services | Superannuation and Insurance Services | Savings and Investments
Publisher: SAGE Publications
Date: 20-04-2022
DOI: 10.1177/00420980221083139
Abstract: We examine the relationship between rail accessibility and the pattern of demographic characteristics at long-established Rail Transit Served Communities. The analytical methods involve the juxtaposition of property premium estimates and assessment of spatial effects on demographic composition. Despite finding considerable property premiums associated with access to rail transit across metropolitan Sydney, we report little evidence of sorting in relation to economically advantaged or disadvantaged residents. Further, the demographic groups commonly linked to gentrification, including high-income and professionals, are not found to dominate areas of high rail accessibility and only those with advanced educational qualifications are shown to increase in concentration with closer access to rail transit.
Publisher: Elsevier BV
Date: 04-2020
Publisher: Wiley
Date: 12-01-2011
Publisher: Informa UK Limited
Date: 08-06-2019
Publisher: Wiley
Date: 28-11-2018
Publisher: Elsevier BV
Date: 12-2022
Publisher: Elsevier BV
Date: 06-2023
Publisher: Elsevier BV
Date: 05-2010
Publisher: Wiley
Date: 10-01-2022
DOI: 10.1002/FOR.2846
Abstract: About 99% of cryptocurrency trades occur on organized exchanges with many investors subsequently keeping their digital assets in accounts with cryptocurrency markets. This generates exposure to the risk of exchange closures. We construct a database containing eight key characteristics on 238 cryptocurrency exchanges and employ machine learning techniques to predict whether a cryptocurrency market will remain active or whether it will go out of business. Both in‐s le and out‐of‐s le measures of forecasting performance are computed and ranked for four popular machine learning algorithms. Although all four models produce satisfactory classification accuracy, our best model is a random forest classifier. It reaches accuracy of 90.4% on training data and 86.1% on a test dataset. From the list of predictors, we find that exchange lifetime, transacted volume, and cyber‐security measures such as security audit, cold storage, and bug bounty programs rank high in terms of feature importance across multiple algorithms. On the other hand, whether an exchange has previously experienced a security breach does not rank highly according to its contribution to classification accuracy.
Publisher: Elsevier BV
Date: 10-2007
Publisher: Emerald
Date: 03-08-2012
DOI: 10.1108/14635781211256747
Abstract: The purpose of this paper is to study correlations between the national real estate investment trusts (REIT) markets in the USA and the four Asia‐Pacific countries of Australia, Hong Kong, Japan and Singapore, and document the extent to which the time variation present in these correlations can be explained from a set of 11 economic and financial factors. Both US dollar and local currency returns are used. Time‐varying correlations are estimated using a DCC‐GARCH model that allows for asymmetries in both the correlations and volatilities. The correlations are then regressed on a set of four economic and seven financial factors, and tests of statistical significance are conducted in order to discriminate between relevant and irrelevant explanatory variables. The authors estimate a fixed‐effects panel regression as well as in idual regressions for each dynamic correlation. Significant time variation is found in the four REIT correlation series. Panel regressions suggest that REIT correlations rise with increases in the interaction of national inflation rates and with higher global equity market uncertainty. It is also found that REIT correlations fall with increases in the US default risk premium and global equity market volume. Relaxing the structure imposed by the panel data model, in idual regressions confirm most of the results, although there are some exceptions. It is also found that there are no substantial differences in the dynamics of the correlation coefficients when switching from the US dollar to local currency denominated returns. Investors in real estate securities across national markets should take into account information about the credit spread, the volatility and volume of global equity markets, and inflation rates when modeling correlations. These variables may alert the investors to the possibility that, under a set of circumstances, investing in real estate across different markets may not provide the expected ersification benefits. Another implication relates to the impact of currency hedging. It appears that the impact of switching from US dollar to local currency denominated returns does not substantially change the time dynamics of the correlations, or the importance of explanatory variables. Although considerable progress has been made in modelling time‐varying correlations between various REIT markets, to the authors' knowledge, this is one of the first papers to investigate the underlying causes of the co‐movement, especially between the US and Asia‐Pacific markets. The paper's results will help investors and risk managers make better choices by identifying those factors that have more systematic effects on the change in the REIT correlations, rather than more transient forces.
