Are combination forecasts of S&P 500 volatility statistically superior?

Forecasting volatility has received a great deal of research attention, with the relative performances of econometric model based and option implied volatility forecasts often being considered. While many studies find that implied volatility is the pre-ferred approach, a number of issues remain unresolved, including the relative merit of combining forecasts and whether the relative performances of various forecasts are statistically different. By utilising recent econometric advances, this paper considers whether combination forecasts of S&P 500 volatility are statistically superior to a wide range of model based forecasts and implied volatility. It is found that a combination of model based forecasts is the dominant approach, indicating that the implied volatility cannot simply be viewed as a combination of various model based forecasts. Therefore, while often viewed as a superior volatility forecast, the implied volatility is in fact an inferior forecast of S&P 500 volatility relative to model-based forecasts.

The jump component of S&P 500 volatility and the VIX index

Much research has investigated the differences between option implied volatilities and econometric model-based forecasts. Implied volatility is a market determined forecast, in contrast to model-based forecasts that employ some degree of smoothing of past volatility to generate forecasts. Implied volatility has the potential to reflect information that a model-based forecast could not. This paper considers two issues relating to the informational content of the S&P 500 VIX implied volatility index. First, whether it subsumes information on how historical jump activity contributed to the price volatility, followed by whether the VIX reflects any incremental information pertaining to future jump activity relative to model-based forecasts. It is found that the VIX index both subsumes information relating to past jump contributions to total volatility and reflects incremental information pertaining to future jump activity. This issue has not been examined previously and expands our understanding of how option markets form their volatility forecasts.

A dynamic approach to forecast rental growth

Numerous econometric models have been proposed for forecasting property market performance, but limited success has been achieved in finding a reliable and consistent model to predict property market movements over a five to ten year timeframe. This research focuses on office rental growth forecasts and overviews many of the office rent models that have evolved over the past 20 years. A model by DiPasquale and Wheaton is selected for testing in the Brisbane, Australia office market. The adaptation of this study did not provide explanatory variables that could assist in developing a reliable, predictive model of office rental growth. In light of this result, the paper suggests a system dynamics framework that includes an econometric model based on historical data as well as user input guidance for the primary variables. The rent forecast outputs would be assessed having regard to market expectations and probability profiling undertaken for use in simulation exercises. The paper concludes with ideas for ongoing research.

Immigrant wage differentials and the role of self-employment in Australia

Immigration has played an important role in the historical development of Australia. Thus, it is no surprise that a large body of empirical work has developed, which focuses upon how migrants fare in the land of opportunity. Much of the literature is comparatively recent, i.e. the last ten years or so, encouraged by the advent of public availability of Australian crosssection micro data. Several different aspects of migrant welfare have been addressed, with major emphasis being placed upon earnings and unemployment experience. For recent examples see Haig (1980), Stromback (1984), Chiswick and Miller (1985), Tran-Nam and Nevile (1988) and Beggs and Chapman (1988). The present paper contributes to the literature by providing additional empirical evidence on the native/migrant earnings differential. The data utilised are from the rather neglected Australian Bureau of Statistics, ABS Special Supplementary Survey No.4. 1982, otherwise known as the Family Survey. The paper also examines the importance of distinguishing between the wage and salary sector and the self-employment sector when discussing native/migrant differentials. Separate earnings equations for the two labour market groups are estimated and the native/migrant earnings differential is broken down by employment status. This is a novel application in the Australian context and provides some insight into the earnings of the selfemployed, a group that despite its size (around 20 per cent of the labour force) is frequently ignored by economic research. Most previous empirical research fails to examine the effect of employment status on earnings. Stromback (1984) includes a dummy variable representing self-employment status in an earnings equation estimated over a pooled sample of paid and self-employed workers. The variable is found to be highly significant, which leads Stromback to question the efficacy of including the self-employed in the estimation sample. The suggestion is that part of self-employed earnings represent a return to non-human capital investment, i.e. investments in machinery, buildings etc, the structural determinants of earnings differ significantly from those for paid employees. Tran-Nam and Nevile (1988) deal with differences between paid employees and the selfemployed by deleting the latter from their sample. However, deleting the self-employed from the estimation sample may lead to bias in the OLS estimation method (see Heckman 1979). The desirable properties of OLS are dependent upon estimation on a random sample. Thus, the 'Ran-Nam and Nevile results are likely to suffer from bias unless individuals are randomly allocated between self-employment and paid employment. The current analysis extends Tran-Nam and Nevile (1988) by explicitly treating the choice of paid employment versus self-employment as being endogenously determined. This allows an explicit test for the appropriateness of deleting self-employed workers from the sample. Earnings equations that are corrected for sample selection are estimated for both natives and migrants in the paid employee sector. The Heckman (1979) two-step estimator is employed. The paper is divided into five major sections. The next section presents the econometric model incorporating the specification of the earnings generating process together with an explicit model determining an individual's employment status. In Section 111 the data are described. Section IV draws together the main econometric results of the paper. First, the probit estimates of the labour market status equation are documented. This is followed by presentation and discussion of the Heckman two-stage estimates of the earnings specification for both native and migrant Australians. Separate earnings equations are estimated for paid employees and the self-employed. Section V documents estimates of the nativelmigrant earnings differential for both categories of employees. To aid comparison with earlier work, the Oaxaca decomposition of the earnings differential for paid-employees is carried out for both the simple OLS regression results as well as the parameter estimates corrected for sample selection effects. These differentials are interpreted and compared with previous Australian findings. A short section concludes the paper.

