Anonymity, Signaling, and Collusion in Limit Order Books

Speaker: Álvaro Cartea (Oxford)

A key feature in the design of a limit order book is the anonymity of limit orders. However, we analyze data with trader identification and find that market makers break the anonymity of limit orders. Market makers use limit orders with large volumes to signal themselves to other market makers to avoid trading with each other and to snipe retail limit orders. We explain the behavior of market makers with a model that considers competitive and collusive equilibria. The model shows that the behavior of market makers we observe in the data is consistent with that in a collusive equilibrium where market makers use signals to avoid sniping each other's limit orders. Signaling enables market makers to share the benign flow from retail limit orders, and to share the additional benign flow from impatient investors who otherwise would have traded with a retail investor’s limit order.

Extended Microprice for Price Interpolation

Speaker: Robert Almgren (Princeton)

It is well-known that order book information beyond the inside quotes can provide meaningful information about the fair value of a traded asset, and useful predictions for future values. There are many ways in which a generalized price interpolant can be constructed. We outline the necessary properties that such an interpolant should have, we present one implementation, and we give empirical evidence for its predictive ability.

Shrinking the Term Structure

Speaker: Markus Pelger (Stanford)

We propose a new framework to explain the factor structure in the full cross section of Treasury bond returns. Our method unifies non-parametric curve estimation with cross-sectional factor modeling. We identify smoothness as a fundamental principle of the term structure of returns. Our approach implies investable factors, which correspond to the optimal spanning basis functions in decreasing order of smoothness. Our factors explain the slope and curvature shapes frequently encountered in PCA. In a comprehensive empirical study, we show that the first four factors explain the time-series variation and risk premia of the term structure of excess returns. Cash flows are covariances as the exposure of bonds to factors is fully explained by cash flow information. We identify a state-dependent complexity premium. The fourth factor, which captures complex shapes of the term structure premium, substantially reduces pricing errors and pays off during recessions.

The Granular Origins of Tail Dispersion Risk

Speaker: Torben Anderson (Northwestern University)

We study tail risk in the cross-section of asset prices at high frequencies. The tail behavior of the cross-section depends on whether a systematic jump event occurred. If so, the cross-sectional return tail is governed by assets' exposures to the systematic event while, otherwise, it is determined by idiosyncratic jumps. The tail shape of the cross-sectional distribution displays distinct properties with and without systematic jumps. We show empirically that shocks to the cross-sectional tail shape are a source of priced risk: fat idiosyncratic tails are favored by investors, while fat-tailed exposures to systematic jumps are disliked.

Observable Versus Latent Risk Factors

Speaker: Viktor Todorov (Northwestern University)

We test for temporal stability in local linear projection coefficients of observable risk factors on latent ones embedded in the cross-section of asset prices and extracted via Principal Component Analysis (PCA). The test can be used for deciding if and over what horizon conventional linear asset pricing techniques can be employed for studying the pricing of observable factors. The proposed test explores the fact that under the null hypothesis residuals from global linear projections of observable factors on latent ones, computed over a fixed time interval via PCA, should be also locally uncorrelated with the PCA factors. The test is fully nonparametric. Its asymptotic behavior is derived under a joint in-fill and large cross-section asymptotic setup. In an empirical application, we show that a linear relation between the market volatility factor and the latent systematic risk factors embedded in the cross-section of stock returns exists only over short periods of length of one trading day.

Shrinkage Estimation for Large Portfolios under Estimation Risk

Speaker: Ming Yuan (Columbia)

We  study a general form of  shrinkage estimates for the portfolio weights, and apply them to maximize the out-of-sample Sharpe ratio of a large portfolio under parameter uncertainty.  Our approach contains almost all existing  shrinkage estimates as special cases. We provide analytical equations for finding the optimal shrinkage parameters, and show that they can converge to  the corresponding optimal Sharpe ratio asymptotically.