Drew Creal
Dados Biográficos
| ID | 8879209 |
|---|---|
| NOME | Drew Creal |
| PRENOMES | Drew |
| SOBRENOME | Creal |
| ASSINATURA | CREAL D |
| AFILIAÇÕES | University of Chicago |
| VERIFICADO | Não |
| TOTAL DE OBRAS | 4 |
| TOTAL DE CITAÇÕES | 0 |
| OBRAS COMO AUTOR | 4 |
| OBRAS COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2011 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2017 |
| ÍNDICE H | 0 |
A Class of Non-Gaussian State Space Models With Exact Likelihood Inference
The likelihood function of a general nonlinear, non-Gaussian state space model is a high-dimensional integral with no closed-form solution. In this article, I show how to calculate the likelihood function exactly for a large class of non-Gaussian state space models that include stochastic intensity, stochastic volatility, and stochastic duration models among others. The state variables in this class follow a nonnegative stochastic process that is…
Market-Based Credit Ratings
We present a methodology for rating in real-time the creditworthiness of public companies in the U.S. from the prices of traded assets. Our approach uses asset pricing data to impute a term structure of risk neutral survival functions or default probabilities. Firms are then clustered into ratings categories based on their survival functions using a functional clustering algorithm. This allows all public firms whose assets are traded to be direct…
Observation-Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk
We propose an observation-driven dynamic factor model for mixed-measurement and mixed-frequency panel data. Time series observations may come from a range of families of distributions, be observed at different frequencies, have missing observations, and exhibit common dynamics and cross-sectional dependence due to shared dynamic latent factors. A feature of our model is that the likelihood function is known in closed form. This enables parameter …
A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations
We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts generalized autoregressive score dynamics to obtain a time-varying covariance matrix of the multivariate Student t distribution. The key novelty of our proposed model concerns the weighting of lagged squared innovations for the estimation of future correlations…
Sem obras proeminentes nesta página.
A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations
We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts generalized autoregressive score dynamics to obtain a time-varying covariance matrix of the multivariate Student t distribution. The key novelty of our proposed model concerns the weighting of lagged squared innovations for the estimation of future correlations…
Market-Based Credit Ratings
We present a methodology for rating in real-time the creditworthiness of public companies in the U.S. from the prices of traded assets. Our approach uses asset pricing data to impute a term structure of risk neutral survival functions or default probabilities. Firms are then clustered into ratings categories based on their survival functions using a functional clustering algorithm. This allows all public firms whose assets are traded to be direct…
Observation-Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk
We propose an observation-driven dynamic factor model for mixed-measurement and mixed-frequency panel data. Time series observations may come from a range of families of distributions, be observed at different frequencies, have missing observations, and exhibit common dynamics and cross-sectional dependence due to shared dynamic latent factors. A feature of our model is that the likelihood function is known in closed form. This enables parameter …
A Class of Non-Gaussian State Space Models With Exact Likelihood Inference
The likelihood function of a general nonlinear, non-Gaussian state space model is a high-dimensional integral with no closed-form solution. In this article, I show how to calculate the likelihood function exactly for a large class of non-Gaussian state space models that include stochastic intensity, stochastic volatility, and stochastic duration models among others. The state variables in this class follow a nonnegative stochastic process that is…
Econometrics (4 obras) · Mathematics (4 obras) · Statistics (4 obras) · Computer Science (3 obras) · Financial Risk and Volatility Modeling (3 obras) · Actuarial science (2 obras) · Banking stability, regulation, efficiency (2 obras) · Credit risk (2 obras) · Credit Risk and Financial Regulations (2 obras) · Economics (2 obras)