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.

The segmentation problem arises in many applications in data mining, A.I. and statistics. In this paper, we consider segmenting simple time series. We develop two Bayesian approaches for segmenting a time series, namely the Bayes Factor approach, and the Minimum Message Length (MML) approach. We...

In this paper, a Bayesian version of the exponential smoothing method of forecasting is proposed. The approach is based on a state space model containing only a single source of error for each time interval. This model allows us to improve current practices surrounding exponential smoothing by...

We propose an innovations form of the structural model underlying exponential smoothing that is further augmented by a latent Markov switching process. A particular case of the new model is the local level model with a switching drift, where the switching component describes the change between...

In this paper we apply Bayesian methods to estimate a stochastic volatility model using both the prices of the asset and the prices of options written on the asset. Implicit posterior densities for the parameters of the volatility model, for the latent volatilities and for the market price of...

A Bayesian estimation procedure is developed for estimating multiple regime vector autoregressive models appropriate for deviations from financial arbitrage relationships. This approach has clear advantages over classical stepwise threshold autoregressive analysis.

The basic ideals underlying the Kalman filter are outlined in this paper without direct recourse to the complex formulae normally associated with this method. The novel feature of the paper is its reliance on a new algebraic system based on the first two moments of the multivariate normal...

Several authors have postulated econometric models for exchange rates restricted to lie within known target zones. However, it is not uncommon to observe exchange rate data with known limits that are not fully 'credible'; that is, where some of the observations fall outside the stated range. An...

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,...