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This paper examines to what extent the build-up of 'global imbalances' since the mid-1990s can be explained in a purely real open-economy DSGE model in which agents' perceptions of long-run growth are based on filtering observed changes in productivity. We show that long-run growth estimates based on filtering U.S. productivity data comove strongly with long-horizon survey expectations. By simulating the model in which agents filter data on U.S. productivity growth, we closely match the U.S. current account evolution. Moreover, with household preferences that control the wealth effect on labor supply, we can generate output movements in line with the data.
Discussions of the international dimension of the global economic crisis have frequently focused on the build-up of large current account "imbalances" since the mid-1990s. This paper examines the extent to which the U.S. current account can be understood in a purely real open-economy DSGE model, were agents' perception of long-run growth evolves over time in response to changes in productivity. We first show that long-run growth forecasts based on
ltering actual productivity growth comove strongly with survey measures of expectations. Simulating the model, we
nd that including data on U.S. TFP growth and the world real interest rate can, under standard parametrizations of our model, explain the evolution of the U.S. current account quite closely. With household preference that allow positive labor supply e¤ects after favorable news of future income, we can also generate output movements in line with the data.
Can a tightening of the bank resolution regime lead to more prudent bank behavior? This policy paper reviews arguments for why this could be the case and presents evidence linking changes in bank resolution regimes with bank risk-taking. The authors find that the tightening of bank resolution in the U.S. (i.e., the introduction of the Orderly Liquidation Authority) significantly decreased overall risk-taking of the most affected banks. This effect, however, does not hold for the largest and most systemically important banks – too-big-to-fail seems to be unresolved. Building on the insights from the U.S. experience, the authors derive principles for effective resolution regimes and evaluate the emerging resolution regime for Europe.
In this paper we investigate the comparative properties of empirically-estimated monetary models of the U.S. economy. We make use of a new data base of models designed for such investigations. We focus on three representative models: the Christiano, Eichenbaum, Evans (2005) model, the Smets and Wouters (2007) model, and the Taylor (1993a) model. Although the three models differ in terms of structure, estimation method, sample period, and data vintage, we find surprisingly similar economic impacts of unanticipated changes in the federal funds rate. However, the optimal monetary policy responses to other sources of economic fluctuations are widely different in the different models. We show that simple optimal policy rules that respond to the growth rate of output and smooth the interest rate are not robust. In contrast, policy rules with no interest rate smoothing and no response to the growth rate, as distinct from the level, of output are more robust. Robustness can be improved further by optimizing rules with respect to the average loss across the three models.
Using data of US domestic mergers and acquisitions transactions, this paper shows that acquirers have a preference for geographically proximate target companies. We measure the ‘home bias’ against benchmark portfolios of hypothetical deals where the potential targets consist of firms of similar size in the same four-digit SIC code that have been targets in other transactions at about the same time or firms that have been listed at a stock exchange at that time. There is a strong and consistent home bias for M&A transactions in the US, which is significantly declining during the observation period, i.e. between 1990 and 2004. At the same time, the average distances between target and acquirer increase articulately. The home bias is stronger for small and relatively opaque target companies suggesting that local information is the decisive factor in explaining the results. Acquirers that diversify into new business lines also display a stronger preference for more proximate targets. With an event study we show that investors react relatively better to proximate acquisitions than to distant ones. That reaction is more important and becomes significant in times when the average distance between target and acquirer becomes larger, but never becomes economically significant. We interpret this as evidence for the familiarity hypothesis brought forward by Huberman (2001): Acquirers know about the existence of proximate targets and are more likely to merge with them without necessarily being better informed. However, when comparing the best and the worst deals, we are able to show a dramatic difference in distances and home bias: The most successful deals display on average a much stronger home bias and distinctively smaller distance between acquirer and target than the least successful deals. Proximity in M&A transactions therefore is a necessary but not sufficient condition for success. The paper contributes to the growing literature on the role of distance in financial decisions.
Our study provides evidence on the share price reactions to the announcement of equity issues in Germany, where capital market is characterized by institutional features distinct from the U.S. market. German seasoned equity issues yield a positive market reaction which contrasts to the significant negative abnormal returns reported for the U.S. We provide evidence that these results are due to differences in both issuing characteristics and floatation methods, and in the corporate governance and ownership structures of the two countries. Our study explains much of the empirical puzzle of different market reactions to seemingly similar events across financial markets.
Recent changes in accounting regulation for financial instruments (SFAS 133, IAS 39) have been heavily criticized by representatives from the banking industry. They argue for retaining a historical cost based "mixed model" where accounting for financial instruments depends on their designation to either trading or nontrading activities. In order to demonstrate the impact of different accounting models for financial instruments on the financial statements of banks, we develop a bank simulation model capturing the essential characteristics of a modern universal bank with investment banking and commercial banking activities. In our simulations we look at different scenarios with periods of increasing/decreasing interest rates using historical data and with different banking strategies (fully hedged; partially hedged). The financial statements of our model bank are prepared under different accounting rules ("Old" IAS before implementation of IAS 39; current IAS) with and without hedge accounting as offered by the respective sets of rules. The paper identifies critical issues of applying the different accounting rules for financial instruments to the activities of a universal bank. It demonstrates important shortcomings of the "Old" IAS rules (before IAS 39), and of the current IAS rules. Under the current IAS rules the results of a fully hedged bank may have to show volatility in income statements due to changes in market interest rates. Accounting results of a partially hedged bank in the same scenario may be less affected even though there are economic gains or losses.