Abstract not found

In 2008 all ECB publications feature a motif taken from the €10 banknote.

In this paper we investigate whether and how far density forecasts sensibly can be combined to produce a “better ” pooled density forecast. In so doing we bring together two important but hitherto largely unrelated areas of the forecasting literature in economics, density forecasting and forecast combination. We provide simple Bayesian methods of pooling information across alternative density forecasts. We illustrate the proposed techniques in an application to two widely used published density forecasts for U.K. inflation. We examine whether in practice improved density forecasts for inflation, one year ahead, might have been obtained if one had combined the Bank of England and NIESR density forecasts or “fan charts”. 1

Abstract not found

This paper considers a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by Engle (2002), and suggests the use of devolatized returns computed as returns standardized by realized volatilities rather than by GARCH type volatility estimates. The t-DCC estimation procedure is applied to a portfolio of daily returns on currency futures, government bonds and equity index futures. The results strongly reject the normal-DCC model in favour of a t-DCC speci…cation. The t-DCC model also passes a number of VaR diagnostic tests over an evaluation sample. The estimation results suggest a general trend towards a lower level of return volatility, accompanied by a rising trend in conditional cross correlations in most markets; possibly re‡ecting the advent of euro in 1999 and increased interdependence of …nancial markets.

This paper provides a recursive empirical analysis of the scope for cost minimization in public debt management when the debt manager faces a given short term interest rate dictated by monetary policy as well as risk and market impact constraints. It simulates the ‘real time’ interest costs of alternative portfolios for UK government debt between April 1985 and March 2000. These portfolios are constructed using forecasts of return spreads based on a recursive modelling procedure. While we find statistically significant evidence of predictability, the interest cost savings are quite small when portfolio shares are constrained to lie within historical bounds.

The evidence in the inflation forecasting literature suggests that simple time series models are typically hard to outperform in predicting the dynamics of the first moment, and that using information about indicators of economic activity does not lead to out-of-sample forecasting gains. While most of the earlier literature focused on the ability of leading indicators (via the Phillips Curve- PC models) to forecast the central tendency of future inflation, our aim is to examine their role in driving the changes of the complete inflation distribution. The second moment is particularly relevant in policy-making as it can help address the question: is the uncertainty about inflation constant over time or does it respond to the state of economy? The recent trend in monetary policy is to view its role as that of balancing risks to price and output stability. In this framework, the distribution of inflation is a necessary tool to evaluate such risks in the form of probability statements. In this paper we introduce a simple semi-parametric approach that characterizes the distribution of inflation forecasts. The approach is structured such that the quantiles of the multi-step forecast errors of the baseline autoregressive models are defined as functions of leading indicators of economic indicators.