Harry J Turtle
Biographic Data
| ID | 3464716 |
|---|---|
| NAME | Harry J Turtle |
| GIVEN NAMES | Harry J |
| FAMILY NAME | Turtle |
| SIGNATURE | TURTLE H J |
| AFFILIATIONS | National Bureau of Economic Research |
| ORCID | 0000-0003-0703-9824 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2000 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 0 |
Molecular Genetics, Risk Aversion, Return Perceptions, and Stock Market Participation
We show that molecular variation in DNA related to cognition, personality, health, and body shape, predicts an individual's equity market participation and risk aversion.Moreover, the molecular genetic endowments predict individuals' return perceptions, most of which we find to be strikingly biased.The genetic endowments also strongly associate with many of the investor characteristics (e.g., trust, sociability, wealth) shown to explain heterogen…
Modeling Conditional Covariances With Economic Information Instruments
We propose a new model for conditional covariances based on predetermined idiosyncratic shocks as well as macroeconomic and own information instruments. The specification ensures positive definiteness by construction, is unique within the class of linear functions for our covariance decomposition, and yields a simple yet rich model of covariances. We introduce a property, invariance to variate order, that assures estimation is not impacted by a s…
Semiparametric ARCH Models
We introduce the method of estimating functions to study the class of autoregressive conditional heteroscedasticity (ARCH) models. We derive the optimal estimating functions by combining linear and quadratic estimating functions. The resultant estimators are more efficient than the quasi-maximum likelihood estimator. If the assumption of conditional normality is imposed, the estimator obtained by using the theory of estimating functions is identi…
No prominent works on this page.
Semiparametric ARCH Models
We introduce the method of estimating functions to study the class of autoregressive conditional heteroscedasticity (ARCH) models. We derive the optimal estimating functions by combining linear and quadratic estimating functions. The resultant estimators are more efficient than the quasi-maximum likelihood estimator. If the assumption of conditional normality is imposed, the estimator obtained by using the theory of estimating functions is identi…
Modeling Conditional Covariances With Economic Information Instruments
We propose a new model for conditional covariances based on predetermined idiosyncratic shocks as well as macroeconomic and own information instruments. The specification ensures positive definiteness by construction, is unique within the class of linear functions for our covariance decomposition, and yields a simple yet rich model of covariances. We introduce a property, invariance to variate order, that assures estimation is not impacted by a s…
Molecular Genetics, Risk Aversion, Return Perceptions, and Stock Market Participation
We show that molecular variation in DNA related to cognition, personality, health, and body shape, predicts an individual's equity market participation and risk aversion.Moreover, the molecular genetic endowments predict individuals' return perceptions, most of which we find to be strikingly biased.The genetic endowments also strongly associate with many of the investor characteristics (e.g., trust, sociability, wealth) shown to explain heterogen…
Applied Mathematics (2 works) · Financial Risk and Volatility Modeling (2 works) · Mathematics (2 works) · Monetary Policy and Economic Impact (2 works) · Statistics (2 works) · Biology (1 work) · Business (1 work) · Complex Systems and Time Series Analysis (1 work) · Computer Science (1 work) · Covariance (1 work)