Bertrand Gruss, Mrs. Sandra V Lizarazo Ruiz, and Mr. Francesco Grigoli
Anchoring of inflation expectations is of paramount importance for central banks’ ability to deliver stable inflation and minimize price dispersion. Relying on daily interest rates and inflation forecasts from major financial institutions in the United States, we calculate monetary policy surprises of individual analysts as the unexpected changes in the federal funds rate before the meetings of the Federal Reserve Board. We then assess the effect of monetary policy surprises on the dispersion of inflation expectations, a proxy for the extent of anchoring, which is based on the same analysts’ inflation projections submit-ted after the Fed meetings. With an identification strategy that hinges on a tight window around the Fed meetings, we find that monetary policy surprises lead to an increase in the dispersion of inflation expectations up to nine months after the policy meeting. We rationalize these results with a partial equilibrium model that features rational expectations and sticky information. When we allow the degree of information rigidity to depend on the realization of firm-specific shocks, the theoretical results are qualitatively consistent and quantitatively close to the empirical evidence.
The yen is an important barometer for the Japanese economy. Depreciations are typically associated with favorable economic developments such as increased corporate profits, rising equity prices, and upward pressure on domestic consumer prices. On the other hand, large and sharp appreciations run the risk of lowering actual and expected inflation, squeezing corporate profits, generating a negative wealth effect through depressed equity prices, and reducing confidence in the Bank of Japan’s efforts to reflate the domestic economy and achieve the inflation target. This paper takes a closer look at underlying drivers of rapid yen appreciations, highlighting the key role of carry-trade and the zero lower bound as important amplifiers.
In November 2014, OPEC announced a new strategy geared towards improving its market share. Oil-market analysts interpreted this as an attempt to squeeze higher-cost producers including US shale oil out of the market. Over the next year, crude oil prices crashed, with large repercussions for the global economy. We present a simple equilibrium model that explains the fundamental market factors that can rationalize such a "regime switch" by OPEC. These include: (i) the growth of US shale oil production; (ii) the slowdown of global oil demand; (iii) reduced cohesiveness of the OPEC cartel; (iv) production ramp-ups in other non-OPEC countries. We show that these qualitative predictions are broadly consistent with oil market developments during 2014-15. The model is calibrated to oil market data; it predicts accommodation up to 2014 and a market-share strategy thereafter, and explains large oil-price swings as well as realistically high levels of OPEC output.
We carry out an ex post assessment of popular models used to forecast oil prices and propose a host of alternative VAR models based on traditional global macroeconomic and oil market aggregates. While the exact specification of VAR models for nominal oil price prediction is still open to debate, the bias and underprediction in futures and random walk forecasts are larger across all horizons in relation to a large set of VAR specifications. The VAR forecasts generally have the smallest average forecast errors and the highest accuracy, with most specifications outperforming futures and random walk forecasts for horizons up to two years. This calls for caution in reliance on futures or the random walk for forecasting, particularly for near term predictions. Despite the overall strength of VAR models, we highlight some performance instability, with small alterations in specifications, subsamples or lag lengths providing widely different forecasts at times. Combining futures, random walk and VAR models for forecasting have merit for medium term horizons.
Mr. Christopher W. Crowe and Mr. S. Mahdi Barakchian
Conventional VAR and non-VAR methods of identifying the effects of monetary policy shocks on the economy have found a negative output response to monetary tightening using U.S. data over the 1960s-1990s. However, we show that these methods fail to find this contractionary effect when the sample is restricted to the period since the 1980s, apparently due to changes in the policymaking environment that reduce their effectiveness. Identifying policy shocks using Fed Funds futures data, we recover the contractionary effect of monetary tightening on output and find that almost half of output variation over the period appears due to policy shocks.
This paper discusses the behavior of futures prices for foreign exchange in Brazil during a period of high inflation and successive stabilization attempts (1989-92). After testing for futures prices unbiasedness and predicability by applying the Generalized Method of Moments, the paper argues that the finding of excess returns may be viewed as a rational response to the frequent and unpredictable changes in the exchange rate policy during that period. This response could reflect (i) an informational problem where the exchange rate policy is assumed to be unknown; or, (ii) a “peso” problem of rational (under) overprediction where the futures bias is the market response to the known policy of infrequent large nominal devaluations. The second line of explanation is suggested by conditioning the probability distribution of the excess return of futures contracts on the event of a major devaluation.
This paper undertakes an investigation into the efficiency of the crude oil futures market and the forecasting accuracy of futures prices. Efficiency of the market is analysed in terms of the expected excess returns to speculation in the futures market. Accuracy of futures prices is compared with that of forecasts using alternative techniques, including time series and econometric models, as well as judgemental forecasts. The paper also explores the predictive power of futures prices by comparing the forecasting accuracy of end-of-month prices with weekly and monthly averages, using a variety of different weighting schemes. Finally, the paper investigates whether the forecasts from using futures prices can be improved by incorporating information from other forecasting techniques.