Pub Date : 2025-12-18DOI: 10.1016/j.jeconom.2025.106167
Bin Chen , Yuefeng Han , Qiyang Yu
High-dimensional tensor-valued data have recently gained attention from researchers in economics and finance. We consider the estimation and inference of high-dimensional tensor factor models, where each dimension of the tensor diverges. Our focus is on a factor model that admits CP-type tensor decomposition, which allows for non-orthogonal loading vectors. Based on the contemporary covariance matrix, we propose an iterative simultaneous projection estimation method. Our estimator is robust to weak dependence among factors and weak correlation across different dimensions in the idiosyncratic shocks. We establish an inferential theory, demonstrating both consistency and asymptotic normality under relaxed assumptions. Within a unified framework, we consider two eigenvalue ratio-based estimators for the number of factors in a tensor factor model and justify their consistency. Simulation studies confirm the theoretical results and an empirical application to sorted portfolios reveals three important factors: a market factor, a long-short factor, and a volatility factor.
{"title":"Estimation and inference for CP tensor factor models","authors":"Bin Chen , Yuefeng Han , Qiyang Yu","doi":"10.1016/j.jeconom.2025.106167","DOIUrl":"10.1016/j.jeconom.2025.106167","url":null,"abstract":"<div><div>High-dimensional tensor-valued data have recently gained attention from researchers in economics and finance. We consider the estimation and inference of high-dimensional tensor factor models, where each dimension of the tensor diverges. Our focus is on a factor model that admits CP-type tensor decomposition, which allows for non-orthogonal loading vectors. Based on the contemporary covariance matrix, we propose an iterative simultaneous projection estimation method. Our estimator is robust to weak dependence among factors and weak correlation across different dimensions in the idiosyncratic shocks. We establish an inferential theory, demonstrating both consistency and asymptotic normality under relaxed assumptions. Within a unified framework, we consider two eigenvalue ratio-based estimators for the number of factors in a tensor factor model and justify their consistency. Simulation studies confirm the theoretical results and an empirical application to sorted portfolios reveals three important factors: a market factor, a long-short factor, and a volatility factor.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106167"},"PeriodicalIF":4.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-10DOI: 10.1016/j.jeconom.2025.106164
Adam Dearing
We provide new non-parametric identification results for stationary dynamic discrete choice models, where both the flow utilities and the distribution of unobserved shocks are fully non-parametric. Our main identification result establishes that a multinomial choice model is non-parametrically identified when there is a special regressor that (i) has a known derivative in the utility function (e.g., enters utility quasi-linearly); (ii) only affects the evolution of the other variables indirectly through the policy function; and (iii) exhibits a type of bounded persistence. To our knowledge, this is the first non-parametric identification result for stationary models that does not require any state variable to exhibit a form of serial independence. Our identification arguments map conditional choice probabilities and the state transition process into structural primitives, and they can be applied to models with persistent unobserved heterogeneity. Our identification results have broad applicability in practice, since candidate variables for the special regressor are already common in the empirical literature.
