arXiv subjects

Aleh Tsyvinski

Publications and source records attributed to Aleh Tsyvinski.

16 recordsLinked to original sources

arXiv2026

Multidimensional Sorting: Comparative Statics

Characterizing multidimensional sorting problems is notoriously difficult - solutions are known only for a small number of examples. Our main results completely characterize the comparative statics of sorting and earnings with respect to technological change for a general class of multidimensional assignment models. We show that symmetric technological change passes fully into worker earnings, while antisymmetric change results only in reallocation. In general, both margins adjust, with relative importance governed by the complementarity between workers and jobs and their distributions. To quantitatively illustrate our theory, we quantify responses in sorting and earnings to cognitive skill-biased technological change for U.S. data.

econ.GN↗

arXiv2026

AI Premium

Using 380 trillion tokens of realized AI consumption across more than four hundred large language models from the licensed proprietary OpenRouter dataset covering approximately 2 percent of current global monthly AI token consumption, we analyze how AI affects firms, markets, and workers. Leveraging the unprecedented size, scope and granularity data, we construct the AI Factor from growth in tokens, dollars, and users, estimate firm-level AI Betas from stock return comovement, and characterize the \emph{AI Premium}. First, we build a high-frequency AI factor and decompose it into salient components. Second, we show that firms whose returns covary more positively with the AI factor -- high AI beta firms -- earn higher subsequent returns, and the AI premium is large and heterogeneous. A value-weighted long-short strategy earns 64.1 basis points per week, and the premium is large for loadings on the intensive, frontier-oriented margin of AI consumption -- closed-source models, paying and seasoned users, and long prompts -- but not on casual or open-weight use. Third, the premium reaches beyond technology firms into consumer-facing and capital-heavy parts of the economy, but is absent in emerging markets, including China. Fourth, the AI exposure is more positive in nonroutine interactive work and more negative in analytical, scientific, and operations-control skills -- an occupation one standard deviation higher in interaction-and-communication content has 0.36-standard-deviation higher market-implied AI exposure. Additionally, we provide early evidence of the rise of the agentic economy.

cs.CY↗

arXiv2026

Trading Frictions in Dynamic Cap-and-Trade Markets

We develop a dynamic stochastic model of markets with an externality and multiple trading frictions, and cap-and-trade as the leading application. Slow participation, limited intermediation, and heterogeneous information interact in equilibrium: agents choose costly market access, access determines residual compliance demand, intermediary constraints translate residual demand into a surrender-month premium, and the premium feeds back into access incentives. These interactions shape how effectively the market corrects the externality. We characterize access choices in closed form, prove that the equilibrium premium is unique, and show that endogenous access dampens the response to each friction in isolation, while the interaction of multiple frictions is non-additive and can amplify the price response. We quantify the model using 2.7 million EU ETS registry transactions and compliance records from 2005-2021. About 40% of operators do not trade annually, purchases concentrate in April when returns are systematically high, and operator flow predicts future returns.

econ.TH↗

arXiv2026

Higher-Order Asset Pricing Factors via Forward Selection Fama-MacBeth Regression

We show that the higher-order terms and interactions of the common sparse linear factors are significantly priced in the cross-section of equity returns. A higher-order model with only a small number of selected higher-order terms from six widely used factors outperforms traditional benchmarks both in-sample and out-of-sample. It also substantially reduces the alphas of the extensive factor zoo, suggesting that the pricing power of many zoo factors is attributable to their exposure to higher-order terms of common linear factors. We identify and rank the most relevant higher-order terms by developing a forward selection Fama-MacBeth procedure.

econ.EM↗

arXiv2026

Cryptocurrency as an Investable Asset Class: Coming of Age

We organize existing empirical regularities of cryptocurrencies into seven stylized facts and analyze cryptocurrencies through the lens of empirical asset pricing. We find important similarities with traditional markets--risk-adjusted performance so far is broadly comparable, and the cross-section of returns can be summarized by a small set of factors. However, cryptocurrency also has its own distinct character: jumps are frequent and large, and blockchain information helps drive prices. This common set of stylized facts provides evidence that cryptocurrency is emerging as an investable asset class. Additionally, we discuss potential data quality issues and possible changes in future regulations and the cryptocurrency environment.

