Session 1: Forecasting

Chair:  Pär Österholm

Jamie L. Cross: Macroeconomic Forecasting with Large Stochastic Volatility in Mean VARs

Abstract: Vector autoregressions with stochastic volatility in both the conditional mean and variance are commonly used to estimate the macroeconomic effects of uncertainty shocks. Despite their popularity, intensive computational demands when estimating such models have made out-of-sample forecasting exercises impractical, particularly when working with large data sets. In this article, we propose an efficient Markov chain Monte Carlo (MCMC) algorithm for posterior and predictive inference in such models that facilitates such exercises. The key insight underlying the algorithm is that the (log-)conditional densities of the log volatilities possess Hessian matrices that are banded. This enables us to build upon recent advances in band and sparse matrix algorithms for state space models. In a simulation exercise, we evaluate the new algorithm numerically and establish its computational and statistical efficiency over a conventional particle filter based algorithm. Using macroeconomic data for the US we find that such models generally deliver more accurate point and density forecasts over a conventional benchmark in which stochastic volatility only enters the variance of the model.

Niko Hauzenberger: General Bayesian time-varying parameter VARs for predicting government bond yields

Abstract: This paper proposes a vector autoregressive model with time- varying parameters and stochastic volatility which treats the nature of parameter dynamics as unknown. Coefficients are allowed to evolve according to a random walk, a Markov switching process and/or depend on further predictors. Estimation and inference relies on Bayesian methods. Overfitting issues are tackled through careful model specification and flexible shrinkage priors. As an empirical application, we forecast the US term structure of interest rates and show that our approach performs well relative to a set of competing models, and how the model can be used to explain structural breaks in coefficients related to the US yield curve.

Yong Song: Identification and Forecasting of Bull and Bear Markets using Multivariate Returns

Abstract: Bull and bear market identification generally focuses on a broad index of returns through a univariate analysis. This paper proposes a new approach to identify and forecast bull and bear markets through multivariate returns. The model assumes all assets are directed by a common discrete state variable from a hierarchical Markov switching model. The hierarchical specification allows the cross-section of state specific means and variances to differ over bull and bear markets. We investigate several empirically realistic specifications that permit feasible estimation even with 100 assets. Our results show that the multivariate framework provides competitive bull and bear regime identification and improves portfolio performance and density prediction compared to several benchmark models including univariate Markov switching models.