{"title":"Non-Parametric identification of stationary dynamic discrete choicemodels","authors":"Adam Dearing","doi":"10.1016/j.jeconom.2025.106164","DOIUrl":"10.1016/j.jeconom.2025.106164","url":null,"abstract":"<div><div>We provide new non-parametric identification results for stationary dynamic discrete choice models, where both the flow utilities and the distribution of unobserved shocks are fully non-parametric. Our main identification result establishes that a multinomial choice model is non-parametrically identified when there is a special regressor that (i) has a known derivative in the utility function (e.g., enters utility quasi-linearly); (ii) only affects the evolution of the other variables indirectly through the policy function; and (iii) exhibits a type of bounded persistence. To our knowledge, this is the first non-parametric identification result for stationary models that does not require any state variable to exhibit a form of serial independence. Our identification arguments map conditional choice probabilities and the state transition process into structural primitives, and they can be applied to models with persistent unobserved heterogeneity. Our identification results have broad applicability in practice, since candidate variables for the special regressor are already common in the empirical literature.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106164"},"PeriodicalIF":4.0,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-08DOI: 10.1016/j.jeconom.2025.106161
Luciano de Castro , Antonio F. Galvao , Hirofumi Ota
This paper develops a novel economic model and econometric methods to jointly identify and estimate parameters related to intertemporal preference and risk attitude. We begin by formulating an intertemporal consumption model with multiple assets based on dynamic quantile preferences that account for elasticity of intertemporal substitution, risk attitude, and discount factor. We establish the properties of the model and obtain interesting explicit expressions for the value function, and the optimal consumption. In addition, we derive the quantile Euler equation. From this equilibrium condition, we show that, when at least two returns are available, one is able to separately identify the risk attitude, which is measured by the quantile τ, and the elasticity of intertemporal substitution and discount factor. We propose new econometric theory for estimating these parameters of interest and establish the statistical properties of the semiparametric two-step estimator. In particular, we show that the estimator is consistent, with a cubic-root rate of convergence, derive its limiting distribution, and suggest a subsampling procedure for inference. Finally, we empirically estimate the structural model, and results show evidence that discount factor is slightly smaller than one, the elasticity of intertemporal substitution is larger than one, and risk attitude is close to the median.
{"title":"Quantile approach to intertemporal consumption with multiple assets","authors":"Luciano de Castro , Antonio F. Galvao , Hirofumi Ota","doi":"10.1016/j.jeconom.2025.106161","DOIUrl":"10.1016/j.jeconom.2025.106161","url":null,"abstract":"<div><div>This paper develops a novel economic model and econometric methods to jointly identify and estimate parameters related to intertemporal preference and risk attitude. We begin by formulating an intertemporal consumption model with multiple assets based on dynamic quantile preferences that account for elasticity of intertemporal substitution, risk attitude, and discount factor. We establish the properties of the model and obtain interesting explicit expressions for the value function, and the optimal consumption. In addition, we derive the quantile Euler equation. From this equilibrium condition, we show that, when at least two returns are available, one is able to separately identify the risk attitude, which is measured by the quantile <em>τ</em>, and the elasticity of intertemporal substitution and discount factor. We propose new econometric theory for estimating these parameters of interest and establish the statistical properties of the semiparametric two-step estimator. In particular, we show that the estimator is consistent, with a cubic-root rate of convergence, derive its limiting distribution, and suggest a subsampling procedure for inference. Finally, we empirically estimate the structural model, and results show evidence that discount factor is slightly smaller than one, the elasticity of intertemporal substitution is larger than one, and risk attitude is close to the median.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106161"},"PeriodicalIF":4.0,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-06DOI: 10.1016/j.jeconom.2025.106104
Brendan Kline
This paper develops identification results for the distribution of valuations in a class of allocation-transfer games. These games determine an allocation of units of a valuable object and arrangement of monetary transfers on the basis of the actions taken by the players. The results allow dependent valuations, discrete parts of the action space, non-smoothness, and unknown (to the econometrician, prior to observing the data) details of how the allocations and transfers are determined. The identification strategy is based on the assumption of a single monotone equilibrium used in the data, in which players use strategies that are weakly increasing functions of their valuations for the object being allocated. As extensions, the identification strategy accommodates certain relaxations of the equilibrium assumption, while maintaining the assumption of the use of monotone strategies.
