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Níl an t-ábhar seo ar fáil i nGaeilge.

Rupert de Vincent-Humphreys



Prices & Costs

Current Position

Principal Economist

Fields of interest

Macroeconomics and Monetary Economics,Financial Economics,Mathematical and Quantitative Methods



Msc in Finance & Economics, London School of Economics, United Kingdom


MSci in Physics, University of Durham, United Kingdom

Professional experience

Principal Economist - Prices & Costs Division, Directorate General Economics, European Central Bank


Senior Economist - Structural Economics Division, Directorate Monetary Analysis, Bank of England


Economist, Bank of England

4 November 2019
Economic Bulletin Issue 7, 2019
This box summarises the findings of an ad hoc ECB survey of leading euro area companies about their price-setting behaviour, covering various dimensions. Among the findings, it was seen that firms mostly vary their prices by geographical market and by type of customer, but much less so depending on the sales platform. The frequency of price reviews and changes varies significantly across sectors, with weekly or even daily price changes being common for some retailers, while in some parts of the services sector annual price changes are more typical. Manufacturers tend to review prices monthly but change them only on a quarterly, semi-annual or annual basis. Increases in average selling prices are achieved, to a large extent, by introducing new products with higher value content. When reviewing prices, firms pay most attention to costs and competitor prices, with the latter being particularly important for consumer-facing firms. Many factors contribute to sluggish price adjustment: pricing strategies are based on broadly stable costs and margins, a fear that competitors will not follow suit and, for consumer-oriented firms, a focus on pricing points and an understanding that customers expect prices to remain stable.
JEL Code
D4 : Microeconomics→Market Structure and Pricing
E3 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles
L1 : Industrial Organization→Market Structure, Firm Strategy, and Market Performance
5 February 2019
Economic Bulletin Issue 1, 2019
For two decades the ECB Survey of Professional Forecasters (SPF) has been collecting point forecasts and probability distributions for euro area-wide HICP inflation, real GDP growth and the unemployment rate at different horizons. This article documents the evolution of the SPF through the changing economic landscape of the past twenty years, including the Great Moderation, with relatively high economic growth and stable inflation, the financial crisis and, more recently, a prolonged period of subdued inflationary pressures. Analyses show that the strong and persistent shocks in the past ten years have created challenges for the stability of the economic relationships and mean reversion tendencies on which forecasts tend to be based. They also suggest that in 2009 there was a lasting increase in forecasters’ assessments of uncertainty across all variables and horizons. Learning from the SPF has remained a useful input for the ECB’s economic analysis and monetary policy.
JEL Code
D84 : Microeconomics→Information, Knowledge, and Uncertainty→Expectations, Speculations
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
25 September 2018
Economic Bulletin Issue 6, 2018
Private sector inflation expectations are a key component of a broad range of indicators that the ECB considers when determining the appropriate monetary policy stance for achieving its price stability objective. Inflation expectations can not only affect inflation itself through the wage and price-setting processes, but also serve as a useful cross-check on the ECB’s and the Eurosystem’s own projections. This article focuses on market-based measures of longer-term inflation expectations, which are timely indicators derived from the prices of instruments that are traded in financial markets and linked to future inflation outcomes. It reviews recent developments in the information that can be extracted from different types of market-based indicator, starting from the period leading up to the ECB’s announcement of its expanded asset purchase programme (APP). The fall in market-based indicators of longer-term inflation expectations between 2014 and mid-2016 was consistent across major jurisdictions, possibly reflecting global concerns about weak aggregate demand and associated disinflationary pressures. Their subsequent recovery has been driven by a partial dissipation of these concerns and, in particular, a significant improvement in the euro area macroeconomic environment. The lion’s share of the movement in longer-term inflation expectations over the past few years has stemmed from the inflation risk component of these indicators, suggesting that the balance of risks to the inflation outlook has been one of the main drivers. Indeed, information extracted from the prices of inflation options implies that the risk-neutral probability of deflation increased noticeably in late 2014 and early 2015, before declining more recently.
JEL Code
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates
G01 : Financial Economics→General→Financial Crises
28 December 2017
Economic Bulletin Issue 8, 2017
23 December 2010
This paper presents a set of probability density functions for Euribor outturns in three months’ time, estimated from the prices of options on Euribor futures. It is the first official and freely available dataset to span the complete history of Euribor futures options, thus comprising over ten years of daily data, from 13 January 1999 onwards. Time series of the statistical moments of these option-implied probability density functions are documented until April 2010. Particular attention is given to how these probability density functions, and their associated summary statistics, reacted to the unfolding financial crisis between 2007 and 2009. In doing so, it shows how option-implied probability density functions could be used to contribute to monetary policy and financial stability analysis.
JEL Code
C13 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Estimation: General
C14 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Semiparametric and Nonparametric Methods: General
G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates
G13 : Financial Economics→General Financial Markets→Contingent Pricing, Futures Pricing