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ABSTRACTThis paper analyses the relationship between monetary policy and asset prices in the context of optimal policy rules. The transmission mechanism is represented by a linearized rational expectations model augmented for the effect of asset prices on aggregate demand. Stabilization objectives are represented by a discounted quadratic loss function penalizing inflation and output gap volatility. Asset prices are allowed to deviate from their intrinsic value due to momentum trading. We find that in the presence of wealth effects and inefficient markets, asset price misalignments from their fundamentals should be included in the optimal interest rate reaction function.

This paper examines the time-series properties of house price to earnings ratio (HPER) in the UK using aggregate and regional data. Specifically, we utilise a series of unit root tests to examine the null hypothesis of nonstationary HPERs. These include linear tests as well as a nonlinear test and also a test which accounts for abrupt structural change. The results are against the notion of stationary HPERs. This implies that house prices may permanently diverge from earnings.

In this paper we examine the predictive power of the Heterogeneous Autoregressive (HAR)model for the return volatility of major European government bond markets. Results fromHAR-type volatility forecasting models show that past short and medium-term volatilityare significant predictors of the term structure of intraday volatility of European bondswith maturities ranging from 1-year up to 30-years. When we decompose bond marketvolatility into its continuous and discontinuous (jump) component, we find that the jumpcomponent is a significant predictor. Moreover, we show that feedback from past short-term volatility to the forecast of future volatility is stronger in days that precede monetarypolicy announcements.

ABSTRACTWe forecast US inflation using a standard set of macroeconomic predictors and a dynamic model selection and averaging methodology that allows the forecasting model to change over time. Pseudo out‐of‐sample forecasts are generated from models identified from a multipath general‐to‐specific algorithm that is applied dynamically using rolling regressions. Our results indicate that the inflation forecasts that we obtain employing a short rolling window substantially outperform those from a well‐established univariate benchmark, and contrary to previous evidence, are considerably robust to alternative forecast periods.