Knowledge that Transforms

We model how investors allocate between asset managers, managers choose portfolios of multiple securities, fees are set, and security prices are determined. Investors are indifferent between higher-cost informed managers and lower-cost uninformed managers, interpreted as passive managers as their portfolio is linked to the " expected market portfolio." We make precise Samuelson's dictum by showing that active investors reduce micro-inefficiencies more than they do macro-inefficiencies. In fact, all inefficiency arises from systematic factors when the number of assets is large. Further, we show how the costs of active and passive investing affect macro- and micro-efficiency, fees, and assets managed by active and passive managers. Our findings help explain the rise of delegated asset management and the resultant changes in financial markets.

We introduce a covariance and spread (i.e., exchange rate forward discount) adjusted carry factor that prices the cross-section of FX market returns, where many other single- and multifactor models fail. Both the covariance matrix of exchange rate growths and forward discounts contain important information for pricing that is not captured by well-known factors. The time-varying conditional covariance matrix and forward discounts forecast future realized currency returns.

What distribution best characterizes the time series and cross-section of individual stock returns? To answer this question, we estimate the degree of cross-sectional return skewness relative to a benchmark that nests many models considered in the literature. We find that cross-sectional skewness in monthly returns far exceeds what this benchmark model predicts. However, cross-sectional skewness in long-run returns in the data is substantially below what the model predicts. We show that fat-tailed idiosyncratic events appear to be necessary to explain skewness in the data. (JEL, G10, G11, G12, G13, G14).

This paper develops a general equilibrium model and provides empirical support that the market volatility-of-volatility (VOV) predicts market returns and drives the time-varying volatility risk. In asset pricing tests with the market, volatility, and VOV as factors, the risk premium on VOV is statistically and economically significant and robust. Market and volatility risks are not priced in unconditional models, but, consistent with theory, their factor loadings, conditional on VOV, are priced. The pricing impact of VOV strengthens during market crashes, suggesting that VOV is particularly relevant during market turmoil, when investors demand increased compensation for VOV risk.

Many financial instruments are designed with embedded leverage, such as options and leveraged exchange-traded funds (ETFs). Embedded leverage alleviates investors’ leverage constraints, and, therefore, we hypothesize that embedded leverage lowers required returns. Consistent with this hypothesis, we find empirically that options and leveraged ETFs provide significant amounts of embedded leverage; this embedded leverage increases return volatility in proportion to the embedded leverage; and higher embedded leverage is associated with lower risk-adjusted returns. The results are statistically and economically significant, and we provide extensive robustness tests and discuss the broader implications of embedded leverage for financial economics.

At a given point in time, bitcoin prices are different on exchanges located in different countries, or against different currencies. While existing literature attributes the largest price differences to frictions, like market segmentation, trading platforms advertize how to execute trades based on this information. We provide a novel risk-based explanation of these price differences for a sample containing the most reputable exchanges and after accounting for all transaction costs and limitations to trade. Bitcoin prices for more expensive pairs are riskier because they depreciate more in bad times for cryptocurrency investors, when aggregate liquidity and investor sentiment are lower.

Using a dynamic selection model, we obtain consistent and unbiased estimates of risk and returns for infrequently traded bonds and conduct the first comprehensive asset pricing test of municipal bonds using the multifactor approach. Correction for sample selection and infrequent trading problems results in substantially higher beta estimates. Besides conventional risk factors, illiquidity and taxes are important for the pricing of municipal bonds. Importantly, bond returns contain a significant liquidity risk premium. Failing to account for sample selection bias leads to erroneous inference on the magnitude of systematic risk and substantial underestimation of risk premiums.

We provide novel evidence that arbitrageurs use exchange-traded funds (ETFs) as an avenue to circumvent short-sale constraints at the stock level. Using a large sample of U.S. equity ETF holdings, we document that shorting activity on ETFs rises with the difficulty of shorting underlying stocks. Stocks heavily shorted via their holding ETFs underperform those that are lightly shorted. The return predictability of ETF shorting is distinct from stock-level shorting measures and is concentrated among stocks that face severe arbitrage constraints. These findings suggest that ETFs allow arbitrageurs to target overpriced stocks that are otherwise difficult to short.

This paper explains that anticipated market liquidity is an important concern for arbitrageurs considering entry into a market, a concern that can generate self-fulfilling asset prices. In the model, fixed investment costs turn a market illiquid and generate an arbitrage opportunity. The worst-case return on pledged collateral constrains arbitrageurs’ leverage. The interaction between this return and arbitrageurs’ capital makes entry decisions complementary and can create multiple equilibria. When arbitrageurs enter with capital, the market becomes more liquid; the worst-case return rises; and more arbitrageurs enter with capital. When arbitrageurs withhold capital, the market stays illiquid; the worst-case return falls; and other arbitrageurs stay out.

We empirically study how inventory constraints of underwriters affect corporate bond offerings. Using underwriter-insurer-level transaction data, we find that a more constrained underwriter is more likely to place a bond and increases the allocation in the primary market to an insurer with a stronger preexisting relationship. The same underwriter is also more likely to buy back part of an allocation from the same insurer within 6 to 12 months after an offering. Overall, by “parking” inventory to relationship investors in the primary market, underwriters mitigate the effect of their inventory constraints on firms’ bond financing costs.