Bianca De Paoli

Email: b.s.de-paoli@lse.ac.uk

Bianca De Paoli Bianca De Paoli Bianca De Paoli

Research interests: International Macroeconomics, Monetary Economics, Macro-Finance.

Curriculum Vitae

Teaching:

Macro II

 

Research:

The forward premium anomaly refers to the empirical observation that high interest rate currencies tend to appreciate rather than depreciate as the risk-neutral uncovered interest rate parity (UIP) would imply. Part of the literature have attributed the failure of UIP to the existence of time varying foreign exchange risk premia. But resolving the forward premium puzzle requires a volatile FX risk premium that covaries negatively with the depreciation rate. This paper shows that a canonical general equilibrium small open economy model with consumption based external habit formation can only generate such a FX premium under certain assumptions namely: Households need to have slow-moving habits as well as incur very persistent output shocks. The results are shown to be independent of the origin of shocks. Moreover, the introduction of mechanisms that increase exchange rate volatility also help reconcile such puzzle.

Work in progress:

The aim of this paper is to answer the question: how should monetary policy be conducted in the face of multiple sources of uncertainty? These uncertainties may include: uncertainty about future shocks; uncertainty about the parameters within a policy model; and uncertainty about alternative model specifications. We apply Bayesian econometrics and Bayesian decision theory are in order to provide a unified way of answering this question. Using UK data, we estimate a set of dynamic general equilibrium models which includes a learning model, a small and a big scale New Keynesian model, a small open economy framework and a model with backward looking features. We then define a class of Taylor-like rules and search for the rule that delivers the best weighted performance across the models, where the weights are the posterior odds computed from the marginal data density of each model.

Other works: