We propose using a Bayesian time-varying coefficient model estimated with Markov chain-Monte Carlo methods to measure contagion empirically. The proposed measure works in the joint presence of heteroskedasticity and omitted variables and does not require knowledge of the timing of the crisis. It distinguishes contagion not only from interdependence but also from structural breaks and can be used to investigate positive as well as negative contagion. The proposed measure appears to work well using both simulated and actual data.
A stochastic volatility model where volatility was driven solely by a latent variable called news was estimated for three stock indices. A Markov chain Monte Carlo algorithm was used for estimating Bayesian parameters and filtering volatilities. Volatility persistence being close to one was consistent with both volatility clustering and mean reversion. Filtering showed highly volatile markets, reflecting frequent pertinent news. Diagnostics showed no model failure, although specification improvements were always possible. The model corroborated stylized findings in volatility modeling and has potential value for market participants in asset pricing and risk management, as well as for policymakers in the design of macroeconomic policies conducive to less volatile financial markets.
We use a Bayesian approach to estimate a standard two-country New Open Economy Macroeconomics model using data for the United States and the euro area, and we perform model comparisons to study the importance of departing from the law of one price and complete markets assumptions. Our results can be summarized as follows. First, we find that the baseline model does a good job in explaining real exchange rate volatility but at the cost of overestimating volatility in output and consumption. Second, the introduction of incomplete markets allows the model to better match the volatilities of all real variables. Third, introducing sticky prices in Local Currency Pricing improves the fit of the baseline model but does not improve the fit as much as introducing incomplete markets. Finally, we show that monetary shocks have played a minor role in explaining the behavior of the real exchange rate, while both demand and technology shocks have been important.
This paper investigates the channels through which remittances affect macroeconomic volatility in African countries using a dynamic stochastic general equilibrium (DSGE) model augmented with financial frictions. Empirical results indicate that remittances—as a share of GDP—have a significant smoothing impact on output volatility but their impact on consumption volatility is somewhat small. Furthermore, remittances are found to absorb a substantial amount of GDP shocks in these countries. An investigation of the theoretical channels shows that the stabilization impact of remittances essentially hinges on two channels: (i) the size of the negative wealth effect on labor supply induced by remittances and, (ii) the strength of financial frictions and the ability of remittances to alleviate these frictions.