Modeling electricity price events as point processes

Energy prices are highly volatile and often feature unexpected spikes. It is the aim of this paper to examine whether the occurrence of these extreme price events displays any regularities that can be captured using an econometric model. Here we treat these price events as point processes and apply Hawkes and Poisson autoregressive models to model the dynamics in the intensity of this process.We use load and meteorological information to model the time variation in the intensity of the process. The models are applied to data from the Australian wholesale electricity market, and a forecasting exercise illustrates both the usefulness of these models and their limitations when attempting to forecast the occurrence of extreme price events.

Who really pays for urban infrastructure? : the impact of developer infrastructure charges on housing affordability in Brisbane, Australia

This thesis advances the understanding of the impact of developer infrastructure charges on housing affordability in Brisbane, Australia through the development of an econometric model and empirical analysis. The results indicate substantial on-passing of these government charges to purchasers of both new and existing homes, thus negatively impacting housing affordability across the whole community. The results of this thesis will inform policy makers and assist in the development of evidence based policy related to housing affordability and funding of urban infrastructure. Being generic, the econometric model is expected to be a tool that is suitable for estimating similar house price effects in other housing markets.

Econometric analysis of stuctural systems with permanent and transitory shocks

This paper considers the implications of the permanent/transitory decomposition of shocks for identification of structural models in the general case where the model might contain more than one permanent structural shock. It provides a simple and intuitive generalization of the influential work of Blanchard and Quah [1989. The dynamic effects of aggregate demand and supply disturbances. The American Economic Review 79, 655–673], and shows that structural equations with known permanent shocks cannot contain error correction terms, thereby freeing up the latter to be used as instruments in estimating their parameters. The approach is illustrated by a re-examination of the identification schemes used by Wickens and Motto [2001. Estimating shocks and impulse response functions. Journal of Applied Econometrics 16, 371–387], Shapiro and Watson [1988. Sources of business cycle fluctuations. NBER Macroeconomics Annual 3, 111–148], King et al. [1991. Stochastic trends and economic fluctuations. American Economic Review 81, 819–840], Gali [1992. How well does the ISLM model fit postwar US data? Quarterly Journal of Economics 107, 709–735; 1999. Technology, employment, and the business cycle: Do technology shocks explain aggregate fluctuations? American Economic Review 89, 249–271] and Fisher [2006. The dynamic effects of neutral and investment-specific technology shocks. Journal of Political Economy 114, 413–451].

Modeling multivariate autoregressive conditional heteroskedasticity with the double smooth transition conditional correlation GARCH Model

In this paper, we propose a multivariate GARCH model with a time-varying conditional correlation structure. The new double smooth transition conditional correlation (DSTCC) GARCH model extends the smooth transition conditional correlation (STCC) GARCH model of Silvennoinen and Teräsvirta (2005) by including another variable according to which the correlations change smoothly between states of constant correlations. A Lagrange multiplier test is derived to test the constancy of correlations against the DSTCC-GARCH model, and another one to test for another transition in the STCC-GARCH framework. In addition, other specification tests, with the aim of aiding the model building procedure, are considered. Analytical expressions for the test statistics and the required derivatives are provided. Applying the model to the stock and bond futures data, we discover that the correlation pattern between them has dramatically changed around the turn of the century. The model is also applied to a selection of world stock indices, and we find evidence for an increasing degree of integration in the capital markets.

Fast rates for estimation error and oracle inequalities for model section

We consider complexity penalization methods for model selection. These methods aim to choose a model to optimally trade off estimation and approximation errors by minimizing the sum of an empirical risk term and a complexity penalty. It is well known that if we use a bound on the maximal deviation between empirical and true risks as a complexity penalty, then the risk of our choice is no more than the approximation error plus twice the complexity penalty. There are many cases, however, where complexity penalties like this give loose upper bounds on the estimation error. In particular, if we choose a function from a suitably simple convex function class with a strictly convex loss function, then the estimation error (the difference between the risk of the empirical risk minimizer and the minimal risk in the class) approaches zero at a faster rate than the maximal deviation between empirical and true risks. In this paper, we address the question of whether it is possible to design a complexity penalized model selection method for these situations. We show that, provided the sequence of models is ordered by inclusion, in these cases we can use tight upper bounds on estimation error as a complexity penalty. Surprisingly, this is the case even in situations when the difference between the empirical risk and true risk (and indeed the error of any estimate of the approximation error) decreases much more slowly than the complexity penalty. We give an oracle inequality showing that the resulting model selection method chooses a function with risk no more than the approximation error plus a constant times the complexity penalty.

An Econometric Analysis of Inter-Firm Labour Mobility

This paper utilizes the Survey of Work History (1981) data to examine the importance of non-random sampling in the context of a model of interfirm labour mobility. The paper adopts Heckman's two-step procedure in order to estimate a three-equation model incorporating an individual's mobility status as endogenously determined. The main conclusion is that in estimating wage equations it is important to consider the role of job mobility and to correct for the effects of sample-selection bias. The results generally accord with those reported by Osberg et al. (1986) in the only previous Canadian study of job mobility in a sample-selection context.

An approximate method for the solution of an influence function foil rolling model

Fleck and Johnson (Int. J. Mech. Sci. 29 (1987) 507) and Fleck et al. (Proc. Inst. Mech. Eng. 206 (1992) 119) have developed foil rolling models which allow for large deformations in the roll profile, including the possibility that the rolls flatten completely. However, these models require computationally expensive iterative solution techniques. A new approach to the approximate solution of the Fleck et al. (1992) Influence Function Model has been developed using both analytic and approximation techniques. The numerical difficulties arising from solving an integral equation in the flattened region have been reduced by applying an Inverse Hilbert Transform to get an analytic expression for the pressure. The method described in this paper is applicable to cases where there is or there is not a flat region.