2023

We identify a novel economic mechanism through which passive ownership positively affects informational efficiency in the cross-section of firms. Passive investors’ inelastic demand lowers a firm’s cost-of-capital, inducing it to take more risk. The higher cash flow variance, in turn, incentivizes active investors to acquire more precise private information, pushing up price informativeness for firms with high passive ownership. High passive ownership also implies higher stock prices and higher stock-return variances. An increase in the aggregate size of passive investors amplifies these cross-sectional differences. We also document complementarities in firms’ real investment and investors’ information choices that can cause information crashes.

We develop an asset pricing model with endogenous corporate policies that explains how inflation jointly affects real asset prices and corporate default risk. Our model includes two empirically founded nominal rigidities: fixed nominal debt coupons (sticky leverage) and sticky cash flows. These two frictions result in lower real equity prices and credit spreads when expected inflation rises. A decrease in expected inflation has opposite effects, with even larger magnitudes. In the cross-section, the model predicts that the negative impact of higher expected inflation on real equity values is stronger for low leverage firms. We find empirical support for the model’s predictions.

We provide the asymptotic distributional theory for the so-called General or Generalized Dynamic Factor Model (GDFM), laying the foundations for an inferential approach in the GDFM analysis of high-dimensional time series. By exploiting the duality between common shocks and dynamic loadings, we derive the asymptotic distribution and associated standard errors for a class of estimators for common shocks, dynamic loadings, common components, and impulse response functions. We present an empirical application aimed at constructing a “core” inflation indicator for the U.S. economy, which demonstrates the superiority of the GDFM-based indicator over the most common approaches, particularly the one based on Principal Components.

We present a method to estimate price impact in order-driven markets that does not require averaging over executions or scenarios. Given order book data associated with one single execution of a sell metaorder, we estimate its contribution to price decrease during the trade. We do so by modelling the limit order book using a state-dependent Hawkes process, and by defining the price impact profile of the execution as a function of the compensator of the state-dependent Hawkes process. We apply our method to a dataset from NASDAQ, and we conclude that the scheduling of sell child orders has a bigger impact on price than their sizes.

Previous literature shows that prevalent risk measures such as value at risk or expected shortfall are ineffective to curb excessive risk-taking by a tail-risk-seeking trader with S-shaped utility function in the context of portfolio optimisation. However, these conclusions hold only when the constraints are static in the sense that the risk measure is just applied to the terminal portfolio value. In this paper, we consider a portfolio optimisation problem featuring S-shaped utility and a dynamic risk constraint which is imposed throughout the entire trading horizon. Provided that the risk control policy is sufficiently strict relative to the Sharpe ratio of the asset, the trader’s portfolio strategies and the resulting maximal expected utility can be effectively constrained by a dynamic risk measure. Finally, we argue that dynamic risk constraints might still be ineffective if the trader has access to a derivatives market.

We investigate whether it is possible to formulate option pricing and hedging models without using probability. We present a model that is consistent with two notions of volatility: a historical volatility consistent with statistical analysis, and an implied volatility consistent with options priced with the model. The latter will be also the quadratic variation of the model, a pathwise property. This first result, originally presented in Brigo and Mercurio (1998, 2000) [10, 11], is then connected with the recent work of Armstrong et al (2018, 2021) [1, 2], where using rough paths theory it is shown that implied volatility is associated with a purely pathwise lift of the stock dynamics involving no probability and no semimartingale theory in particular, leading to option models without probability. Finally, an intermediate result by Bender et al. (2008) [5] is recalled. Using semimartingale theory, Bender et al. showed that one could obtain option prices based only on the semimartingale quadratic variation of the model, a pathwise property, and highlighted the difference between historical and implied volatility. All three works confirm the idea that while historical volatility is a statistical quantity, implied volatility is a pathwise one. This leads to a 20 years mini-anniversary of pathwise pricing through 1998, 2008 and 2018, which is rather fitting for a talk presented at the conference for the 45 years of the Black, Scholes and Merton option pricing paradigm.

