applied to quarterly and monthly US inflation in an empirical study. We find that the persistence of quarterly inflation has … and density forecasts for monthly US inflation. …

-likelihood decomposition, which is an alternative to the classic prediction-error decomposition for linear Gaussian models. Simulation studies …

The papers in this special issue of Mathematics and Computers in Simulation are substantially revised versions of the … under the Basel Accord: A Bayesian approach to forecasting value-at-risk of VIX futures, fast clustering of GARCH processes … dynamics of BRICS's country risk ratings and domestic stock markets, U.S. stock market and oil price, forecasting value …

provide a theoretical justification of the use of random subspace methods and show their usefulness when forecasting monthly …

surface types. We finally show that our proposed model can also be effective in forecasting. We provide evidence that our … model significantly outperforms existing models in the forecasting of tennis match results. …

short-term forecasting models. These empirical findings have been established for different macroeconomic data sets and … specification is most effective in its forecasting performance. Furthermore, the forecast performances of the different … extended empirical out-of-sample forecasting competition for quarterly growth of gross domestic product in the euro area and …

The paper investigates the impact of jumps in forecasting co-volatility, accommodating leverage effects. We modify the … forecasting weekly and monthly horizons. …

We present an accurate and efficient method for Bayesian forecasting of two financial risk measures, Value-at-Risk and …