Theodoros Moysiadis

On binary and categorical time series models with feedback

We study the problem of ergodicity, stationarity and maximum likelihood estimation for multinomial logistic models that include a latent process. Our work includes various models that have been proposed for the analysis of binary and, more general, categorical time series. We give verifiable ergodicity and stationarity conditions for the analysis of such time series data. In addition, we study maximum likelihood estimation and prove that, under mild conditions, the estimator is asymptotically normally distributed. These results are applied to real and simulated data.

On Locally Dyadic Stationary Processes

We introduce the concept of local dyadic stationarity, to account for nonstationary time series, within the framework of Walsh–Fourier analysis. We define and study time-varying, dyadic, autoregressive, moving average (tvDARMA) models. It is proven that the general tvDARMA process can be approximated locally by either a time-varying dyadic moving average and a time-varying dyadic autoregressive processes.

Credit-scoring and bank lending policy in consumer loans

The objective of this paper is to estimate a credit-scoring model for the consumer loans during the period 2007–2009. A probit model per year is estimated to examine, whether the probability of granting a loan changes through years. Moreover through a bivariate probit model, we analysed how the borrower’s characteristics influence the decision of granting consumer loans and their performance. Eventually, we demonstrated that the financial institutions’ lending policies are compatible with default risk minimisation and thus it is important for banks to review the borrowers’ creditworthiness periodically, in order to minimise default risk.