Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag (ADL) model is chosen. The results show that a...

This paper proposes full-Bayes priors for time-varying parameter vector autoregressions (TVP-VARs) which are more robust and objective than existing choices proposed in the literature. We formulate the priors in a way that they allow for straightforward posterior computation, they require...

Purpose – Economic cross-linkages and the increased co-movement of asset prices across international markets are important outcomes as the result of globalization. Hereby, the nature of international stock markets and the extent to which the 1997-1998 East Asian turmoil had affected the market...

This draft is a summary of the paper entitled: Forecasting Fuel Prices with the Chilean Exchange Rate. In that paper we show that the Chilean exchange rate has the ability to predict the returns of oil prices and of three additional oil-related products: gasoline, propane and heating oil. The...

ARFIMAX models are applied in estimating the intra-day realized volatility of the CAC40 and DAX30 indices. Volatility clustering and asymmetry characterize the logarithmic realized volatility of both indices. ARFIMAX model with time-varying conditional heteroscedasticity is the best performing...

This paper examines the behaviour of high and low prices of four commodities, namely crude oil, natural gas, gold and silver, and of the corresponding ranges using both daily and intraday data at various frequencies. For this purpose, it applies fractional integration and cointegration...

ARCH models for the daily S&P500 log-returns are estimated, whereas the intraday prices comprise the dataset for an ARFIMAX model. Model’s forecasting performance is statistically superior when the CBOE’s VIX index is incorporated as an explanatory variable.

Academics and practitioners have extensively studied Value-at-Risk (VaR) to propose a unique risk management technique that generates accurate VaR estimations for long and short trading positions. However, they have not succeeded yet as the developed testing frameworks have not been widely...

This paper seeks to understand the long memory behaviour of global equity returns using novel methods from wavelet analysis. We implement the wavelet based multivariate long memory approach, which possibly is the first application of wavelet based multivariate long memory technique in finance...

This study investigates the long range dependence and correlation structures of some select stock markets. Using novel wavelet methods of long range dependence, we show presence of long memory in the stock returns of some emerging economies and the lack of it in developed markets of Europe and...