Semiparametric Bayesian Inference of Long‐Memory Stochastic Volatility Models

By Mark J. Jensen. 2004

57 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/f904445f30788ef63148d7f98d7d0720a944752e

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Citations in the corpus

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.”
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.”
Order-Based Dependent Dirichlet Processes (2006, methodology): “Jensen (2004) used a Dirichlet process prior on the wavelet representation of the observables to conduct Bayesian inference in a stochastic volatility model with long memory.”
Factor stochastic volatility with time varying loadings and Markov switching regimes (2007, unknown): “Jensen (2004)29 develops semiparametric inference for long-memory SV processes, while So et al. (1998) and Carvalho and Lopes (2004) accommodate Markov jumps in the log-volatilities.31 ∗ Corresponding author.”
A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries (2008, background): “Breidt, Crato, and de Lima (1998), Hurvich and Ray (2003), Jensen (2004), and Deo, Hurvich, and Lu (2006) discussed the specification and estimation of SV models with long memory.”
Bayesian semiparametric stochastic volatility modeling (2010, methodology): “Jensen (2004) uses a DPM tomodel the distribution of additive noise of log-squared returns while in this paper we are concerned with the conditional distribution of returns.”