Demystifying monetary policy surprises: Fed response to financial conditions and wait and see for new economic data

U.S. Monetary Policy Surprises and Mortgage Rates

Monetary policy in a world of radical uncertainty

Monetary policy dysregulation with data distortion

Monetary policy announcements and expectations: Evidence from german firms

A t each Federal Open Market Committee (FOMC) meeting the members make decisions on monetary policy. Since monetary policy is forward looking, changes in those policies are expected only if unexpected information on the state of the U.S. economy has arrived since the previous meeting. If the economic data come in stronger (weaker) than expected at the previous FOMC meeting, then the members might enact tighter (looser) monetary policy. So, clearly, predicting whether the Fed will change its policy depends on having an understanding of whether the incoming data have been stronger or weaker than expected. Understanding the data at this depth is complicated because many macroeconomic variables are released between one FOMC meeting and the next and it is unlikely that all of the variables will turn out uniformly better or worse than expected. Some variables or economic indicators will be stronger and some will be weaker. How should one conclude whether the overall data have come in stronger, weaker, or as expected? One approach is for Fed watchers to use their own subjective judgment to characterize the data. An alternative approach is to use a quantitative measure constructed by economists at Citigroup. Their “Economic Surprise Index” is constructed daily by taking a weighted average of the data “surprises” (actual releases versus Bloomberg survey median forecasts) observed over the past three months. The index focuses on key data releases, such as the monthly jobs estimate and the monthly change in the consumer price index, as well as other data releases. Older surprises are discounted relative to more recent surprises to prevent the index from becoming stale. A value greater (less) than zero denotes stronger(weaker)than-expected data, whereas a value near zero indicates that the data have been coming in as expected. The chart shows a plot of the Economic Surprise Index since 2003. As expected, the index is somewhat volatile with a mean of roughly zero. The series is modestly persistent with an autocorrelation coefficient of 0.66—hence there are fairly long swings within which the series stays positive or negative. This persistence casts doubt on the Following the Fed with a News Tracker

Significance The US stock market and especially the seven largest technology companies have powered the rally, but markets in Europe and Asia have also risen on better-than-expected economic data and the delivery or expectation of policy stimulus. However, consumers remain more pessimistic about the near-term outlook. Impacts China’s stock market has momentum; the MSCI China Index has gained 25% since January 21 on better economic data and more forceful policy. Policy divergence prompts currency volatility; sterling hit a 21-month high to the euro on UK rates likely falling after euro-area rates. Fears about limited scope for looser monetary policy -- which have driven large EM bond and equity fund outflows in 2024 -- will persist.

From one Bundesbank to the next ? Germany's central Bank as a model for Europe Thomas Fricke How will the European Central Bank work ? Will the policy of monetary authorities in Europe look like that of the german Central Bank, an institution that has been used as a model in Maastricht's Treaty ? People who want the fight against inflation to remain an absolute priority fear that this will not be the case : according to them, too much attention might be paid to other macro- economic data such as growth, unemployment or the external balance. From an institutional point of view, the ECB is a clone of the Bundesbank. It will be indépendant, and price stability will be its main goal. But does it mean that it will have the same feeling towards inflation ? Will its behaviour and its results be the same despite the fact that decisions will be made by all member countries ? In order to understand whether the future ECB would just be another Bundesbank, one must first undesrstand the origins, the philosophy and the actual functioning of Germany's Central Bank. Despite its importance, the Bundesbank is often wrongly understood in foreign countries. In Germany itself the myth surrounding it blurrs the vision of what it really is. Very often its policy does not seem to be understood by foreign countries, whereas it seldom is criticized within Germany. Very often its tight policy is viewed as excessive and lacking in international solidarity because it hampers other countries'growth. These misunderstandings stem from the foundations of the monetary policy and from the rather secretive way in which the Bundesbank works.

This paper examines the impact of U.S. monetary policy surprises on securitized real estate markets in 18 countries. The policy surprises are measured by both the surprise changes to the target federal funds rate (the target factor) and surprises in the future direction of the Federal Reserve monetary policy (the path factor). The results show that most international securitized real estate markets have significantly positive responses to surprise decrease in current or future expected federal funds rates, though such responses vary greatly across countries. Also, while the U.S. securitized real estate market reacts mainly to the target factor, foreign securitized real estate markets react to the path factor. Furthermore, we find that the cross-country variation in the response to the target factor is correlated with the country’s exchange rate regime and its degree of real economic and particularly financial integration, while the cross-country variation in the response to the path factor is mainly related to the country’s degree of financial integration.

The current literature has provided a number of important insights about the effects of macroeconomic data releases on monetary policy expectations and asset prices. However, one puzzling aspect of that literature is that the estimated responses are quite small. Indeed, these studies typically find that the major economic releases, taken together, account for only a small amount of the variation in asset prices-even those closely tied to near-term policy expectations. In this paper we argue that this apparent detachment arises in part from the difficulties associated with measuring macroeconomic news. We propose two new econometric approaches that allow us to account for the noise in measured data surprises. Using these estimators, we find that asset prices and monetary policy expectations are much more responsive to incoming news than previously believed. Our results also clarify the set of facts that should be captured by any model attempting to understand the interactions between economic data, monetary policy, and asset prices.

