Eric Renault
Biographic Data
| ID | 8920779 |
|---|---|
| NAME | Eric Renault |
| GIVEN NAMES | Eric |
| FAMILY NAME | Renault |
| SIGNATURE | RENAULT E |
| AFFILIATIONS | Center for Interuniversity Research and Analysis on Organizations |
| ORCID | 0000-0001-6982-4671 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2000 |
| LATEST PUBLICATION YEAR | 2003 |
| H-INDEX | 0 |
Iterative and Recursive Estimation in Structural Nonadaptive Models
An inference method, called latent backfitting, is proposed. This method appears well suited for econometric models where the structural relationships of interest define the observed endogenous variables as a known function of unobserved state variables and unknown parameters. This nonlinear state-space specification paves the way for iterative or recursive EM-like strategies. In the E steps, the state variables are forecasted given the observati…
Statistical Inference for Random-Variance Option Pricing
This article deals with the estimation of continuous-time stochastic volatility models of option pricing. We argue that option prices are much more informative about the parameters than are asset prices. This is confirmed in a Monte Carlo experiment that compares two very simple strategies based on the different information sets. Both approaches are based on indirect inference and avoid any discretization bias by simulating the continuous-time mo…
No prominent works on this page.
Statistical Inference for Random-Variance Option Pricing
This article deals with the estimation of continuous-time stochastic volatility models of option pricing. We argue that option prices are much more informative about the parameters than are asset prices. This is confirmed in a Monte Carlo experiment that compares two very simple strategies based on the different information sets. Both approaches are based on indirect inference and avoid any discretization bias by simulating the continuous-time mo…
Iterative and Recursive Estimation in Structural Nonadaptive Models
An inference method, called latent backfitting, is proposed. This method appears well suited for econometric models where the structural relationships of interest define the observed endogenous variables as a known function of unobserved state variables and unknown parameters. This nonlinear state-space specification paves the way for iterative or recursive EM-like strategies. In the E steps, the state variables are forecasted given the observati…
Computer Science (2 works) · Econometrics (2 works) · Financial Risk and Volatility Modeling (2 works) · Inference (2 works) · Mathematics (2 works) · Statistics (2 works) · Stochastic processes and financial applications (2 works) · Artificial Intelligence (1 work) · Capital asset pricing model (1 work) · Capital Investment and Risk Analysis (1 work)