MétaCan
Menu
Back to cohort
Record W3099227813 · doi:10.3982/ecta17038

Dynamic Noisy Rational Expectations Equilibrium With Insider Information

2020· article· en· W3099227813 on OpenAlexaff
Jérôme Detemple, Marcel Rindisbacher, Scott Robertson

Bibliographic record

VenueEconometrica · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicStochastic processes and financial applications
Canadian institutionsQuest University Canada
FundersNational Science Foundation
KeywordsEconomicsRational expectationsInformation asymmetryAsset (computer security)Private information retrievalInsider tradingInsiderMicroeconomicsMathematical economicsEconometricsCapital asset pricing modelBounded functionFinancial economicsComputer scienceFinanceMathematics

Abstract

fetched live from OpenAlex

We study equilibria in multi‐asset and multi‐agent continuous‐time economies with asymmetric information and bounded rational noise traders. We establish the existence of two equilibria. First, a full communication equilibrium where the informed agents' signal is disclosed to the market and static policies are optimal. Second, a partial communication equilibrium where the signal disclosed is affine in the informed and noise traders' signals, and dynamic policies are optimal. Here, information asymmetry creates demand for two public funds, as well as a dark pool where private information trades can be implemented. Markets are endogenously complete and equilibrium returns have a three factor structure with stochastic factors and loadings. Results are valid for constant absolute risk averse investors, general vector diffusions for fundamentals, nonlinear terminal payoffs, and non‐Gaussian noise trading. Asset price dynamics and public information flows are endogenous, and rational expectations equilibria are special cases of the general results.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.203
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueEconometricaSame topicStochastic processes and financial applicationsFrench-language works237,207