Publisher: Elsevier BV
Date: 12-2016
Publisher: Informa UK Limited
Date: 2013
Publisher: Emerald
Date: 02-2013
DOI: 10.1108/14635781311292971
Abstract: The purpose of this paper is to investigate contagion between real estate investment trusts (REITs) within and across three geographical regions: North America, Europe and Asia‐Pacific. The paper also examines excess comovement between the considered national REIT markets on the one hand, and broad equity indices on the other. In particular, the authors are interested in contagion between the considered markets during the 2007‐2009 GFC period in comparison to the entire 2004‐2011 s le period. Using an international factor pricing framework similar to Bekaert, Harvey and Ng, the paper defines contagion as excess comovement between two financial markets, after removing the effects of the underlying economic fundamentals, i.e. risk factors, and time‐changing volatility. Controlling for economic factors is important for distinguishing between pure contagion and information spillovers, which may transmit through existing economic channels. The authors then analyse excess correlations between the derived standardized residuals, for REITS and equity markets in order to investigate excess comovement between the indices during the whole s le and GFC period. The paper finds no evidence of excess comovement between the considered REIT and equity indices during non‐crisis s le intervals. However, the paper finds contagion between several national REITs and regional or global equity markets during the GFC period. The paper reports statistically significant excess correlations between national REITs and regional and world real estate markets during the entire s le period, while there is only limited evidence to suggest that the correlation amongst REIT markets has increased during the GFC period. The paper concludes that a similar degree of dependence persisted among national REIT markets over the crisis and non‐crisis s le periods for most markets. Despite the ongoing debate on contagion in financial markets, there is only a small body of literature investigating contagion specifically for property or real estate markets. This is even more surprising, since the GFC originated from a subprime mortgage crisis and was, therefore, heavily related to real estate. The paper extends the literature by testing for contagion between REITs considering eleven national markets across three geographical regions. In contrast, the existing literature is typically constrained to a significantly smaller number of markets. The paper also explicitly takes into account the impact of the recent GFC, and tests for contagion over this period.
Publisher: Informa UK Limited
Date: 05-08-2014
Publisher: Wiley
Date: 06-08-2007
Publisher: Elsevier BV
Date: 02-2022
Publisher: Wiley
Date: 19-03-2020
DOI: 10.1002/FOR.2678
Publisher: Elsevier BV
Date: 10-2020
Publisher: Informa UK Limited
Date: 05-2010
Publisher: Wiley
Date: 08-2005
Publisher: Informa UK Limited
Date: 16-02-2015
Publisher: Elsevier BV
Date: 09-2006
Publisher: Informa UK Limited
Date: 02-2012
Publisher: Walter de Gruyter GmbH
Date: 03-05-2013
Abstract: Abstract : We consider the local identification of parameters in structural VAR models with ARCH type errors. By establishing a mapping between the structural and reduced-form models, we provide a set of sufficient conditions for the joint identification of all parameters. Under these conditions, as the structural parameters are identified, various restrictions on the parameters can be tested in a standard manner. For ex le, the significance test for the ARCH effect in the usual GARCH formulation for a structural shock does not suffer the complications caused by a lack of identification encountered in univariate GARCH models.
Publisher: SAGE Publications
Date: 09-2010
DOI: 10.1260/0958-305X.21.5.367
Abstract: We investigate time series linkages between the EU carbon allowance price and the prices of coal, oil, natural gas and electricity. We find no long-run relationship between the variables, but instead some short-run linkages. Using Granger causality tests and generalised impulse-response analysis we find evidence of links between i) carbon and oil, ii) carbon and natural gas, and iii) electricity and carbon, as well as other links between the energy variables. The finding of no long-term relationship can be attributed either to the relative immaturity and imperfections of the carbon market, or the possibility that while carbon trading may result in a more efficient use of energy resources, it does not directly impact fossil fuel prices.
Start Date: 04-2019
End Date: 12-2023
Amount: $220,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 02-2012
End Date: 12-2015
Amount: $170,000.00
Funder: Australian Research Council
View Funded Activity