Emergency Funds in Australian Households: An Empirical Analysis of Capacity and Sources

This paper examines demographic and socioeconomic characteristics as predictors of emergency fund adequacy in Australian households. The results indicate that the presence of children, the number of dependents and income-earning units, the age and ethnicity of the household head, income dependency upon retirement plans and investments and government pensions and benefits, homeownership and disposable income are significant determinants of the capacity to raise emergency funds. They are also important predictors of the likely source of emergency funds. However, they are generally better at predicting mainstay sources of funds such as own savings and loans from deposit-taking institutions and credit card usage than loans from family or friends

The Distribution of Financial Literacy in Australia

Ordered logit models are used to predict financial literacy on the basis of individual demographic, socioeconomic and financial characteristics. The data is drawn from the 2003 ANZ Survey of Adult Financial Literacy in Australia and relates to 3,548 respondents. Financial literacy is defined, amongst other things, in terms of standard mathematical ability and understanding of basic and advanced financial terms. Factors examined include gender, age, ethnicity, occupation, educational level and family structure, along with household income, savings (including superannuation), and mortgage and non-mortgage debt. The evidence suggests that financial literacy is highest for respondents aged between 50 and 60 years, professionals, executives, business and farm owners, and those who have completed university or college with higher levels of income, savings and debt. Financial literacy is lowest for females, the unemployed and other non-workers, those from a non-English speaking background, and those with only the lowest levels of secondary education. The models best predict the highest and lowest levels of financial literacy.Financial literacy; ordered logit; demographic, socioeconomic and financial characteristics.

Debt as a source of financial stress in Australian households

This paper examines the role of demographic, socioeconomic and debt portfolio characteristics as contributors to financial stress in Australian households. The data is drawn from the most-recent Household Expenditure Survey Confidentialised Unit Record Files (CURF) and relate to 3,268 probability-weighted households. Financial stress is defined, amongst other things, in terms of financial reasons for being unable to have a holiday, have meals with family and friends, and engage in hobbies and other leisure activities and overall financial management. Characteristics examined included family structure and composition, source and level of household income, age, sex and marital status, ethic background, housing value, debt repayments and credit card usage. Binary logit models are used to identify the source and magnitude of factors associated with financial stress. The evidence provided suggests that financial stress is higher in families with more children or other dependents and from ethnic minorities, especially those more reliant on government pensions and benefits, and negatively related to disposable income and housing value. There is little evidence to suggest that Australia’s historically high levels of household debt are currently the cause of significant amounts of financial stress in these households.Household and consumer debt, owner-occupied and investor housing, financial stress.

Emergency finance in Australian households An empirical analysis of capacity and sources

This paper examines demographic and socioeconomic characteristics as predictors of emergency finance in Australian households. The data is drawn from the most recent Household Expenditure Survey Confidentialised Unit Record Files (CURF) and relate to 6,892 probability-weighted households. Emergency finance is defined in terms of the ability to raise $2,000 within one week and its potential sources include own savings and loans from deposit-taking institutions, finance companies, credit cards, family and friends and welfare or community organisations. Characteristics examined included family structure and composition, source and level of household income, age, sex and marital status, ethnic background and housing value. Binary logistic models are used to identify the source and magnitude of factors associated with the ability to raise emergency finance and the likelihood of choosing each method of raising finance. The results indicate that the presence of children, the number of dependents and income-earning units, the age, sex and ethnicity of the household head, dependency upon government pensions and benefits, homeownership and disposable income are significant determinants of the capacity to raise emergency finance. However, the demographic and socioeconomic factors examined are generally better at predicting mainstay sources of finance such as own savings and loans from deposit-taking institutions and credit card usage than loans from family and friends and welfare or community organisations.Emergency funds, financial planning, economic and financial wellbeing.

Systematic Features of High-Frequency Volatility in Australian Electricity Markets: Intraday Patterns, Information Arrival and Calendar Effects

This paper investigates the intraday price volatility process in four Australian wholesale electricity markets; namely New South Wales, Queensland, South Australia and Victoria. The data set consists of half-hourly electricity prices and demand volumes over the period 1 January 2002 to 1 June 2003. A range of processes including GARCH, Risk Metrics, normal Asymmetric Power ARCH or APARCH, Student APARCH and skewed Student APARCH are used to model the timevarying variance in prices and the inclusion of news arrival as proxied by the contemporaneous volume of demand, time-of-day, day-of-week and month-of-year effects as exogenous explanatory variables. The skewed Student APARCH model, which takes account of right skewed and fat tailed characteristics, produces the best results in three of the markets with the Student APARCH model performing better in the fourth. The results indicate significant innovation spillovers (ARCH effects)and volatility spillovers (GARCH effects) in the conditional standard deviation equation, even with market and calendar effects included. Intraday prices also exhibit significant asymmetric responses of volatility to the flow of information