{"title":"Identification of incomplete information allocation-transfer games in monotone equilibrium","authors":"Brendan Kline","doi":"10.1016/j.jeconom.2025.106104","DOIUrl":"10.1016/j.jeconom.2025.106104","url":null,"abstract":"<div><div>This paper develops identification results for the distribution of valuations in a class of allocation-transfer games. These games determine an allocation of units of a valuable object and arrangement of monetary transfers on the basis of the actions taken by the players. The results allow dependent valuations, discrete parts of the action space, non-smoothness, and unknown (to the econometrician, prior to observing the data) details of how the allocations and transfers are determined. The identification strategy is based on the assumption of a single monotone equilibrium used in the data, in which players use strategies that are weakly increasing functions of their valuations for the object being allocated. As extensions, the identification strategy accommodates certain relaxations of the equilibrium assumption, while maintaining the assumption of the use of monotone strategies.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106104"},"PeriodicalIF":4.0,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145690563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-05DOI: 10.1016/j.jeconom.2025.106160
Federico A. Bugni , Ivan A. Canay , Steve McBride
This paper studies settings where the analyst is interested in identifying and estimating the average direct causal effect of a binary treatment on an outcome. We consider a setup in which the outcome realization does not get immediately realized after the treatment assignment, a feature that is ubiquitous in empirical settings. The period between the treatment and the realization of the outcome allows other observed actions to occur and affect the outcome. In this context, we study several regression-based estimands routinely used in empirical work to capture the average treatment effect and shed light on interpreting them in terms of ceteris paribus effects, indirect causal effects, and selection terms. We obtain three main and related takeaways under a common set of assumptions. First, the three most popular estimands do not generally satisfy what we call strong sign preservation, in the sense that these estimands may be negative even when the treatment positively affects the outcome conditional on any possible combination of other actions. Second, the most popular regression that includes the other actions as controls satisfies strong sign preservation if and only if these actions are mutually exclusive binary variables. Finally, we show that a linear regression that fully stratifies the other actions leads to estimands that satisfy strong sign preservation.
{"title":"Decomposition and interpretation of treatment effects in settings with delayed outcomes","authors":"Federico A. Bugni , Ivan A. Canay , Steve McBride","doi":"10.1016/j.jeconom.2025.106160","DOIUrl":"10.1016/j.jeconom.2025.106160","url":null,"abstract":"<div><div>This paper studies settings where the analyst is interested in identifying and estimating the average <em>direct</em> causal effect of a binary treatment on an outcome. We consider a setup in which the outcome realization does not get immediately realized after the treatment assignment, a feature that is ubiquitous in empirical settings. The period between the treatment and the realization of the outcome allows other observed actions to occur and affect the outcome. In this context, we study several regression-based estimands routinely used in empirical work to capture the average treatment effect and shed light on interpreting them in terms of ceteris paribus effects, indirect causal effects, and selection terms. We obtain three main and related takeaways under a common set of assumptions. First, the three most popular estimands do not generally satisfy what we call <em>strong sign preservation</em>, in the sense that these estimands may be negative even when the treatment positively affects the outcome conditional on any possible combination of other actions. Second, the most popular regression that includes the other actions as controls satisfies strong sign preservation <em>if and only if</em> these actions are mutually exclusive binary variables. Finally, we show that a linear regression that fully stratifies the other actions leads to estimands that satisfy strong sign preservation.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106160"},"PeriodicalIF":4.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145690430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-05DOI: 10.1016/j.jeconom.2025.106154
Weichi Wu , Zhou Zhou , Yongmiao Hong
This paper considers estimation of and testing for a class of locally stationary time series factor models with evolutionary dynamics, where the entries and dimension of the factor loading matrix are allowed to vary with time while the factors and idiosyncratic components are locally stationary. We propose an adaptive sieve estimator for the span of the time-varying loading matrix of a locally stationary factor process. A uniformly consistent estimator of the effective number of factors is developed via eigenanalysis of a non-negative definite time-varying matrix. We also propose a possibly high-dimensional bootstrap test for the hypothesis of constant factor loadings by comparing the kernels of the covariance matrices of the whole time series with their local counterparts. This test avoids the assumption that factors and idiosyncratic errors are stationary or the covariance matrix of factors is time-invariant. Our results cover both cases of white noise idiosyncratic errors and serially correlated idiosyncratic errors. We examine the finite sample performance of our proposed estimator and test via simulation studies and real data analysis.
