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...

This paper extends the Bayesian Model Averaging framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited Information Bayesian Model Averaging (LIBMA) methodology and then test it using simulated data. Simulation...

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...

We examine the linkages between market and funding liquidity pressures, as well as their interaction with solvency issues surrounding key financial institutions during the 2007 subprime crisis. A multivariate GARCH model is estimated in order to test for the transmission of liquidity shocks...

Empirical evidence on the distribution of relative price changes almost invariably reveals high kurtosis and a tendency toward right-skewness. Simple mixed distribution models including volatile and infrequently adjusted prices can account for these and other common features, such as correlation...

Hong Kong SAR's government faces the dual challenges of volatile revenue and medium term spending pressures arising from a rapidly aging population. Age-related spending pressures raise long-run sustainability concerns, while revenue volatility creates risks to service provision, possibly...

We provide empirical evidence that deviations from uncovered interest rate parity (UIP) display significant nonlinearities, consistent with theories based on transaction costs or limits to speculation. This evidence suggests that the forward bias documented in the literature may be less...