Monte Carlo methods for estimating, smoothing, and filtering one- and two-factor stochastic volatility models

By Garland B. Durham. 2006

96 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/502dc9fb5baeb62bbe399430c7a35eaa6d45c8fa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Michael Mcaleer
2. Manabu Asai
3. Tim Bollerslev
4. George Tauchen
5. S. J. Koopman
6. Jun Yu
7. N. Shephard
8. V. Todorov

In Finance

1. Michael Mcaleer
2. S. J. Koopman
3. Tim Bollerslev
4. Manabu Asai
5. George Tauchen
6. V. Todorov
7. N. Shephard
8. Jun Yu

Citations in the corpus

• Testing the assumptions behind importance sampling (2009, methodology): “…Danielsson (1994), Shephard and Pitt (1997), Durbin and Koopman (1997), Sandmann and Koopman (1998), Durham and Gallant (2002), Durham (2003), Liesenfeld and Richard (2003), Brandt and Kang (2004), Durham (2006), Asai and McAleer (2006), Richard and Zhang (2007), and Jungbacker and Koopman (2007).”
• Bayesian semiparametric stochastic volatility modeling (2010, unknown): “However, parametric SVmodels have not fully captured the asymmetries and leptokurtotic behavior present in return data (see Gallant et al., 1997; Mahieu and Schotman, 1998; Liesenfeld and Jung, 2000; Meddahi, 2001; Durham, 2006).”
• SV mixture models with application to S&P 500 index returns ☆ (2007, unknown): “The particle filter described above is somewhat different from the one used in Durham (2006).”
• Leverage and Volatility Feedback Effects in High-Frequency Data (2006, background): “See Durham and Gallant (2002) for further discussion along these lines.”
• Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison (2006, methodology): “Unfortunately, direct independent sampling from a high-dimensional distribution such as in (3) is usually not possible (see Liesenfeld and Richard (2003) and Durham (2004) for counterexamples, however). MCMC techniques overcome this problem by constructing a Markov chain with stationary distribution”
• A Semiparametric Stochastic Volatility Model (2012, unknown): “To do so we follow the suggestion made by Durham (2006) by fitting the same model to the same dataset repeatedly with different random seeds.”
• Bayesian Nonparametric Modelling of the Return Distribution with Stochastic Volatility (2011, unknown): “Models with nonparametric return distributions can capture features of the return data which the parametric equivalents cannot fully capture (Gallant et al. 1997; Mahieu and Schotman 1998; Durham 2006).”
• Multivariate Stochastic Volatility: A Review (2006, background): “To achieve this goal, Shephard and Pitt ( 1997 ), Durbin and Koopman ( 1997 ), and Durham ( 2005 ) proposed basing the importance sampler on the Laplace approximation to f ( Y ,Λ;).”