{"title":"Inference for time-varying factor models under local stationarity","authors":"Weichi Wu , Zhou Zhou , Yongmiao Hong","doi":"10.1016/j.jeconom.2025.106154","DOIUrl":"10.1016/j.jeconom.2025.106154","url":null,"abstract":"<div><div>This paper considers estimation of and testing for a class of locally stationary time series factor models with evolutionary dynamics, where the entries and dimension of the factor loading matrix are allowed to vary with time while the factors and idiosyncratic components are locally stationary. We propose an adaptive sieve estimator for the span of the time-varying loading matrix of a locally stationary factor process. A uniformly consistent estimator of the effective number of factors is developed via eigenanalysis of a non-negative definite time-varying matrix. We also propose a possibly high-dimensional bootstrap test for the hypothesis of constant factor loadings by comparing the kernels of the covariance matrices of the whole time series with their local counterparts. This test avoids the assumption that factors and idiosyncratic errors are stationary or the covariance matrix of factors is time-invariant. Our results cover both cases of white noise idiosyncratic errors and serially correlated idiosyncratic errors. We examine the finite sample performance of our proposed estimator and test via simulation studies and real data analysis.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106154"},"PeriodicalIF":4.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145690428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-03DOI: 10.1016/j.jeconom.2025.106162
Hongfei Wang , Ping Zhao , Long Feng , Zhaojun Wang
{"title":"Corrigendum to “Robust mutual fund selection with false discovery rate control” [Journal of Econometrics 252 (2025) 106121]","authors":"Hongfei Wang , Ping Zhao , Long Feng , Zhaojun Wang","doi":"10.1016/j.jeconom.2025.106162","DOIUrl":"10.1016/j.jeconom.2025.106162","url":null,"abstract":"","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106162"},"PeriodicalIF":4.0,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145690429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-28DOI: 10.1016/j.jeconom.2025.106155
Leopoldo Catania , Enzo D’Innocenzo , Alessandra Luati
State estimation in unobserved component models with parameter uncertainty is traditionally performed through approximate filters, where Gaussian distributions with given moments are employed to replace otherwise intractable conditional densities. This paper re-examines signal-plus-noise models where parameter uncertainty is induced by a latent variable that may assume a fixed number of states. First, it is shown that, for these models, the approximate filters commonly adopted in the literature can be obtained as linear combinations of minimum variance linear unbiased estimators. Second, it is observed that they coincide with filters implied by a novel class of dynamic adaptive mixture models, where the parameters of a mixture of distributions evolve over time following a recursion that is based on the score of the one-step-ahead predictive distribution. Focusing on a robust specification, where the mixture components are Student’s t distributions, we prove existence, stationarity, and ergodicity of the data generating process as well as invertibility of the filter, and consistency and asymptotic normality of the maximum likelihood estimator of the static parameters. An application to energy spot prices is discussed, where the novel specification is compared with, and shown to outperform, robust score-driven filters and the related class of mixture autoregressive models.
{"title":"Unobserved component models, approximate filters and dynamic adaptive mixture models","authors":"Leopoldo Catania , Enzo D’Innocenzo , Alessandra Luati","doi":"10.1016/j.jeconom.2025.106155","DOIUrl":"10.1016/j.jeconom.2025.106155","url":null,"abstract":"<div><div>State estimation in unobserved component models with parameter uncertainty is traditionally performed through approximate filters, where Gaussian distributions with given moments are employed to replace otherwise intractable conditional densities. This paper re-examines signal-plus-noise models where parameter uncertainty is induced by a latent variable that may assume a fixed number of states. First, it is shown that, for these models, the approximate filters commonly adopted in the literature can be obtained as linear combinations of minimum variance linear unbiased estimators. Second, it is observed that they coincide with filters implied by a novel class of dynamic adaptive mixture models, where the parameters of a mixture of distributions evolve over time following a recursion that is based on the score of the one-step-ahead predictive distribution. Focusing on a robust specification, where the mixture components are Student’s <em>t</em> distributions, we prove existence, stationarity, and ergodicity of the data generating process as well as invertibility of the filter, and consistency and asymptotic normality of the maximum likelihood estimator of the static parameters. An application to energy spot prices is discussed, where the novel specification is compared with, and shown to outperform, robust score-driven filters and the related class of mixture autoregressive models.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106155"},"PeriodicalIF":4.0,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145621626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-24DOI: 10.1016/j.jeconom.2025.106153
Woosik Gong , Myung Hwan Seo
This paper develops valid bootstrap inference methods for the dynamic short panel threshold regression. We show that the standard nonparametric bootstrap is inconsistent for the first-differenced generalized method of moments (GMM) estimator. The inconsistency arises from an -consistent non-normal asymptotic distribution of the threshold estimator when the true parameter lies in the continuity region of the parameter space, which stems from the rank deficiency of the approximate Jacobian of the sample moment conditions on the continuity region. To address this, we propose a grid bootstrap to construct confidence intervals for the threshold and a residual bootstrap to construct confidence intervals for the coefficients. They are shown to be valid regardless of the model’s continuity. Moreover, we establish a uniform validity for the grid bootstrap. A set of Monte Carlo experiments compares the proposed bootstraps with the standard nonparametric bootstrap. An empirical application to a firm investment model illustrates our methods.
