Published & Forthcoming
Duration-Driven Returns, with Niels J. Gormsen, June 2022
We propose a duration-based explanation for the premia on major equity factors, including value, profitability, investment, low-risk, and payout factors. These factors invest in firms that earn most of their cash flows in the near future and could therefore be driven by a premium on near-future cash flows. We test this hypothesis using a novel dataset of single-stock dividend futures, which are claims on dividends of individual firms. Consistent with our hypothesis, the expected CAPM alpha on individual cash flows decrease in maturity within a firm, and the alpha is not related to the above characteristics when controlling for maturity.
Forthcoming, Journal of Finance
The Size–Power Tradeoff in HAR Inference, with James H. Stock and Daniel J. Lewis, September 2021
Abstract + | Online Appendix | Working Paper Version (with additional results) | Replication Files
Heteroskedasticity and autocorrelation-robust (HAR) inference in time series regression typically involves kernel estimation of the long-run variance. Conventional wisdom holds that, for a given kernel, the choice of truncation parameter trades off a test’s null rejection rate and power, and that this tradeoff differs across kernels. We formalize this intuition: using higher-order expansions, we provide a unified size-power frontier for both kernel and weighted orthonormal series tests using nonstandard “fixed-b” critical values. We also provide a frontier for the subset of these tests for which the fixed-b distribution is t or F. These frontiers are respectively achieved by the QS kernel and equal-weighted periodogram. The frontiers have simple closed-form expressions, which upon evaluation show that the price paid for restricting attention to tests with t and F critical values is small. The frontiers are derived for the Gaussian multivariate location model, but simulations suggest the qualitative findings extend to stochastic regressors.
Econometrica (2021), Vol. 89, No. 5, 2497–2516
HAR Inference: Recommendations for Practice, with James H. Stock, Daniel J. Lewis, and Mark W. Watson, October 2018
Abstract + | Replication Files
The classic papers by Newey and West (1987) and Andrews (1991) spurred a large body of work on how to improve heteroscedasticity- and autocorrelation-robust (HAR) inference in time-series regression. This literature finds that using a larger-than-usual truncation parameter to estimate the long-run variance, combined with Kiefer–Vogelsang (2002, 2005) fixed-b critical values, can substantially reduce size distortions, at only a modest cost in (size-adjusted) power. Empirical practice, however, has not kept up. This article therefore draws on the post-Newey–West/Andrews literature to make concrete recommendations for HAR inference. We derive truncation parameter rules that choose a point on the size-power tradeoff to minimize a loss function. If Newey–West tests are used, we recommend the truncation parameter rule S = 1.3T1/2 and (nonstandard) fixed-b critical values. For tests of a single restriction, we find advantages to using the equal-weighted cosine (EWC) test, where the long run variance is estimated by projections onto Type-II cosines, using ν = 0.4T2/3 cosine terms; for this test, fixed-b critical values are, conveniently, tν or F. We assess these rules using first an ARMA/GARCH Monte Carlo design, then a dynamic factor model design estimated using 207 quarterly U.S. macroeconomic time series.
Journal of Business & Economic Statistics (2018), Vol. 36, No. 4, 541–559
Spatial Clustering During Memory Search, with Jonathan F. Miller, Sean M. Polyn, and Michael J. Kahana, May 2013 (from a previous life!)
In recalling a list of previously experienced items, participants are known to organize their responses on the basis of the items’ semantic and temporal similarities. Here, we examine how spatial information influences the organization of responses in free recall. In Experiment 1, participants studied and subsequently recalled lists of landmarks. In Experiment 2, participants played a game in which they delivered objects to landmarks in a virtual environment and later recalled the delivered objects. Participants in both experiments were simply asked to recall as many items as they could remember in any order. By analyzing the conditional probabilities of recall transitions, we demonstrate strong spatial and temporal organization of studied items in both experiments.
Journal of Experimental Psychology: Learning, Memory, and Cognition (2013), Vol. 39, No. 3, 773–781
Working Papers & Work in Progress
Does the Market Understand Time Variation in the Equity Premium?, with Mihir Gandhi and Niels J. Gormsen, October 2022
Abstract + | Slides
We test whether the market has intertemporally consistent expectations about time variation in the equity premium. We use option prices to estimate the expected future equity premium (the forward rate) and compare this estimate to the equity premium estimated in the future (the future spot rate). Forward rates are strong predictors of future spot rates for most stochastic discount factors, suggesting that the representative investor understands the direction in which the equity premium is expected to move in the future. We can, however, reject fully rational expectations for many specifications of the stochastic discount factor. The expectation errors can be explained by a model in which an increase in the equity premium causes investors to overestimate the future equity premium. Alternatively, the apparent errors can be rationalized by a stochastic discount factor that features a highly volatile and countercyclical price of discount-rate risk.
A New Test of Excess Movement in Asset Prices, with Ned Augenblick, August 2022
Abstract + | Online Appendix | Slides
We derive new bounds on the rational variation in asset prices over time. The resulting test requires no proxy for fundamental value, and it allows significantly more flexibility in preferences and discount rates than in standard volatility tests. We gain traction by focusing specifically on risk-neutral beliefs implied by option prices. The core insight is that movement in physical beliefs must be bounded, and although risk preferences distort prices away from beliefs, it is still possible to place bounds on risk-neutral belief movement. Aside from rational expectations, our main assumption is that the slope of the stochastic discount factor is locally constant within an option contract. We show that this assumption allows for an informative null, and it is satisfied in a range of standard frameworks. Implementing our test empirically using index options, we find that there is so much movement in risk-neutral beliefs that the bounds are routinely violated.
Replaces & subsumes “Restrictions on Asset-Price Movements Under Rational Expectations: Theory and Evidence” (previous draft)
Horizon-Dependent Risk Pricing: Evidence from Short-Dated Options, March 2019
I present evidence from index options that the price of risk over the value of the S&P 500 increases as the investment horizon becomes shorter. I show first how these risk prices may be estimated from the data, by translating the risk-neutral probabilities implied by options prices into physical probabilities that must provide unbiased forecasts of the terminal outcome. The risk price can be interpreted as the marginal investor’s effective risk aversion, and estimating this value over different option-expiration horizons for the S&P, I find that risk aversion is reliably higher for near-term outcomes than for longer-term outcomes: the market’s relative risk aversion over terminal index values decreases from around 15 at a one-week horizon to around 3 at a 12-week horizon. It is difficult to reconcile these findings with leading asset-pricing models, and I discuss necessary conditions for any such rational model to produce such a pattern. Models with dynamically inconsistent risk preferences, however, are capable of straightforwardly producing the findings presented here, and I discuss possible specifications of such models and their applicability to related results from previous literature.
High Valuations and Low Growth: Low-Frequency Evidence in the Time Series and Cross Section, 2022