There is a growing literature on the realized volatility (RV) forecasting of asset returns using high-frequency data. We explore the possibility of forecasting RV with factor analysis; once considering the significant jumps. A real high-frequency financial data application suggests that the...

This paper assesses the forecasting performance of various variable reduction and variable selection methods. A small and a large set of wisely chosen variables are used in forecasting the industrial production growth for four Euro Area economies. The results indicate that the Automatic Leading...

The online Supplement presents the proof the auxiliary Lemmas 1-6, the entire set of tables with results from the Monte Carlo and the empirical studies, and further discussion on selected topics.Full paper is available at: 'https://ssrn.com/abstract=2707176' https://ssrn.com/abstract=2707176

We address the issue of modelling and forecasting macroeconomic variables using rich datasets by adopting the class of Vector Autoregressive Moving Average (VARMA) models. We overcome the estimation issue that arises with this class of models by implementing an iterative ordinary least squares...

A financial conditions index (FCI) is designed to summarise the state of financial markets. We construct two with UK data. The first is the first principal component (PC) of a set of financial indicators. The second comes from a new approach taking information from a large set of macroeconomic...

Forecasts play a critical role at inflation-targeting central banks, such as the Bank of England. Breaks in the forecast performance of a model can potentially incur important policy costs. Commonly used statistical procedures, however, implicitly put a lot of weight on type I errors (or false...

By employing large panels of survey data for the UK economy, we aim at reviewing linear approaches for regularisation and dimension reduction combined with techniques from the machine learning literature, like Random Forests, Support Vector Regressions and Neural Networks for forecasting GDP...

We forecast CPI inflation in the United Kingdom up to one year ahead using a large set of monthly disaggregated CPI item series combined with a wide set of forecasting tools, including dimensionality reduction techniques, shrinkage methods and non-linear machine learning models. We find that...

We propose a new approach to forecasting the term structure of interest rates, which allows to efficiently extract the information contained in a large panel of yields. In particular, we use a large Bayesian Vector Autoregression (BVAR) with an optimal amount of shrinkage towards univariate AR...

Factor based forecasting has been at the forefront of developments in the macroeconometric forecasting literature in the recent past. Despite the flurry of activity in the area, a number of specification issues such as the choice of the number of factors in the forecasting regression, the...