Eric Jacquier
Datos Biográficos
| ID | 8920271 |
|---|---|
| NOMBRE | Eric Jacquier |
| NOMBRES | Eric |
| APELLIDO | Jacquier |
| FIRMA | JACQUIER E |
| AFILIACIONES | Cornell University |
| VERIFICADO | No |
| TOTAL DE OBRAS | 3 |
| TOTAL DE CITAS | 0 |
| TOTAL COMO AUTOR | 3 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 1994 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2002 |
| ÍNDICE H | 0 |
Bayesian Analysis of Stochastic Volatility Models
New techniques for the analysis of stochastic volatility models in which the logarithm of conditional variance follows an autoregressive model are developed. A cyclic Metropolis algorithm is used to construct a Markov-chain simulation tool. Simulations from this Markov chain coverage in distribution to draws from the posterior distribution enabling exact finite-sample inference. The exact solution to the filtering/smoothing problem of inferring a…
Bayesian Analysis of Stochastic Volatility Models
New techniques for the analysis of stochastic volatility models in which the logarithm of conditional variance follows an autoregressive model are developed. A cyclic Metropolis algorithm is used to construct a Markov-chain simulation tool. Simulations from this Markov chain converge in distribution to draws from the posterior distribution enabling exact finite-sample inference. The exact solution to the filtering/smoothing problem of inferring a…
[Bayesian Analysis of Stochastic Volatility Models]
Sin obras prominentes en esta página.
Bayesian Analysis of Stochastic Volatility Models
New techniques for the analysis of stochastic volatility models in which the logarithm of conditional variance follows an autoregressive model are developed. A cyclic Metropolis algorithm is used to construct a Markov-chain simulation tool. Simulations from this Markov chain converge in distribution to draws from the posterior distribution enabling exact finite-sample inference. The exact solution to the filtering/smoothing problem of inferring a…
[Bayesian Analysis of Stochastic Volatility Models]
Bayesian Analysis of Stochastic Volatility Models
New techniques for the analysis of stochastic volatility models in which the logarithm of conditional variance follows an autoregressive model are developed. A cyclic Metropolis algorithm is used to construct a Markov-chain simulation tool. Simulations from this Markov chain coverage in distribution to draws from the posterior distribution enabling exact finite-sample inference. The exact solution to the filtering/smoothing problem of inferring a…
Bayesian probability (3 obras) · Econometrics (3 obras) · Financial Risk and Volatility Modeling (3 obras) · Mathematics (3 obras) · Statistics (3 obras) · Stochastic processes and financial applications (3 obras) · Stochastic volatility (3 obras) · Autoregressive model (2 obras) · Bayes factor (2 obras) · Bayes' theorem (2 obras)