terms of forecasting performance. Considering the gap in the literature, this study focuses on the Turkish stock and FX …

Based on recent evidence of fractional cointegration in commodity spot and futures markets, we investigate whether a …. Specifically, we propose to model and forecast commodity spot and futures prices using a fractionally cointegrated vector …. We derive the best linear predictor forecast for this model and perform an out-of-sample forecast comparison with …

We extend the analysis of Christoffersen and Diebold (1998) on long-run forecasting in cointegrated systems to … multicointegrated systems. For the forecast evaluation we consider several loss functions, each of which has a particular interpretation … square forecast error (MSFE) criterion focuses on the forecast errors of the flow variables alone. Likewise, a loss function …

Interest rate data are an important element of macroeconomic forecasting. Projections of future interest rates are not … only an important product themselves, but also typically matter for forecasting other macroeconomic and financial variables …. A popular class of forecasting models is linear vector autoregressions (VARs) that include shorter- and longer …

frequency information into one model. We consider subsample averaging, bootstrap averaging, forecast averaging methods for the … forecasting the daily S&P 500 index return quantile (Value-at-Risk or VaR is simply the negative of it), using high …-frequency information is beneficial, often substantially and particularly so, in forecasting downside risk. Our empirical results show that …

AutoRegression (VAR) and a fully structural Dynamic Stochastic General Equilibrium (DSGE) model, at forecasting financial returns. We … show that the DSGE model outperforms the unrestricted VAR at forecasting financial returns in the long term and generates …