{"title":"Bootstraps for dynamic panel threshold models","authors":"Woosik Gong , Myung Hwan Seo","doi":"10.1016/j.jeconom.2025.106153","DOIUrl":"10.1016/j.jeconom.2025.106153","url":null,"abstract":"<div><div>This paper develops valid bootstrap inference methods for the dynamic short panel threshold regression. We show that the standard nonparametric bootstrap is inconsistent for the first-differenced generalized method of moments (GMM) estimator. The inconsistency arises from an <span><math><msup><mrow><mi>n</mi></mrow><mrow><mn>1</mn><mo>/</mo><mn>4</mn></mrow></msup></math></span>-consistent non-normal asymptotic distribution of the threshold estimator when the true parameter lies in the continuity region of the parameter space, which stems from the rank deficiency of the approximate Jacobian of the sample moment conditions on the continuity region. To address this, we propose a grid bootstrap to construct confidence intervals for the threshold and a residual bootstrap to construct confidence intervals for the coefficients. They are shown to be valid regardless of the model’s continuity. Moreover, we establish a uniform validity for the grid bootstrap. A set of Monte Carlo experiments compares the proposed bootstraps with the standard nonparametric bootstrap. An empirical application to a firm investment model illustrates our methods.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106153"},"PeriodicalIF":4.0,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145621625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-20DOI: 10.1016/j.jeconom.2025.106131
Felipe Asencio , Alejandro Bernales , Daniel González , Richard Holowczak , Thanos Verousis
We develop a multi-asset model to decompose informed trading into the components concerning the underlying stock-value and the volatility in equity options. We isolate the stock-value and volatility components by characterizing their distinct intraday price responses in contracts with different option deltas and vegas, respectively. The stock-value (volatility) component represents on average 41 % (19 %) of the option spread, which remains substantial under various statistical validity analyses and robustness checks. In daily empirical applications, we also show that volatility-informed trading anticipates a 'Volmageddon' high-volatility event, and straddle trades are positively associated with volatility-informed trading.
{"title":"Decomposing informed trading in equity options","authors":"Felipe Asencio , Alejandro Bernales , Daniel González , Richard Holowczak , Thanos Verousis","doi":"10.1016/j.jeconom.2025.106131","DOIUrl":"10.1016/j.jeconom.2025.106131","url":null,"abstract":"<div><div>We develop a multi-asset model to decompose informed trading into the components concerning the underlying stock-value and the volatility in equity options. We isolate the stock-value and volatility components by characterizing their distinct intraday price responses in contracts with different option <em>deltas</em> and <em>vegas</em>, respectively. The stock-value (volatility) component represents on average 41 % (19 %) of the option spread, which remains substantial under various statistical validity analyses and robustness checks. In daily empirical applications, we also show that volatility-informed trading anticipates a 'Volmageddon' high-volatility event, and straddle trades are positively associated with volatility-informed trading.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106131"},"PeriodicalIF":4.0,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145577888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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