We develop a methodology for estimating and testing the effect of anomalies in conditional asset pricing models when premia are time-varying. Our method, which builds on the two-pass methodology, is developed for ordinary and weighted least-squares estimation, considering both cases of correct specification and global misspecification of the candidate asset pricing model. A cross-sectional R-squared test to dissect anomalies is proposed, establishing its limiting properties under the null hypothesis of no effect of anomalies and its alternative. Using a dataset of 20,000 individual US stock returns, we find that although anomalies are statistically significant in about half the cases (out of 170 anomalies), they explain a small fraction (less than 10%) of the cross-sectional variation of expected returns. Anomalies tend to be more important during economic and financial crises.

Our objective is to price the cross-section of asset returns. Despite considering hundreds of systematic risk factors (``factor zoo''), factor models still have sizable pricing errors. A limitation of these models is that returns compensate only for systematic risk. We allow compensation also for unsystematic risk while imposing no arbitrage. The resulting stochastic discount factor (SDF) dominates traditional factor models in pricing assets. Empirically, about 70% of this SDF's variation is explained by its unsystematic-risk component, which is correlated with strategies reflecting market frictions and behavioral biases. Our findings provide an avenue for resolving the factor zoo.

We study the distributional effects of asset ownership on price informativeness in a general equilibrium model featuring investors (oligopolists) with different degrees of price impact and ability to learn about individual asset payoffs from private signals as well as price signals, and competitive fringe that only learns from asset prices. We show that price informativeness is nonmonotonic in the oligopolists’ aggregate size, decreasing in the sector’s concentration and in the size of the passive oligopolistic sector. We further show that the size effect can be decomposed into a learning channel capturing investors’ quality of private signals and an information passthrough channel measuring the sensitivity of investors’ trades to private signals, with the latter one being the primary source of variation in price informativeness relative to the size distribution.

We propose two specifications of a real-time mixed-frequency semi-structural time series model for evaluating the output potential, output gap, Phillips curve, and Okun’s law for the US. The baseline model uses minimal theory-based multivariate identification restrictions to inform trend-cycle decomposition, while the alternative model adds the CBO’s output gap measure as an observed variable. The latter model results in a smoother output potential and lower cyclical correlation between inflation and real variables but performs worse in forecasting beyond the short term. This methodology allows for the assessment and real-time monitoring of official trend and gap estimates.

This paper generalises dynamic factor models for multidimensional dependent data. In doing so, it develops an interpretable technique to study complex information sources ranging from repeated surveys with a varying number of respondents to panels of satellite images. We specialise our results to model microeconomic data on US households jointly with macroeconomic aggregates. This results in a powerful tool able to generate localised predictions, counterfactuals and impulse response functions for individual households, accounting for traditional time-series complexities depicted in the statespace literature. The model is also compatible with the growing focus of policymakers for real-time economic analysis as it is able to process observations online, while handling missing values and asynchronous data releases.

This manuscript proposes to extend the information set of time-series regression trees with latent stationary factors extracted via state-space methods. In doing so, this approach generalises time-series regression trees on two dimensions. First, it allows to handle predictors that exhibit measurement error, non-stationary trends, seasonality and/or irregularities such as missing observations. Second, it gives a transparent way for using domain-specific theory to inform time-series regression trees. Empirically, ensembles of these factor-augmented trees provide a reliable approach for macro-finance problems. This article highlights it focussing on the lead-lag effect between equity volatility and the business cycle in the United States.

This article proposes a generalisation of the delete-d jackknife to solve hyperparameter selection problems for time series. I call it artificial delete-d jackknife to stress that this approach substitutes the classic removal step with a fictitious deletion, wherein observed datapoints are replaced with artificial missing values. This procedure keeps the data order intact and allows plain compatibility with time series. This manuscript justifies the use of this approach asymptotically and shows its finite-sample advantages through simulation studies. Besides, this article describes its real-world advantages by regulating high-dimensional forecasting models for foreign exchange rates.