q-fin.GN↗

arXiv2026

Triple/Double-Debiased Lasso

In this paper, we propose a triple (or double-debiased) Lasso estimator for inference on a low-dimensional parameter in high-dimensional linear regression models. The estimator is based on a moment function that satisfies not only first- but also second-order Neyman orthogonality conditions, thereby eliminating both the leading bias and the second-order bias induced by regularization. We derive an asymptotic linear representation for the proposed estimator and show that its remainder terms are never larger and are often smaller in order than those in the corresponding asymptotic linear representation for the standard double Lasso estimator. Because of this improvement, the triple Lasso estimator often yields more accurate finite-sample inference and confidence intervals with better coverage. Monte Carlo simulations confirm these gains. In addition, we provide a general recursive formula for constructing higher-order Neyman orthogonal moment functions in Z-estimation problems, which underlies the proposed estimator as a special case.

econ.EM↗

arXiv2025

Composite Sorting

This paper introduces an assignment model with concave costs of skill gaps, which arise generally when firms mitigate costs of mismatch as in Stigler (1939) and Laffont and Tirole (1986, 1991). Concave costs of skill gaps imply that the output function is neither supermodular nor submodular. We thus introduce a tractable model that interpolates between the polar canonical cases of supermodularity and submodularity. We characterize sorting, wages, and comparative statics and show these substantively differ from traditional assignment models. Under composite sorting: (1) distinct worker types work in the same occupation, and (2) worker types are simultaneously part of both positive and negative sorting. Quantitatively, our model can generate and help explain earnings dispersion between and within occupations.

econ.GN↗

arXiv2025

Bunching and Taxing Multidimensional Skills

We characterize optimal policy in a multidimensional nonlinear taxation model with bunching. We develop an empirically relevant model with cognitive and manual skills, firm heterogeneity, and labor market sorting. We first derive two conditions for the optimality of taxes that take into account bunching. The first condition $-$ a stochastic dominance optimal tax condition $-$ shows that at the optimum the schedule of benefits dominates the schedule of distortions in terms of second-order stochastic dominance. The second condition $-$ a global optimal tax formula $-$ provides a representation that balances the local costs and benefits of optimal taxation while explicitly accounting for global incentive constraints. Second, we use Legendre transformations to represent our problem as a linear program. This linearization allows us to solve the model quantitatively and to precisely characterize bunching. At an optimum, 10 percent of workers is bunched. We introduce two notions of bunching $-$ blunt bunching and targeted bunching. Blunt bunching constitutes 30 percent of all bunching, occurs at the lowest regions of cognitive and manual skills, and lumps the allocations of these workers resulting in a significant distortion. Targeted bunching constitutes 70 percent of all bunching and recognizes the workers' comparative advantage. The planner separates workers on their dominant skill and bunches them on their weaker skill, thus mitigating distortions along the dominant skill dimension.

econ.GN↗

arXiv2024

Inefficiencies of Carbon Trading Markets

The European Union Emission Trading System is a prominent market-based mechanism to reduce emissions. While the theory is well understood, we are the first to study the whole cap-and-trade mechanism as a financial market. Analyzing the universe of transactions in 2005-2020 (more than one million records of granular transaction data), we show that this market features significant inefficiencies undermining its goals. First, about 40% of firms never trade in a given year. Second, many firms only trade during surrendering months, when compliance is immediate and prices are predictably high. Third, a number of operators engage in speculative trading, exploiting private information.

q-fin.GN↗

arXiv2024

One Factor to Bind the Cross-Section of Returns

We propose a new non-linear single-factor asset pricing model $r_{it}=h(f_{t}λ_{i})+ε_{it}$. Despite its parsimony, this model represents exactly any non-linear model with an arbitrary number of factors and loadings -- a consequence of the Kolmogorov-Arnold representation theorem. It features only one pricing component $h(f_{t}λ_{I})$, comprising a nonparametric link function of the time-dependent factor and factor loading that we jointly estimate with sieve-based estimators. Using 171 assets across major classes, our model delivers superior cross-sectional performance with a low-dimensional approximation of the link function. Most known finance and macro factors become insignificant controlling for our single-factor.

q-fin.GN↗

arXiv2023

Sorting with Teams

We fully solve a sorting problem with heterogeneous firms and multiple heterogeneous workers whose skills are imperfect substitutes. We show that optimal sorting, which we call mixed and countermonotonic, is comprised of two regions. In the first region, mediocre firms sort with mediocre workers and coworkers such that the output losses are equal across all these teams (mixing). In the second region, a high skill worker sorts with low skill coworkers and a high productivity firm (countermonotonicity). We characterize the equilibrium wages and firm values. Quantitatively, our model can generate the dispersion of earnings within and across US firms.

