Christopher G Lamoureux
Biographic Data
| ID | 8920740 |
|---|---|
| NAME | Christopher G Lamoureux |
| GIVEN NAMES | Christopher G |
| FAMILY NAME | Lamoureux |
| SIGNATURE | LAMOUREUX C G |
| AFFILIATIONS | Washington University in St. Louis |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 1989 |
| LATEST PUBLICATION YEAR | 1994 |
| H-INDEX | 0 |
Endogenous Trading Volume and Momentum in Stock-Return Volatility
This article examines the ability of volume data to shed light on the source of persistence in stock-return volatility. A mixture model, in which a latent common factor restricts the joint density of volume and returns, is used to relax the assumption of exogenous volume used in previous studies. We use a point-in-time signal-extraction procedure to identify this latent process and a calibrated simulation to conduct analysis of the viability of t…
Persistence in Variance, Structural Change, and the GARCH Model
This article examines the persistence of the variance, as measured by the generalized autoregressive conditional heteroskedasticity (GARCH) model, in stock-return data. In particular, we investigate the extent to which persistence in variance may be overstated because of the existence of, and failure to take account of, deterministic structural shifts in the model. Both an analysis of daily stock-return data and a Monte Carlo simulation experimen…
Estimation of Stable-Law Parameters
The stable distribution has many desirable properties and is applicable in many areas of scientific pursuit (e.g., the study of stock-return behavior). Despite this, little is known about the properties of the various extant estimation techniques for the parameters of the stable laws. This article compares the iterative regression technique with the latest version of the fractile technique, using both simulated and actual data
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Estimation of Stable-Law Parameters
The stable distribution has many desirable properties and is applicable in many areas of scientific pursuit (e.g., the study of stock-return behavior). Despite this, little is known about the properties of the various extant estimation techniques for the parameters of the stable laws. This article compares the iterative regression technique with the latest version of the fractile technique, using both simulated and actual data
Persistence in Variance, Structural Change, and the GARCH Model
This article examines the persistence of the variance, as measured by the generalized autoregressive conditional heteroskedasticity (GARCH) model, in stock-return data. In particular, we investigate the extent to which persistence in variance may be overstated because of the existence of, and failure to take account of, deterministic structural shifts in the model. Both an analysis of daily stock-return data and a Monte Carlo simulation experimen…
Endogenous Trading Volume and Momentum in Stock-Return Volatility
This article examines the ability of volume data to shed light on the source of persistence in stock-return volatility. A mixture model, in which a latent common factor restricts the joint density of volume and returns, is used to relax the assumption of exogenous volume used in previous studies. We use a point-in-time signal-extraction procedure to identify this latent process and a calibrated simulation to conduct analysis of the viability of t…
Econometrics (3 works) · Economics (3 works) · Financial Risk and Volatility Modeling (3 works) · Mathematics (3 works) · Statistics (3 works) · Complex Systems and Time Series Analysis (2 works) · Market Dynamics and Volatility (2 works) · Autoregressive conditional heteroskedasticity (1 work) · Autoregressive model (1 work) · Computer Science (1 work)