Decomposing volatilities into a common market-driven component and an idiosyncratic itemspecific one is an important issue in financial econometrics. This, however, requires the statistical analysis of large panels of time series, hence faces the usual challenges associated with highdimensional data. Factor model methods in such a context are an ideal tool, but they do not readily apply to the ...

This paper studies the monetary transmission mechanism in the U.S. It proposes a mixed-frequency version of the factor-augmented vector autoregressive regression (FAVAR) model, which is used to construct a coincident index to measure the monetary transmission mechanism. The model divides the transmission of changes in monetary policy to the economy into three stages according to the timing and ...

We measure global real and nominal macroeconomic uncertainty and analyze its impact on individual countries’ macroeconomic performance. Global uncertainty is measured through the conditional variances of global factors in inflation and output growth, estimated from a bivariate dynamic factor model with GARCH errors. The impact of global uncertainty is measured by including the conditional varia...

In this paper, we investigate the asymmetry in the tail dependence between US equity portfolios and the aggregate US market. Given the limited number of observations in the tails of a joint distribution, standard non-parametric measures of tail dependence often have poor finite-sample properties. We therefore develop a parametric model for measuring and testing asymmetry in tail dependence, usi...

In this paper, we propose new tests for threshold cointegration in the conditional autoregressive distributed lag (ADL) model. The indicators in the threshold model are based on either a nonstationary or stationary threshold variable. The proposed tests are appropriate when the conditioning variables are weakly exogenous. The cointegrating vector in this paper is not pre-specified. We adopt a s...

We introduce the Multivariate Autoregressive Conditional Double Poisson model to deal with discreteness, overdispersion and both auto and cross-correlation, arising with multivariate counts. We model counts with a double Poisson and assume that conditionally on past observations the means follow a Vector Autoregression. We resort to copulas to introduce contemporaneous correlation. We apply it ...

Value at Risk (VaR) has become the industry standard to measure the market risk. However, the selection of the VaR models is controversial. Simulation Results indicate Historical Simulation has significant positive bias, while GARCH (1,1) has has significant negative bias. Also HS adapts structural change slowly but stable, while GARCH adapts structural break rapidly but less stable. Thus the m...

This paper intends to harmonize two different approaches to the analysis of the business cycle and in doing so it retrieves the stylized facts of the business cycle in Europe. We start with the ‘classical’ approach proposed in Burns and Mitchell (1946) of dating and analyzing the business cycle; we then adopt the ‘modern’ alternative: the Markov-switching time series model proposed in Hamilton ...

This paper proposes a Bayesian approach to assess if the data support candidate set-identifying restrictions for Vector Autoregressive models. The researcher is uncertain about the validity of some sign restrictions that she is contemplating to use. She therefore expresses her uncertainty with a prior distribution that covers the parameter space both where the restrictions are satisfied and whe...

Article history: Received 11 October 2012 Received in revised form 3 November 2014 Accepted 3 November 2014 Available online 11 November 2014 In this paper, we investigate the dynamic response of stock market volatility to changes in monetary policy. Using a vector autoregressive model, our findings reveal a significant response of stock returns and volatility to monetary policy shocks. While t...