Achieving an acceptable level of price growth is one of the main objectives of economic policies. With consideration to the importance of food, information on food price response to monetary policies is important. To achieve the object, scholars recently emphasize the use of models in which a wide range of economic data are included. These models are created by inclusion of one or more factors within the traditional VAR models. In this study, we tried to evaluate the effect of monetary policy on food price by using small scale of FAVAR model. For the purpose, 31 macroeconomic variables in periods 1367:1 to 1387:4 were included. The results showed that the liquidity shock has not influenced food price index for approximately ten next seasons. After this period, the liquidity shock makes increasing fluctuations on food price in such a way that the equilibrium has not been reachable. Therefore, a monetary shock will lead to instability fluctuations in the food price index for the long run. The fluctuations are cyclic and will increase over time in the way that they present reductions and increases around an equilibrium point.

George Box’s Realization, That All Models, Especially Statistical Models, Are Wrong Means That It Is Impossible for There to Be Any True Probabilities, True Models, True Theories, True Expectations or True (Accepted) Hypotheses: The Claims, Made By Rational Expectations Proponents About True Objective Probabilities, True Models, True Expectations, True Expected Values, etc., Have No Scientific Foundation

Conundrum or Complication: A Study of Yield Curve Dynamics Under Unusual Economic Conditions and Monetary Policies

Modelling the real yen–dollar rate and inflation dynamics based on international parity conditions

Significance This volatility is driven by expectations of further monetary stimulus in response to a slowing economy. Despite persistent concerns about the fallout from the anticipated tightening in US monetary policy and many country-specific risks, such as the standoff between Greece and its creditors, equity market sentiment remains supported by accommodative monetary policies worldwide and expectations of the US monetary policy tightening being gradual. Impacts Market volatility could increase further, as better-than-expected economic data in the euro-area vies with weaker-than-anticipated US data. Decoupling of surging equity prices and weak economic fundamentals threatens the rally's sustainability, increasing scope for volatility. This decoupling is most pronounced in China, where weak economic data prompt buying of equities in anticipation of stimulus measures. The greatest risk in equity markets is uncertainty surrounding US interest rates and their impact on emerging markets.

This dissertation consists of four essays, focusing on the relationship between financial risks, financial market uncertainty and macroeconomic conditions. The first essay estimates the effects of anticipated and unanticipated monetary policy changes on jump variation by employing high frequency non-parametric jump detection methods. We use an event study approach and a structural VAR framework in examining stock price jump variation for the aggregate economy, the financial, health, energy and telecommunication-information technology sectors (Tel-Info). We find that anticipated changes in the Fed funds have no significant effect on jumps. In contrast, jump variation in the price of financial market data increases with monetary policy surprises. We document evidence of asymmetries in the response of jumps to monetary policy changes. Monetary policy surprises and positive changes in the Fed target rate induce increment in jumps. Similar results exist in the sector analysis. In addition, this study uncovers no evidence of endogenous response between jumps and monetary policy surprises. The second essay estimates the response of uncertainty/risk aversion to monetary policy actions in both the financial sector and the aggregate economy using a Structural Vector Autoregressive (SVAR) model. When compared with other sectors, our constructs reveal that financial risk aversion/uncertainty has greater correlation with the aggregate risk aversion and uncertainty. Our analysis reveals that financial risk aversion and uncertainty exhibit stronger interdependence with monetary policy actions than aggregate uncertainty and risk aversion. The third essay provides the dynamic characterization of the link between ex-ante financial distress risk and the real economy. Using 219, 990 firm-year observations, an ex-ante measure of financial distress is generated at sector level. By employing simultaneous equation model, we provide a comprehensive set-up for predicting ex-ante financial distress risk and examining its effect on GDP growth. Over the period of 1970-2012, the results from the US firms reveal that ex-ante financial distress strongly relate to GDP growth. Ex-ante distress risk contracts GDP growth by up to 10%. Similar contractions in the growth of GDP are uncovered when a single equation approach is used in establishing the relationship between financial distress and growth in GDP. In addition, the results remain consistent when a confirmatory analysis is generated by using a weighted sector index of financial distress. The fourth essay examines liquidity risk and financial integration using bank level flows for 95 countries. The results suggest that global banking network influences liquidity risk. The more the banks are connected to each other the more they are prone to liquidity risk and the result is the same for intermediaries in the network formation. Borrowers that connect to important lenders are not at an advantage, but banks that have independent access to finance in the financial network are at an advantage. On a regional basis, banks in Europe, Africa and Asia and Pacific that have strong connections are prone to liquidity risk. American banks that function as intermediary in the network are prone to liquidity risk. Banks in the American region that have independent access to financing in the financial network are able to reduce liquidity creation, but their degree of connectivity worsens net stable funding.