<b> </b> For situations with a large number of series, N, each with T observations and each containing a certain amount of information for prediction of the variable of interest, we propose a new statistical modelling methodology that first estimates the common factors from a panel of data using...

Many empirical time series such as asset returns and traffic data exhibit the characteristic of time-varying conditional covariances, known as volatility or conditional heteroscedasticity. Modeling multivariate volatility, however, encounters several difficulties, including the curse of...

In this article, we propose a predictive mean squared error criterion for selecting diffusion index models, which are useful in forecasting when many predictors are available. A special feature of the proposed criterion is that it takes into account the uncertainty in estimated common factors....

We present a methodology for rating in real-time the creditworthiness of public companies in the U.S. from the prices of traded assets. Our approach uses asset pricing data to impute a term structure of risk neutral survival functions or default probabilities. Firms are then clustered into...

We extend Ohlson's (1995) model and examine the relationship between returns and residual income that incorporate analysts' earnings forecasts and other non-earnings information variables in the balance sheet, namely default probability and agency cost of a debt covenant contract. We further...

We present a general class of nonlinear time series Markov regime-switching models for seasonal data which may exhibit periodic features in the hidden Markov process as well as in the laws of motion in each of the regimes. This class of models allows for nontrivial dependencies between seasonal,...

Correlations between asset returns are important in many financial applications. In recent years, multivariate volatility models have been used to describe the time-varying feature of the correlations. However, the curse of dimensionality quickly becomes an issue as the number of correlations is...