Knowledge that Transforms

We introduce the concept of risk entanglement in a preference-free setting to jointly explain the exchange rate volatility, cyclicality, and currency risk premia in the data. Risk entanglement specifies a subset of incomplete market models, in which nondiffusive or nonlog-normal shocks to exchange rates are not fully spanned by asset returns. When risks are entangled, there exist multiple pricing-consistent exchange rates, but none of them are equal to the ratio of the stochastic discount factors (SDFs) or their projections. Decoupling the exchange rate from the SDFs allows us to address key FX market patterns that are puzzling in international finance.

We represent risk factors as sums of orthogonal components capturing fluctuations with cycles of different length. The representation leads to novel spectral factor models in which systematic risk is allowed—without being forced—to vary across frequencies. Frequency-specific systematic risk is captured by a notion of spectral beta. We show that traditional factor models restrict the spectral betas to be constant across frequencies. The restriction can hide horizon-specific pricing effects that spectral factor models are designed to reveal. We illustrate how the methods may lead to economically meaningful dimensionality reduction in the factor space.

We use data from an online financial service to show that many consumers fail to stick to their self-set debt paydown plans. This behavior is best explained by present bias. Our empirical approach is informed by a parsimonious model showing that the sensitivity of spending to paycheck receipt reflects a present-biased agents short-run impatience, and that this sensitivity is reduced by available resources only for agents who are aware (sophisticated) of their future impatience. Classifying users accordingly, we find that (i) sophisticated users debt paydown decreases with short-run impatience, and that (ii) planned paydown is most predictive of actual paydown for sophisticated users.

Extending Kaniel et al. (2012) and many others, we present the first empirical evidence that indicates the high volume return premium is linked to economic fundamentals. The volume premium has strong predictive power for future industrial production growth and other macroeconomic indicators with or without controls for common equity pricing factors and business cycle variables. However, only a small portion of the volume premium can be attributed to its comovement with equity return factors and economic risk factors. Mispricing-based factor models also fail to adequately explain the return anomaly.

We examine 24 global factor premiums across equity, bond, commodity, and currency markets via replication and out-of-sample evidence between 1800 and 2016. Replication yields ambiguous evidence within a unified testing framework that accounts for p-hacking. Out-of-sample tests reveal strong and robust presence of the large majority of global factor premiums, with limited out-of-sample decay of the premiums. We find global factor premiums to be generally unrelated to market, downside, or macroeconomic risks in the 217 years of data. These results reveal significant global factor premiums that present a challenge to traditional asset pricing theories.

We study a dynamic voluntary disclosure setting where the manager’s information and the firm’s value evolve over time. The manager is not limited in her disclosure opportunities, but disclosure is costly. The results show that the manager discloses even if this leads to a price decrease in the current period. The manager absorbs this price drop in order to increase her option value of withholding disclosure in the future. That is, by disclosing today, the manager can improve her continuation value. The results provide a number of novel empirical predictions regarding asset prices and disclosure patterns over time. These include, among others, that disclosures are negatively correlated in time, and stock return skewness is negatively correlated with lagged returns for firms with low uncertainty over their future profitability, in more competitive industries, and in industries with less informative public news.

We model the design of a benchmark fixing as an estimator of fair market value. The fixing data are the transactions of agents whose profits depend on the fixing, implying incentives for manipulation. We derive the optimal linear fixing under an assumption that transaction weights are unidimensional. We also axiomatically characterize the unique linear fixing that is robust to a certain form of collusion among traders. Our analysis provides a foundation for the commonly used volume-weighted average price (VWAP) and its analogue based on unidimensional weights. We characterize the relative advantages of these fixing designs, depending on market characteristics.

We construct the network, centrality measures, and attributions of trading profits for a sample of CLS Bank settlement data that spans diverse currency pairs, participants, and execution platforms. We define an average centrality differential as the return to the more-central counterparty in the trade. Estimates imply that the more-central counterparty receives a higher return, and that this differential increases as the counterparties’ centralities diverge. These two results are consistent with a pervasive centrality premium. This premium may reflect bargaining power, but we also find evidence that the premium is partially offset by losses that central agents incur in supplying liquidity.

We investigate the impact of an exogenous trading glitch at a high-frequency market-making firm on standard measures of stock liquidity (spreads, price impact, turnover, and depth) and institutional trading costs (implementation shortfall and volume-weighted average price slippage). Stocks in which the firm accumulates large long (short) positions increase (decrease) by about 4% during the glitch and become substantially more illiquid. It takes one day for prices and spread-based liquidity measures to revert. Institutional trading costs, however, remain significantly higher for more than one week. Both liquidity measures are also weakly correlated outside the glitch period, suggesting they capture different aspects of liquidity.

Chinese interbank foreign exchange trading was originally conducted through a centralized, anonymous limit order book (LOB). We determine the impact of the introduction of a parallel decentralized over-the-counter (OTC) market. We find that: (1) most trading migrated to the OTC, (2) the LOB price function is upward-sloping versus the OTC price function is downward-sloping, and (3) the LOB market has a single price function versus the OTC market has multiple price functions. Next, we develop a theoretical model of parallel markets that can simultaneously explain all of these empirical findings. We test a new model prediction and find support.