standard inference from least-squares estimation of a suitably adjusted predictive regression. We analyze US and international …

We develop a new variational Bayes estimation method for large-dimensional sparse vector autoregressive models with …, both in simulation and when forecasting a large cross section of industry portfolios spanning almost a hundred years of …

realized covariance matrices through a GARCH-type structure. We compare the forecasting performance of several such models in …

volatility information improves the day volatility estimation. The results indicate a forecasting improvement using bivariate … leverage effect and maintains a mathematical structure that facilitates volatility estimation. A class of bivariate models that …

. Two approaches based on the extreme value theory were compared: Block Maxima and the Peaks Over Threshold. Forecasts were …

realized correlations in the DCC-HEAVY model. The new model removes well known asymptotic bias in DCC-GARCH model estimation … and has more desirable asymptotic properties. We also derive a Quasi-maximum likelihood estimation and provide closed …

based on two S&P 500 cash index out-of-sample forecasting periods, one of which covers exclusively the recent 2007 … realized volatility and the augmented GARCH models with the FHS or the EVT quantile estimation methods produce superior VaR …

augments the prediction problem by covariate forecasting models. In this paper, we present simple alternatives for multi …

occur. Two important statistical models for change point detection and prediction are the regime-switching and threshold … different regimes. In a threshold model, change is detected as soon as a split variable passes a threshold. In this paper, by … combining the two mentioned models, namely regime switching and threshold, an EWS for change point detection is designed. The …