econ.GN↗

arXiv2022

Beckmann's approach to multi-item multi-bidder auctions

We consider the problem of revenue-maximizing Bayesian auction design with several bidders having independent private values over several items. We show that it can be reduced to the problem of continuous optimal transportation introduced by Beckmann (1952) where the optimal transportation flow generalizes the concept of ironed virtual valuations to the multi-item setting. We establish the strong duality between the two problems and the existence of solutions. The results rely on insights from majorization and optimal transportation theories and on the characterization of feasible interim mechanisms by Hart and Reny (2015).

econ.TH↗

arXiv2022

Weighted-average quantile regression

In this paper, we introduce the weighted-average quantile regression framework, $\int_0^1 q_{Y|X}(u)ψ(u)du = X'β$, where $Y$ is a dependent variable, $X$ is a vector of covariates, $q_{Y|X}$ is the quantile function of the conditional distribution of $Y$ given $X$, $ψ$ is a weighting function, and $β$ is a vector of parameters. We argue that this framework is of interest in many applied settings and develop an estimator of the vector of parameters $β$. We show that our estimator is $\sqrt T$-consistent and asymptotically normal with mean zero and easily estimable covariance matrix, where $T$ is the size of available sample. We demonstrate the usefulness of our estimator by applying it in two empirical settings. In the first setting, we focus on financial data and study the factor structures of the expected shortfalls of the industry portfolios. In the second setting, we focus on wage data and study inequality and social welfare dependence on commonly used individual characteristics.

econ.EM↗

arXiv2021

Policy with stochastic hysteresis

The paper develops a general methodology for analyzing policies with path-dependency (hysteresis) in stochastic models with forward looking optimizing agents. Our main application is a macro-climate model with a path-dependent climate externality. We derive in closed form the dynamics of the optimal Pigouvian tax, that is, its drift and diffusion coefficients. The dynamics of the present marginal damages is given by the recently developed functional Itô formula. The dynamics of the conditional expectation process of the future marginal damages is given by a new total derivative formula that we prove. The total derivative formula represents the evolution of the conditional expectation process as a sum of the expected dynamics of hysteresis with respect to time, a form of a time derivative, and the expected dynamics of hysteresis with the shocks to the trajectory of the stochastic process, a form of a stochastic derivative. We then generalize the results. First, we propose a general class of hysteresis functionals that permits significant tractability. Second, we characterize in closed form the dynamics of the stochastic hysteresis elasticity that represents the change in the whole optimal policy process with an introduction of small hysteresis effects. Third, we determine the optimal policy process.

econ.TH↗

arXiv2020

On Vickrey's Income Averaging

We consider a small set of axioms for income averaging -- recursivity, continuity, and the boundary condition for the present. These properties yield a unique averaging function that is the density of the reflected Brownian motion with a drift started at the current income and moving over the past incomes. When averaging is done over the short past, the weighting function is asymptotically converging to a Gaussian. When averaging is done over the long horizon, the weighing function converges to the exponential distribution. For all intermediate averaging scales, we derive an explicit solution that interpolates between the two.

econ.TH↗

arXiv2019

Tax Mechanisms and Gradient Flows

We demonstrate how a static optimal income taxation problem can be analyzed using dynamical methods. Specifically, we show that the taxation problem is intimately connected to the heat equation. Our first result is a new property of the optimal tax which we call the fairness principle. The optimal tax at any income is invariant under a family of properly adjusted Gaussian averages (the heat kernel) of the optimal taxes at other incomes. That is, the optimal tax at a given income is equal to the weighted by the heat kernels average of optimal taxes at other incomes and income densities. Moreover, this averaging happens at every scale tightly linked to each other providing a unified weighting scheme at all income ranges. The fairness principle arises not due to equality considerations but rather it represents an efficient way to smooth the burden of taxes and generated revenues across incomes. Just as nature wants to distribute heat evenly, the optimal way for a government to raise revenues is to distribute the tax burden and raised revenues evenly among individuals. We then construct a gradient flow of taxes -- a dynamic process changing the existing tax system in the direction of the increase in tax revenues -- and show that it takes the form of a heat equation. The fairness principle holds also for the short-term asymptotics of the gradient flow, where the averaging is done over the current taxes. The gradient flow we consider can be viewed as a continuous process of a reform of the nonlinear income tax schedule and thus unifies the variational approach to taxation and optimal taxation. We present several other characteristics of the gradient flow focusing on its smoothing properties.

econ.TH↗