Introduction of electronic trading in index futures : volatility and volume effects

There is continuing debate in the US over full introduction of electronic trading in those index futures contracts that are still traded at the CME via open outcry. Since the late 1990s major international exchanges trading index futures contracts have converted to full electronic trading. Recent empirical studies have focused on effects on bid/ask spreads and related price volatility following these changes. We take a different approach and investigate and test for structural change in conditional volatility and volume effects following the shift to electronic trading in the Australian Share Price Index futures contract. Multiple Switching point GARCH models are employed with the data sampled at 5, 15 and 30-minute intervals from transaction records supplied by the Sydney Futures Exchange. There is significant evidence of structural changes in both the persistence of volatility shocks and simultaneous volume effects following the change to screen trading in this futures market.

Simultaneous volatility transmission and spillover effects

Simultaneous volatility models are developed and shown to be separate from multivariate GARCH estimators. An example is provided that allows for simultaneous and unidirectional volatility and volume of trade effects. These effects are tested using intraday data from the Australian cash index and index futures markets. Overnight volatility spillover effects from the United States S&P500 index futures markets are tested using alternative estimates of this US market volatility. The simultaneous volatility model proves to be robust to alternative specifications of returns equations and to misspecification of the direction of volatility causality.

Construction of optimal prediction intervals for load forecasting problems

Short-term load forecasting is fundamental for the reliable and efficient operation of power systems. Despite its importance, accurate prediction of loads is problematic and far remote. Often uncertainties significantly degrade performance of load forecasting models. Besides, there is no index available indicating reliability of predicted values. The objective of this study is to construct prediction intervals for future loads instead of forecasting their exact values. The delta technique is applied for constructing prediction intervals for outcomes of neural network models. Some statistical measures are developed for quantitative and comprehensive evaluation of prediction intervals. According to these measures, a new cost function is designed for shortening length of prediction intervals without compromising their coverage probability. Simulated annealing is used for minimization of this cost function and adjustment of neural network parameters. Demonstrated results clearly show that the proposed methods for constructing prediction interval outperforms the traditional delta technique. Besides, it yields prediction intervals that are practically more reliable and useful than exact point predictions.

Institutions and growth volatility

Recently some studies provided evidence that democratic political institutions generate less volatile growth. These studies, however, do not provide any link between democracy and investment volatility. Here, we focus on the specific channel that links individualistic societies and low growth volatility. We test whether investment volatility and consequently growth volatility are lower in individualistic societies. We construct a two-equation system of investment and income growth volatility, allowing various measures of individualism to influence growth volatility both directly and indirectly. We find that individualism significantly directly and indirectly influences growth volatility negatively.

The accuracy of housing forecasting in Australia

Forecasting is an integral part of all business planning, and forecasting the outlook for housing is of interest to many firms in the housing construction sector. This research measures the performance of a number of industry forecasting bodies; this is done to provide users with an indicator of the value of housing forecasting undertaken in Australia. The accuracy of housing commencement forecasts of three Australian organisations – the Housing Industry Association (HIA), the Indicative Planning Council for the Housing Industry (IPC) and BIS-Shrapnel – is examined through the empirical analysis of their published forecasts supplemented by qualitative data in the form of opinions elicited from several industry “experts” employed in these organisations. Forecasting performance was determined by comparing the housing commencement forecast with the actual data collected by the Australian Bureau of Statistics on an ex-post basis. Although the forecasts cover different time periods, the level of accuracy is similar, at around 11-13 per cent for four-quarter-ahead forecasts. In addition, national forecasts are more accurate than forecasts for individual states. This is the first research that has investigated the accuracy of both private and public sector forecasting of housing construction in Australia. This allows users of the information to better understand the performance of various forecasting organisations.

Forecasting the onset of the grass pollen season in Melbourne (Australia)

In Melbourne, a southern hemisphere city with a cool temperate climate, the grass pollen season has been monitored using a Burkard spore trap for 12 years (11 pollen seasons, which extend from October through January). The onset of the grass pollen season (OGPS) has been defined in various ways using both arbitrary cumulative scores (Sum 75, Sum 100) and percentages (10% Pollen Fly). OGPS, based on the forecast model of pollen season devised by Lejoly-Gabriel (Acta Geogr. Lovan., 13 (1978) 1–260) has been most widely used in efforts to forecast the beginning of the pollen season. OGPS occurred in Melbourne between 20 October to 24 November (average 6 November), a difference of 35 days. Duration of the pollen season ranged from 46 to 81 days, with a mean of 55 days, one of the longest reported. The relationships between onset and various weather parameters for July have enabled us to modify a model, using linear regression analysis, to predict onset. The prediction model is based on a negative correlation between date of onset and the sum of rainfall for July (a winter month). The error of prediction (Ep) is 24% and predicted day of OGPS was precisely predicted on 2 occasions, and on others with a range of accuracy of 3 to 14 days.

Forecasting construction demand : a vector error correction model with dummy variables

Modelling the level of demand for construction is vital in policy formulation and implementation as the construction industry plays an important role in a country’s economic development process. In construction economics, research efforts on construction demand modelling and forecasting are various, but few researchers have considered the impact of global economy events in construction demand modelling. An advanced multivariate modelling technique, namely the vector error correction (VEC) model with dummy variables, was adopted to predict demand in the Australian construction market. The results of prediction accuracy tests suggest that the general VEC model and the VEC model with dummy variables are both acceptable for forecasting construction economic indicators. However, the VEC model that considers external impacts achieves higher prediction accuracy than the general VEC model. The model estimates indicate that the growth in population, changes in national income, fluctuations in interest rates and changes in householder expenditure all play significant roles when explaining variations in construction demand. The VEC model with disturbances developed can serve as an experimentation using an advanced econometrical method which can be used to analyse the effect of specific events or factors on the construction market growth.

Volatility decomposition of Australian housing prices

This study examines the volatility pattern of Australian housing prices. The approach for this research was to decompose the conditional volatility of housing prices into a “permanent” component and a “transitory” component via a Component-Generalized Autoregressive Conditional Heteroskedasticity (C-GARCH) model. The results demonstrate that the shock impact on the short-run component (transitory) is much larger than the long-run component (permanent), whereas the persistence of transitory shocks is much less than permanent shocks. Moreover, both permanent and transitory volatility components have different determinants. The results provide important new insights into the volatility pattern of housing prices which has direct implications for investment in housing by owner-occupiers and investors.