Macroeconomic risk factors in Australian commercial real estate, listed property trust and property sector stock returns: A comparative analysis using GARCH-M

This paper employs a Generalised Autoregressive Conditional Heteroskedasticity in Mean (GARCH-M) model to consider the effect of macroeconomic factors on Australian property returns over the period 1985 to 2002 Three direct (office, retail and industrial property) and two indirect (listed property trust and property stock) returns are included in the analysis, along with market returns, short, medium and long-term interest rates, expected and unexpected inflation, construction activity and industrial employment and production. In general, the macroeconomic factors examined are found to be significant risk factors in Australian commercial property returns. However, the results also indicate that forecast accuracy in these models is higher for direct office, listed property trust and property stock returns and that the persistence of volatility shocks varies across the different markets, with volatility half lives of between five and seven months for direct retail and industrial property, two and three months for direct office property and less than two months with both forms of indirect property investment

Weak-form market efficiency in European emerging and developed stock markets

This paper tests for random walks and weak-form market efficiency in European equity markets. Daily returns for sixteen developed markets (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and the United Kingdom) and four emerging markets (Czech Republic, Hungary, Poland and Russia) are examined for random walks using a combination of serial correlation coefficient and runs tests, Augmented Dickey-Fuller (ADF), Phillips-Perron (PP) and Kwiatkowski, Phillips, Schmidt and Shin (KPSS) unit root tests and multiple variance ratio (MVR) tests. The results, which are in broad agreement across the approaches employed, indicate that of the emerging markets only Hungary is characterized by a random walk and hence is weak-form efficient, while in the developed markets only Germany, Ireland, Portugal, Sweden and the United Kingdom comply with the most stringent random walk criteria.Developed and emerging markets, random walk hypothesis, market efficiency

Systematic Features of High-Frequency Volatility in Australian Electricity Markets: Intraday Patterns, Information Arrival and Calendar Effects

This paper investigates the intraday price volatility process in four Australian wholesale electricity markets; namely New South Wales, Queensland, South Australia and Victoria. The data set consists of half-hourly electricity prices and demand volumes over the period 1 January 2002 to 1 June 2003. A range of processes including GARCH, Risk Metrics, normal Asymmetric Power ARCH or APARCH, Student APARCH and skewed Student APARCH are used to model the time-varying variance in prices and the inclusion of news arrival as proxied by the contemporaneous volume of demand, time-of-day, day-of-week and month-of-year effects as exogenous explanatory variables. The skewed Student APARCH model, which takes account of right skewed and fat tailed characteristics, produces the best results in three of the markets with the Student APARCH model performing better in the fourth. The results indicate significant innovation spillovers (ARCH effects) and volatility spillovers (GARCH effects) in the conditional standard deviation equation, even with market and calendar effects included. Intraday prices also exhibit significant asymmetric responses of volatility to the flow of information.

The Relationship Between Energy Spot and Futures Prices: Evidence from the Australian Electricity Market

This paper examines the relationship between futures and spot electricity prices for two of the Australian electricity regions in the National Electricity Market (NEM): namely, New South Wales and Victoria. A generalised autoregressive conditional heteroskedasticity (GARCH) model is used to identify the magnitude and significance of mean and volatility spillovers from the futures market to the spot market. The results indicate the presence of positive mean spillovers in the NSW market for peak and off-peak (base load) futures contracts and mean spillovers for the offpeak Victorian futures market. The large number of significant innovation and volatility spillovers between the futures and spot markets indicates the presence of strong ARCH and GARCH effects. Contrary to evidence from studies in North American electricity markets, the results also indicate that Australian electricity spot and futures prices are stationary.

Short and Long-Term Price Linkages Among Asia-Pacific Economic Cooperation (APEC) Equity Markets

This paper examines the short and long-term price linkages among Asia-Pacific Economic Cooperation (APEC) equity markets over the period 1995 to 2000. Seven developed markets (Australia, Canada, Hong Kong, Japan, New Zealand, Singapore and the United States) and eleven emerging markets (China, Chile, Indonesia, Korea, Malaysia, Mexico, Peru, the Philippines, Russia, Taiwan and Thailand) are included in the analysis. Multivariate cointegration procedures, Granger-causality tests, level VAR and generalised variance decomposition analyses based on error-correction and vector autoregressive models are conducted to examine long and short-run relationships among these markets. The results indicate that there is a stationary long-run relationship and significant short-run causal linkages among the APEC equity markets. The results also indicate that the degree of comovement and codependencies among APEC’s domestic and sub-regional markets varies considerably. In general, Australasian, Northern Asian and South American markets are relatively more influenced by domestic market conditions, North American markets relatively more by regional factors and Southern Asian markets more strongly influenced by markets outside either their own or geographical close domestic markets.Cointegration; regional equity markets; APEC