This paper presents a comprehensive empirical evaluation of option-implied and returns-based forecasts of volatility, in which recent developments related to the impact on measured volatility of market microstructure noise are taken into account. The paper also assesses the robustness of the performance of the option-implied forecasts to the way in which those forecasts are extracted from the option market. Using a test for superior predictive ability, model-free implied volatility, which aggregates information across the volatility 'smile', and at-the-money implied volatility, which ignores such information, are both tested as benchmark forecasts. The forecasting assessment is conducted using intraday data for three Dow Jones Industrial Average (DJIA) stocks and the S&P500 index over the 1996-2006 period, with future volatility proxied by a range of alternative noise-corrected realized measures. The results provide compelling evidence against the model-free forecast, with its poor performance linked to both the bias and excess variability that it exhibits as a forecast of actual volatility. The positive bias, in particular, is consistent with the option market factoring in a substantial premium for volatility risk. In contrast, implied volatility constructed from liquid at-the-money options is given strong support as a forecast of volatility, at least for the DJIA stocks. Neither benchmark is supported for the S&P500 index. Importantly, the qualitative results are robust to the measure used to proxy future volatility, although there is some evidence to suggest that any option-implied forecast may perform less well in forecasting the measure that excludes jump information, namely bi-power variation.

in recenT years, there has been considerable governmental focus on increasing the financial literacy of Australians, notably the work undertaken by the Financial Literacy Foundation (2007). This paper examines the extent to which, and among which demographic groups, inadequate financial literacy plays a role in perpetuating financial hardship. It investigates the causes of financial hardship among individuals with low incomes and the role played by financial counsellors in providing financial literacy advice. Gippsland, a rural and regional area comprising south-eastern Victoria, was chosen as the focus for this study. The term ‘financial hardship’ is used synonymously in the literature with terms such as financial distress, financial constraint, financial fragility or difficulty (for example, Allen Consulting Group 2008; State Services Authority 2008; La Cava and Simon 2005; ABS 2004; and Worthington 2006). Those likely to suffer from financial distress include: individuals with low incomes; one-parent families; indigenous Australians; and those with no earned income (ABS 2004). La Cava and Simon (2005) list various dimensions of financial stress related to the inability to pay household bills (utility, registration and insurance) and to a shortage of money leading to households pawning or selling items, going without meals, being unable to heat their homes, seeking assistance from welfare organisations or seeking assistance from family or friends. A definition of ‘financial literacy’ widely used in the literature (ANZ and ACNielsen 2008 and 2005; Hartley and Horne 2006; Coben, Dawes and Lee 2005) is ‘the ability to make informed judgments and to take effective decisions regarding the use and management of money’ (Noctor, Stoney and Stradling 1992). However, as Balatti (2007) points out, the ‘ability required’ differs between individuals according to the real-life situation each faces. For example, it may be different for low and high-income situations. Individuals more likely to exhibit inadequate financial literacy skills include: those with education below Year 10; unskilled and non-workers; people with household incomes less than $400 per week; younger adults (18 to 24 years); those over 70 years; people who are renting; and marginalised women (Consumer and Financial Literacy Taskforce 2004; ANZ and ACNielsen 2005; RPR Consulting 2007). This paper reports on a study of the clients of a financial counselling agency, Anglicare Victoria, Gippsland. As only individuals who ‘sought assistance from a welfare organisation’ are included, all are classified as financially stressed under La Cava and Simon’s (2005) definition. The data set also includes those most likely to lack adequate financial literacy skills, as identified by the Consumer and Financial Literacy Taskforce (2004). Financial counselling in Victoria is undertaken by a wide range of organisations, including Anglicare Victoria. Such organisations frequently operate as an agency for a judy TennanT is a lecturer in the school of Business and economics at monash university. email: judith.Tennant@buseco. monash.edu.au

In this paper Bayesian methods are applied to a stochastic volatility model using both the prices of the asset and the prices of options written on the asset. Posterior densities for all model parameters, latent volatilities and the market price of volatility risk are produced via a Markov Chain Monte Carlo (MCMC) sampling algorithm. Candidate draws for the unobserved volatilities are obtained in blocks by applying the Kalman filter and simulation smoother to a linearization of a nonlinear state space representation of the model. Crucially, information from both the spot and option prices affects the draws via the specification of a bivariate measurement equation, with implied Black–Scholes volatilities used to proxy observed option prices in the candidate model. Alternative models nested within the Heston (1993) framework are ranked via posterior odds ratios, as well as via fit, predictive and hedging performance. The method is illustrated using Australian News Corporation spot and option price data.

This paper presents a comprehensive empirical evaluation of option-implied and returnsbased forecasts of volatility, in which new developments related to the impact on measured volatility of market microstructure noise and random jumps are explicitly taken into account. The option-based component of the analysis also accommodates the concept of model-free implied volatility, such that the forecasting performance of the options market is separated from the issue of misspecification of the option pricing model. The forecasting assessment is conducted using an extensive set of observations on equity and option trades for News Corporation for the 1992 to 2001 period, yielding certain clear results. According to several different criteria, the model-free implied volatility is the best performing forecast, overall, of future volatility, with this result being robust to the way in which alternative measures of future volatility accommodate microstructure noise and jumps. Of the volatility measures considered, the one which is, in turn, best forecast by the option-implied volatility is that measure which adjusts for microstructure noise, but which retains some information about random jumps.

In this paper Bayesian methods are applied to a stochastic volatility model using both the prices of the asset and the prices of options written on the asset. Posterior densities for all model parameters, latent volatilities and the market price of volatility risk are produced via a hybrid Markov Chain Monte Carlo sampling algorithm. Candidate draws for the unobserved volatilities are obtained by applying the Kalman filter and smoother to a linearization of a state-space representation of the model. The method is illustrated using the Heston (1993) stochastic volatility model applied to Australian News Corporation spot and option price data. Alternative models nested in the Heston framework are ranked via Bayes Factors and via fit, predictive and hedging performance.