Bayesian semiparametric stochastic volatility modeling
By Mark J. Jensen, John M. Maheu. 2010
133 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Finance
View Paper: https://www.semanticscholar.org/paper/cde8e8599491495d0070d54b98aa53625c62223f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Mark F. J. Steel
- Francesco Ravazzolo
- Roberto Casarin
- Federico Bassetti
- Matthew Harding
- Martin Burda
- Jerry A. Hausman
- Jim E. Griffin
In Finance
- Mark F. J. Steel
- Jerry A. Hausman
- Federico Bassetti
- Roberto Casarin
- Francesco Ravazzolo
- Jim E. Griffin
- John M. Maheu
- Xin Jin
Citations in the corpus
- A Bayesian mixed logit-probit model for multinomial choice (2008, background): “Some recent applications of univariate DPMs include epidemiology (Dunson 2005), genetics (Medvedovic and Sivaganesan 2002), medicine (Kottas, Branco, and Gelfand 2002), finance (Kacperczyk, Damien, and Walker 2003), and stochastic volatility modeling (Jensen and Maheu 2007).”
- Stick-breaking autoregressive processes (2011, methodology): “Jensen and Maheu (2010) describe computational methods for the Dirichlet process-based model which can be extended using the method in Section 3.1.1.”
- Bayesian Nonparametric Modelling of the Return Distribution with Stochastic Volatility (2011, unknown): “In contrast to Jensen and Maheu (2010), the model uses the linearized parametrization of the basic SV model and so makes use of the sampling method of Kim et al. (1998) to efficiently update the volatilities in the MCMC algorithm.”
- Bayesian Nonparametric Calibration and Combination of Predictive Distributions (2015, methodology): “(2011)), is now widely used due to the availability of efficient algorithms for posterior computations (Escobar and West, 1995; MacEachern and Müller, 1998; Papaspiliopoulos and Roberts, 2008; Taddy, 2010), including but not limited to applications in time series settings (Hirano, 2002; Chib and Ham”
- Conditional (2019, unknown): “In summary, the main contributions of our work are described as follows: • We propose a Latent Diffusion-based ID Sampler based on DDIM, capable of sampling multidimensional identity embeddings with diversity and realism, which serve as anonymized identity embeddings for subsequent replacement.”