In this address, I first describe the hallmarks of the Greenspan Fed and the advantages of this strategy that have produced such favorable economic outcomes. Then I look at how well the Fed might do after Greenspan and what challenges it might face. This discussion then leads naturally to suggestions for how the Fed should operate in the future to continue the excellent performance it has achived under Alan Greenspan.
This paper lays out a comprehensive solution to the problem of accounting for claims based the performance of a firm's stock price. The accounting covers employee stock options, stock appreciation rights, put and call options, convertible debt and preferred stock, warrants, and other hybrid securities. This issue has vexed the Financial Accounting Standards Board (FASB) and International Accounting Standards Board (IASB) who have approached the problem on a piecemeal basis, leading to inconsistent treatments of claims that in substance are very similar.
We develop a general equilibrium model of a production economy which has a risky production technology as well as a growth option to expand the scale of the productive sector of the economy. We show that when confronted with growth options, the representative consumer may sharply alter consumption rates to improve the likelihood of investment. This reduction in consumption is accompanied by an erosion of the option value of waiting to invest, leading to investment near the zero NPV threshold.
We perform an econometric analysis of the effect of new drug launches on longevity, using data from the IMS Health Drug Launches database and the WHO Mortality Database.
Bayesian learning implies decreasing weights on prior beliefs and increasing weights on the accuracy of the analyst?s past forecast record, as the number of forecast errors comprising her forecast record (its length) increases. Consistent with this model of investor learning, empirical tests show that investors? reactions to forecast news are increasing in the product of the accuracy and length of analysts? forecast records. Moreover, the Bayesian learning predicted by our model is more descriptive of investor reactions than is a static model which predicts that investors?
Using both a linear regression method and a probability-based method, we find that on average analysts place larger than efficient weights on (i.e., they over-weight) their private information when they forecast corporate earnings. We also find that analysts over-weight more when issuing forecasts more favorable than the consensus, and over-weight less, and may even under-weight, private information when issuing forecasts less favorable than the consensus.
The collapse of Long Term Capital Management ("LTCM") in Fall 1998 and the Federal Reserve Bank's subsequent efforts to orchestrate a bailout raise important questions about the structure of the Bankruptcy Code. The Code contains numerous provisions affording special treatment to financial derivatives contracts, the most important of which exempts these contracts from the "automatic stay" and permits counterparties to terminate derivatives contracts with a debtor in bankruptcy and seize underlying collateral.
In fall 1998 the Federal Reserve Bank ("Fed") arranged a bailout of the massive hedge fund, Long Term Capital Management (LTCM), which faced the prospect of immediate liquidation if it filed a petition under Chapter 11 of the Bankruptcy Code. The Fed's intervention to aid LTCM calls into question the policy rationale underlying the Bankruptcy COde's special treatment of derivatives. In this paper, we make the following claim: derivatives may deserve special treatment, but not for the reason commonly given.
A liquidity trader wishes to trade a ?xed number of shares within a certain time horizon and to minimize the mean and variance of the costs of trading. Explicit formulas for the optimal trading strategies show that risk-averse liquidity traders reduce their order sizes over time and execute a higher fraction of their total trading volume in early periods when price volatility or liquidity increases. In the presence of transaction fees, numerical simulations suggest that traders want to trade more frequently when price volatility goes up or liquidity declines.