This paper proposes a couple of new methods to compute the news impact curve for stochastic volatility (SV) models. The new methods incorporate the joint movement of return and volatility, which has been ignored by the extant literature. The first method employs the Bayesian Markov chain Monte...

We generalise the impulse response function of Elder (2003) by considering indirect volatility spillovers for a VAR model with multivariate GARCH-in-Mean. The extension is relevant for variables that exhibit direct and indirect volatility spillovers (Tsiaplias and Chua, in press).

The present paper shows that a DSGE model can be represented by a finite order VAR if and only if the eigenvalues of the matrix defined in Fernández-Villaverde et al. (2007) are all equal to zero. Further it shows that this condition is equivalent to the unimodularity condition presented...

This paper considers Bayesian variable selection in regressions with a large number of possibly highly correlated macroeconomic predictors. I show that acknowledging the correlation structure in the predictors can improve forecasts over existing popular Bayesian variable selection algorithms.

We investigate the time series properties of both filtered and unfiltered real exchange rate series produced by DSGE models that feature local currency pricing, home bias, nontraded goods, and incomplete markets. Detrended series produced by several specifications approach the empirically...

This paper considers a multivariate extension of the test for neglected nonlinearity proposed by Tsay (1986) that uses principal components to overcome the problem of dimensionality that is common with tests of this type. Monte Carlo experiments reveal that the modified multivariate test...

Using a new uncertainty index from Baker et al. (2012), we evaluate the time-varying correlation between macroeconomic uncertainty, inflation, and output. Estimation results from a multivariate DCC-GARCH model reveal that the sign of the correlation between macroeconomic uncertainty and...

We show how multivariate GARCH models can be used to generate a time-varying “information share” (Hasbrouck, 1995) to represent the changing patterns of price discovery in closely related securities. We find that time-varying information shares can improve credit spread predictions.

This paper applies the 0–1 test for chaos to returns from the German stock market, providing empirical evidence of chaotic structures in the returns of all DAX members. For noise reduction purposes, wavelet denoising is employed prior to the application of the 0–1 test.

This paper considers the theoretical justifications of Lütkpohl’s (1988) test statistics when the data-generating process is relaxed to be a stationary ARFIMA process. Under suitable regularity conditions, we prove the applicability of Lütkpohl’s (1988) method to the stationary ARFIMA (p,...