Estimating conditional probability densities for periodic variables

Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three novel techniques for tackling such problems, and investigate their performance using synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.

Estimating conditional volatility with neural networks

It is well known that one of the obstacles to effective forecasting of exchange rates is heteroscedasticity (non-stationary conditional variance). The autoregressive conditional heteroscedastic (ARCH) model and its variants have been used to estimate a time dependent variance for many financial time series. However, such models are essentially linear in form and we can ask whether a non-linear model for variance can improve results just as non-linear models (such as neural networks) for the mean have done. In this paper we consider two neural network models for variance estimation. Mixture Density Networks (Bishop 1994, Nix and Weigend 1994) combine a Multi-Layer Perceptron (MLP) and a mixture model to estimate the conditional data density. They are trained using a maximum likelihood approach. However, it is known that maximum likelihood estimates are biased and lead to a systematic under-estimate of variance. More recently, a Bayesian approach to parameter estimation has been developed (Bishop and Qazaz 1996) that shows promise in removing the maximum likelihood bias. However, up to now, this model has not been used for time series prediction. Here we compare these algorithms with two other models to provide benchmark results: a linear model (from the ARIMA family), and a conventional neural network trained with a sum-of-squares error function (which estimates the conditional mean of the time series with a constant variance noise model). This comparison is carried out on daily exchange rate data for five currencies.

Robust control of nonlinear stochastic systems by modelling conditional distributions of control signals

We introduce a novel inversion-based neuro-controller for solving control problems involving uncertain nonlinear systems that could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. In this work a novel robust inverse control approach is obtained based on importance sampling from these distributions. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The performance of the new algorithm is illustrated through simulations with example systems.

Conditional distribution modeling for estimating and exploiting uncertainty in control systems

This paper presents a general methodology for estimating and incorporating uncertainty in the controller and forward models for noisy nonlinear control problems. Conditional distribution modeling in a neural network context is used to estimate uncertainty around the prediction of neural network outputs. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localize the possible control solutions to consider. A nonlinear multivariable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non Gaussian distributions of control signal as well as processes with hysteresis.

The impact of board independence and ownership structure on the timeliness of corporate internet reporting of Irish-listed companies

Purpose – This study seeks to provide valuable new insight into the timeliness of corporate internet reporting (TCIR) by a sample of Irish-listed companies. Design/methodology/approach – The authors apply an updated version of Abdelsalam et al. TCIR index to assess the timeliness of corporate internet reporting. The index encompasses 13 criteria that are used to measure the TCIR for a sample of Irish-listed companies. In addition, the authors assess the timeliness of posting companies’ annual and interim reports to their web sites. Furthermore, the study examines the influence of board independence and ownership structure on the TCIR behaviour. Board composition is measured by the percentage of independent directors, chairman’s dual role and average tenure of directors. Ownership structure is represented by managerial ownership and blockholder ownership. Findings – It is found that Irish-listed companies, on average, satisfy only 46 per cent of the timeliness criteria assessed by the timeliness index. After controlling for size, audit fees and firm performance, evidence that TCIR is positively associated with board of director’s independence and chief executive officer (CEO) ownership is provided. Furthermore, it is found that large companies are faster in posting their annual reports to their web sites. The findings suggest that board composition and ownership structure influence a firm’s TCIR behaviour, presumably in response to the information asymmetry between management and investors and the resulting agency costs. Practical implications – The findings highlight the need for improvement in TCIR by Irish-listed companies in many areas, especially in regard to the regular updates of information provided on their web sites. Originality/value – This study represents one of the first comprehensive examinations of the important dimension of the TCIR in Irish-listed companies.

Using monthly returns to model conditional heteroscedasticity

This empirical study examines the extent of non-linearity in a multivariate model of monthly financial series. To capture the conditional heteroscedasticity in the series, both the GARCH(1,1) and GARCH(1,1)-in-mean models are employed. The conditional errors are assumed to follow the normal and Student-t distributions. The non-linearity in the residuals of a standard OLS regression are also assessed. It is found that the OLS residuals as well as conditional errors of the GARCH models exhibit strong non-linearity. Under the Student density, the extent of non-linearity in the GARCH conditional errors was generally similar to those of the standard OLS. The GARCH-in-mean regression generated the worse out-of-sample forecasts.

Conditional volatility and firm size:an empirical analysis of UK equity portfolios

The techniques and insights from two distinct areas of financial economic modelling are combined to provide evidence of the influence of firm size on the volatility of stock portfolio returns. Portfolio returns are characterized by positive serial correlation induced by the varying levels of non-synchronous trading among the component stocks. This serial correlation is greatest for portfolios of small firms. The conditional volatility of stock returns has been shown to be well represented by the GARCH family of statistical processes. Using a GARCH model of the variance of capitalization-based portfolio returns, conditioned on the autocorrelation structure in the conditional mean, striking differences related to firm size are uncovered.

Primary production, financial constraints and economic instability:Zambia since independence.

A systematic analysis is presented of the economic consequences of the abnormally high concentration of Zambia's exports on a commodity whose price is exceptionally unstable. Zambian macro-economic variables in the post-independence years are extensively documented, showing acute instability and decline, particularly after the energy price revolution and the collapse of copper prices. The relevance of stabilization policies designed to correct short-term disequilibrium is questioned. It is, therefore, a pathological case study of externally induced economic instability, complementing other studies in this area which use cross-country analysis of a few selected variables. After a survey of theory and issues pertaining to development, finance and stabilization, the emergence of domestic and foreign financial constraints on the Zambian economy is described. The world copper industry is surveyed and an examination of commodity and world trade prices concludes that copper showed the highest degree of price instability. Specific aspects of Zambia's economy identified for detailed analysis include: its unprofitable mining industry, external payments disequilibrium, a constrained government budget, potentially inflationary monetary growth, and external indebtedness. International comparisons are used extensively, but major copper exporters are subjected to closer scrutiny. An appraisal of policy options concludes the study.

Corporate governance, managers' independence, exporting, and performance of firms in transition economies

Using data on 157 large companies in Poland and Hungary, this paper employs Bayesian structural equation modeling to examine the relations among corporate governance, managers' independence from owners in terms of strategic decision making, exporting, and performance. Managers' independence is positively associated with firms' financial performance and exporting. In turn, the extent of managers' independence is negatively associated with ownership concentration, but positively associated with the percentage of foreign directors on the firm's board. We interpret these results as indicating that concentrated owners tend to constrain managerial autonomy at the cost of the firm's internationalization and performance, but board participation of foreign stakeholders enhances the firm's export orientation and performance by encouraging executives' decision-making autonomy.

Corporate governance, managers' independence, exporting and performance of firms in transition economies

Using data on 157 large companies in Poland and Hungary this paper employs Bayesian structural equation modeling to examine interrelationships between corporate governance, managers' independence from owners in terms of strategic decision-making, exporting and performance. It is found that managers' independence is positively associated with firms' financial performance and exporting. In turn, the extent of managers' independence is contingent on the firm's corporate governance parameters: it is negatively associated with ownership concentration, but positively associated with the percentage of foreign directors on the firm's board. We interpret these results as an indication that (i) risk averse, concentrated owners tend to constrain managerial autonomy at the cost of the firm's internationalization and performance, (ii) board participation of foreign stakeholders, on the other hand, enhances the firm's export orientation and performance by encouraging executives' decision-making autonomy.