Jim E Griffin
Biographic Data
| ID | 8920402 |
|---|---|
| NAME | Jim E Griffin |
| GIVEN NAMES | Jim E |
| FAMILY NAME | Griffin |
| SIGNATURE | GRIFFIN J E |
| AFFILIATIONS | Department of Statistical Science, University College London |
| ORCID | 0000-0002-4828-7368 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Bayesian Inference of Vector Autoregressions with Tensor Decompositions
Vector autoregression (VAR) is a popular model for analyzing multivariate economic time series. However, VARs can be over-parameterized if the numbers of variables and lags are moderately large. Tensor VAR, a recent solution to over-parameterization, treats the coefficient matrix as a third-order tensor and estimates the corresponding tensor decomposition to achieve parsimony. In this paper, we employ the Tensor VAR structure with a CANDECOMP/PAR…
A Bayesian Quantile Time Series Model for Asset Returns
We consider jointly modeling a finite collection of quantiles over time. Formal Bayesian inference on quantiles is challenging since we need access to both the quantile function and the likelihood. We propose a flexible Bayesian time-varying transformation model, which allows the likelihood and the quantile function to be directly calculated. We derive conditions for stationarity, discuss suitable priors, and describe a Markov chain Monte Carlo a…
Flexible Modeling of Dependence in Volatility Processes
This article proposes a novel stochastic volatility (SV) model that draws from the existing literature on autoregressive SV models, aggregation of autoregressive processes, and Bayesian nonparametric modeling to create a SV model that can capture long-range dependence. The volatility process is assumed to be the aggregate of autoregressive processes, where the distribution of the autoregressive coefficients is modeled using a flexible Bayesian ap…
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Flexible Modeling of Dependence in Volatility Processes
This article proposes a novel stochastic volatility (SV) model that draws from the existing literature on autoregressive SV models, aggregation of autoregressive processes, and Bayesian nonparametric modeling to create a SV model that can capture long-range dependence. The volatility process is assumed to be the aggregate of autoregressive processes, where the distribution of the autoregressive coefficients is modeled using a flexible Bayesian ap…
A Bayesian Quantile Time Series Model for Asset Returns
We consider jointly modeling a finite collection of quantiles over time. Formal Bayesian inference on quantiles is challenging since we need access to both the quantile function and the likelihood. We propose a flexible Bayesian time-varying transformation model, which allows the likelihood and the quantile function to be directly calculated. We derive conditions for stationarity, discuss suitable priors, and describe a Markov chain Monte Carlo a…
Bayesian Inference of Vector Autoregressions with Tensor Decompositions
Vector autoregression (VAR) is a popular model for analyzing multivariate economic time series. However, VARs can be over-parameterized if the numbers of variables and lags are moderately large. Tensor VAR, a recent solution to over-parameterization, treats the coefficient matrix as a third-order tensor and estimates the corresponding tensor decomposition to achieve parsimony. In this paper, we employ the Tensor VAR structure with a CANDECOMP/PAR…
Artificial Intelligence (3 works) · Bayesian probability (3 works) · Computer Science (3 works) · Econometrics (3 works) · Mathematics (3 works) · Bayesian inference (2 works) · Financial Risk and Volatility Modeling (2 works) · Inference (2 works) · Markov chain Monte Carlo (2 works) · Statistical Methods and Inference (2 works)