A Latent Factor Model for Forecasting Realized Variances

This article proposes a parsimonious model to forecast large vectors of realized variances (RVar) by exploiting their common dynamics within a latent factor structure. Their long persistence is captured by aggregating latent factors with AR(1) dynamics. The model has obvious advantages over standard autoregressive models not only in terms of parametrization, but also in terms of efficiency, when increasing the dimension of the vector, as it provides more information on the commonality of the series’ dynamics. The model easily accommodates further empirical features of RVars, such as conditional heteroskedasticity. For estimation purposes, we use the maximum likelihood method based on Kalman filter and the efficient method of moments, both being easy to implement and providing accurate estimates. Our empirical illustration on real data shows that the model we propose often outperforms standard models, most of which are, for vectors of RVar series, only implementable under heavy parametric restrictions.

A threshold mixed count time series model: estimation and application

A new class of integer time series models is proposed to capture the dynamic transmission of count processes over time. The approach extends existing integer mixed autoregressive-moving average models (INARMA) by allowing for shifts in the dynamics of the count process through regime changes, referred to as a threshold integer autoregressive-moving average model (TINARMA). An efficient method of moments estimator is proposed, with standard errors based on subsampling, as maximum likelihood methods are infeasible for TINARMA processes. Applying the framework to global banking crises over 200 years of data, the empirical results show strong evidence of autoregressive and moving average dynamics which vary across systemic and nonsystemic regimes over time. Coherent forecast distributions are also produced with special attention given to the Great Depression and the more recent Global Financial Crisis.