This paper presents a consumption-based general equilibrium model for valuing foreign exchange contingent claims. The model identifies a novel economic mechanism by exploiting highly but imperfectly shared consumption disaster with variable intensities which are the concerns to the representative investor under recursive utility. When applied to the data, the model simultaneously replicates (i) the moderate option-implied volatilities; (ii) substantial variations in the risk-neutral skewness of currency returns; (iii) the uncovered interest rate parity puzzle; and (iv) the first two moments of carry trade returns. Furthermore, the model rationalizes salient features of the aggregate stock, government bonds, and equity index options.

This paper proposes a general equilibrium model that explains the pricing of the S&P 500 index options. The central ingredients are a peso component in the consumption growth rate and the time-varying risk aversion induced by habit formation which amplifies consumption shocks. The amplifying effect generates the excess volatility and a large jump-risk premium which combine to produce a pronounced volatility smirk for index options. The time-varying volatility and jump-risk premiums explain the observed state-dependent smirk patterns. Besides volatility smirks, the model has a variety of other implications which are broadly consistent with the aggregate stock and option market data.

I construct an informationally robust auction to sell a common‐value good. I examine the revenue guarantee of an auction over all information structures of bidders and all equilibria. As the number of bidders gets large, the revenue guarantee of my auction converges to the full surplus, regardless of how information changes as more bidders are added. My auction also maximizes the revenue guarantee when there is a single bidder.

This article studies the impact of increasing trading frequency in financial markets on allocative efficiency. We build and solve a dynamic model of sequential double auctions in which traders trade strategically with demand schedules. Trading needs are generated by time-varying private information about the asset value and private values for owning the asset, as well as quadratic inventory costs. We characterize a linear equilibrium with stationary strategies and its efficiency properties in closed form. Frequent trading (more double auctions per unit of time) allows more immediate asset reallocation after new information arrives, at the cost of a lower volume of beneficial trades in each double auction. Under stated conditions, the trading frequency that maximizes allocative efficiency coincides with the information arrival frequency for scheduled information releases, but can far exceed the information arrival frequency if new information arrives stochastically. A simple calibration of the model suggests that a moderate market slowdown to the level of seconds or minutes per double auction can improve allocative efficiency for assets with relatively narrow investor participation and relatively infrequent news, such as small- and micro-cap stocks.

T HE theoretical role of technology in economic growth is no mystery. It provides new machines and processes (improved capital quality) that raise output relative to input, or productivity. Yet for several reasons, once it comes to pursuing this truth by observation, hypothesis, and testing, every manner of obstacle seems to appear. One may be told that technology per se is rarely susceptible of measurement, that productivity increases can be attributed to many causes, impossible to untangle, or that the precise link between embodied technological change and productivity is too hazy. Such doubts are often justified. At times, however, they may unduly discourage those whose curiosity is not satisfied by aggregate production functions, and who would prefer to investigate more closely some particular reasons for productivity increases. To be clear on this point, technological innovation frequently is difficult to represent quantitatively. Output per unit of input often does rise for non-technological reasons (economies of scale, changes in rates of utilization of capacity, or optimal factor combinations), but there must be important cases where the reverse is true. One such case seems to be electrification of manufacturing industries. Here the rate of technical change can be reasonably well measured in terms of horsepower capacity of power equipment and consumption of power (work output). Furthermore, this revolution in the application of power can be viewed against the background of clear, known changes in manufacturing productivity changes which remain mostly unexplained. Specifically, it might be expected that there would be a relationship between electrification and reduced costs of production.' More broadly, the case of electrification might illustrate how the quantitative link between technological change and productivity can be developed. To this end, what follows (section I) traces some suggestive findings regarding the rise of electric power and changes in productivity in American manufacturing, and (section II) attempts to construct a theoretical framework for measuring the impact of electrical technology on factor costs. It is hoped that section II will help shed more light upon the broader question mentioned above, by providing procedures that could be adapted to a wide range of technologyproductivity cases.

This paper explores the shock of school closures caused by the COVID-19 pandemic to study the effect of childcare responsibilities on analyst forecasts. With manually collected data on whether analysts have children, I find that female analysts with children (mother analysts) are less likely to issue timely forecasts after school closures, compared to male analysts with children (father analysts). Mother analysts’ forecasts also become less accurate after school closures, but the negative effect only exists among forecasts for firms with relatively low institutional ownership, suggesting that mother analysts prioritize maintaining the forecast accuracy for firms that are more important to their careers. Additionally, mother analysts shift forecast release times to avoid childcare hours. My findings imply that childcare responsibilities hurt the productivity of mother analysts more than that of father analysts, even though these women have established themselves in a competitive industry.

We collect and analyze detailed filings from global institutional investors to estimate foreign investors’ U.S. dollar (USD) security holdings and currency hedging. Over two decades, foreign USD holdings grew sixfold, while hedge ratios rose by 15 percentage points after the 2008–2009 crisis. Currency hedging across mutual funds, pensions, and insurance reached $2 trillion by 2019. Hedging demand varies across investors, currency areas, and banking systems. We show that expected FX returns, beyond variance minimization, drive currency exposure in portfolios. Finally, we demonstrate and quantify how aggregate hedging demand affects hedging costs in the presence of constrained intermediaries.

We study the design of credit default swaps (CDS) auctions, which determine the payments by CDS sellers to CDS buyers following defaults of bonds. Using a simple model, we find that the current design of CDS auctions leads to biased prices and inefficient allocations. This is because various restrictions imposed in CDS auctions prevent certain investors from participating in the price discovery and allocation process. The imposition of a price cap or floor also gives dealers large influence on the final auction price. We propose an alternative double auction design that delivers more efficient price discovery and allocations.