This paper studies how market closures affect investors' trading policies and the resulting return‐generating process. It shows that closures generate rich patterns of time variation in trading and returns, including those consistent with empirical findings: (1) U‐shaped patterns in the mean and volatility of returns over trading periods, (2) higher trading activity around the close and open, (3) more volatile open‐to‐open returns than close‐to‐close returns, (4) higher returns over trading periods than over nontrading periods, (5) more volatile returns over trading periods than over nontrading periods. It also shows that closures can make prices more informative about future payoffs.

We derive an intertemporal asset pricing model and explore its implications for trading volume and asset returns. We show that investors trade in only two portfolios: the market portfolio, and a hedging portfolio that is used to hedge the risk of changing market conditions. We empirically identify the hedging portfolio using weekly volume and returns data for U.S. stocks, and then test two of its properties implied by the theory: Its return should be an additional risk factor in explaining the cross section of asset returns, and should also be the best predictor of future market returns.

The predictability of an asset's returns will affect the prices of options on that asset, even though predictability is typically induced by the drift, which does not enter the option pricing formula. For discretely-sampled data, predictability is linked to the parameters that do enter the option pricing formula. We construct an adjustment for predictability to the Black-Scholes formula and show that this adjustment can be important even for small levels of predictability, especially for longer maturity options. We propose several continuous-time linear diffusion processes that can capture broader forms of predictability, and provide numerical examples that illustrate their importance for pricing options.

The predictability of an asset's returns will affect the prices of options on that asset, even though predictability is typically induced by the drift, which does not enter the option pricing formula. For discretely‐sampled data, predictability is linked to the parameters that do enter the option pricing formula. We construct an adjustment for predictability to the Black‐Scholes formula and show that this adjustment can be important even for small levels of predictability, especially for longer maturity options. We propose several continuous‐time linear diffusion processes that can capture broader forms of predictability, and provide numerical examples that illustrate their importance for pricing options.

We develop a model to explore the asset pricing implications of firms being buyers of last resort for their own stocks. Those with more ability to repurchase shares when prices drop far below fundamental value (i.e., less financially constrained firms) should have lower short-horizon return variances (controlling for fundamental variance) than other firms. Using standard proxies for financing constraints such as past repurchases and firm age, we find strong support for this predicted relation. We also find that this relation is stronger in the U.S. after 1982 when regulatory reforms lowered the legal cost of conducting repurchases as well as in countries where share repurchases are legally easier to execute.

We document the central role of collateral in the pricing of tri-party repos. Markets are competitive for repos with safe collateral but are severely segmented for repos with risky collateral, such as equities and low-grade corporate bonds. Fund families are the sole contributors to the segmentation, and collateral concentration is the main determinant in the substantial variation in repo pricing, both across and within segments. The segmented structure points to Fidelity as a systemically important player and the markets potential fragility. Facing market segmentation, dealers optimize financing costs by allocating their collateral across fund families.

This paper examines the illiquidity of corporate bonds and its asset‐pricing implications. Using transactions data from 2003 to 2009, we show that the illiquidity in corporate bonds is substantial, significantly greater than what can be explained by bid–ask spreads. We establish a strong link between bond illiquidity and bond prices. In aggregate, changes in market‐level illiquidity explain a substantial part of the time variation in yield spreads of high‐rated (AAA through A) bonds, overshadowing the credit risk component. In the cross‐section, the bond‐level illiquidity measure explains individual bond yield spreads with large economic significance.

From 2007 to June 2013, a small group of fee-paying, high-speed traders receive the Michigan Index of Consumer Sentiment two seconds before its broader release. Within this early peek window, we find highly concentrated trading and a fast price discovery of less than 200 milliseconds. Outside this narrow window, general investors trade at fully adjusted prices. We further establish a causal relationship between the early peek mechanism and the fast price discovery by isolating the impact of the early peek arrangement along two dimensions. In cross section, we use other news releases without the early peek (as controls); in time series, we use the sudden suspension of the early peek arrangement in July 2013 (as the treatment). Our difference-in-difference tests directly connect the early peek arrangement to more efficient price discovery — it results in faster price discovery, lower volatility, and faster resolution of uncertainty. These results show that contrary to the common perception, tiered information release may help to reduce, rather than enhance, the informational advantage of faster traders and improve the efficiency of the price discovery process in financial markets.