Suchergebnisse

PurposeThe purpose of this paper is to propose and test empirically an inflation model containing permanent and transitory heteroskedastic components for the G7 countries. More specifically, recent evidences from the literature are gathered to construct a model with a heteroskedastic global component capturing comovements amongst G7 economies. Moreover, evidence of asymmetric generalized autoregressive conditionally heteroskedastic effects both in the transitory and in the permanent components are taken into account, and the time‐varying variance of each component allows their influence over the observable inflation to change over time. Out‐of‐sample forecasting exercises are used to test the model validity.Design/methodology/approachThe model is written in state‐space form and estimation is carried out in one step via quasi‐maximum likelihood using the augmented Kalman filter, which allows us to compute smoothed estimates of permanent and of transitory components of inflation rates. Out‐of‐sample forecasts are compared against a random walk (RW) and an autoregressive (AR) model of order one. The significance of the differences in forecast accuracy is tested using the Diebold‐Marino test, the forecast encompassing test, and the Pesaran and Timmermann test.FindingsThe proposed model fits the data quite well and has good forecasting capabilities when compared to RW and to AR models of order one. The volatility of the global inflation trend extracted from the model captures the international effects of the "Great Moderation" and of the "Great Recession". An increase in correlation of inflation for certain country pairs since the start of the "Great Recession" is observed. Moreover, there is evidence of asymmetry in inflation volatility, which is consistent with the idea that higher inflation levels lead to greater uncertainty about future inflation.Originality/valueThis article introduces a new global inflation model with permanent and transitory heteroskedastic components incorporating many recent findings of the literature, and proposes a one step estimation procedure for it. The model fits very well the data and produces good out‐of‐sample forecasts.

We use panel probit models with unobserved heterogeneity, state-dependence and serially correlated errors in order to analyze the determinants and the dynamics of current-account reversals for a panel of developing and emerging countries. The likelihood-based inference of these models requires high-dimensional integration for which we use Efficient Importance Sampling (EIS). Our results suggest that current account balance, terms of trades, foreign reserves and concessional debt are important determinants of current-account reversal. Furthermore, we find strong evidence for serial dependence in the occurrence of reversals. While the likelihood criterion suggest that state-dependence and serially correlated errors are essentially observationally equivalent, measures of predictive performance provide support for the hypothesis that the serial dependence is mainly due to serially correlated country-specific shocks related to local political or macroeconomic events.

Modelling and forecasting high dimensional covariance matrices is a key challenge in data-richenvironments involving even thousands of time series since most of the available models sufferfrom the curse of dimensionality. In this paper, we challenge some popular multivariate GARCH(MGARCH) and Stochastic Volatility (MSV) models by fitting them to forecast the conditionalcovariance matrices of financial portfolios with dimension up to 1000 assets observed daily over a30-year time span. The time evolution of the conditional variances and covariances estimated bythe different models is compared and evaluated in the context of a portfolio selection exercise. Weconclude that, in a realistic context in which transaction costs are taken into account, modelling thecovariance matrices as latent Wishart processes delivers more stable optimal portfolio compositionsand, consequently, higher Sharpe ratios. ; Guilherme V. Moura is supported by the Brazilian Government through grants number 424942- 2016-0 (CNPQ) and 302865-2016-0 (CNPQ). André A.P. Santos is supported by the Brazilian Government through grants number 303688-2016-5 (CNPQ) and 420038-2018-3 (CNPQ). Esther Ruiz is supported by the Spanish Government through grant number ECO2015-70331-C2-2-R (MINECO/FEDER).