The presence of nuisance parameters causes unwanted complications in statistical and econometric inference procedures. A number of modified likelihood and message length functions have been developed for better handling of nuisance parameters but they are not equally efficient. In this paper, we...

A Bayesian approach to option pricing is presented, in which posterior inference about the underlying returns process is conducted implicitly, via observed option prices. A range of models which allow for conditional leptokurtosis, skewness and time-varying volatility in returns, are considered,...

In this paper we have demonstrated the implications of incorrectly normalising the parameters of a reduced rank regression model to achieve global identification, and presented a method for estimating this model without using the ordering restrictions imposed in previous Bayesian and frequentist...

Modelling the incidence of self-employment has traditionally proved problematic. Whilst the individual supply side characteristics of the self-employed are well documented, the literature has largely neglected (or misspecified) demand side aspects. In this paper we present results from an...

We propose a new generic method ROPES (Regularized Optimization for Prediction and Estimation with Sparse data) for decomposing, smoothing and forecasting two-dimensional sparse data. In some ways, ROPES is similar to Ridge Regression, the LASSO, Principal Component Analysis (PCA) and...

This paper is concerned with model selection based on penalized maximized log likelihood function. Its main emphasis is on how these penalities might be chosen in small samples to give good statistical properties.

This paper considers residuals for time series regression. Despite much literature on visual diagnostics for uncorrelated data, there is little on the autocorrelated case. In order to examine various aspects of the fitted time series regression model, three residuals are considered. The fitted...

Two methods of identifying cointegrating vectors are commonly used: linear restrictions and the nonlinear method of Johansenos maximum likelihood procedure. That linear method can produce invalid estimates while the Johansen approach always produces valid estimates has been recognised in several...

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...

This paper presents a comprehensive empirical evaluation of option-implied and returns-based 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...