Markov chain Monte Carlo methods for stochastic volatility models

By Siddhartha Chib, Federico Nardari, N. Shephard. 2002

572 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/d86c013b105bcb0c4741c9b20806922e3f85d6c3

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Citations in the corpus

Estimating the Integrated Likelihood via Posterior Simulation Using the Harmonic Mean Identity (2006, methodology): “These methods have been successfully applied to specific models, for example by Albert and Chib (2001), Chib, Nardard, and Shephard (2002), and Basu and Chib (2003).”
On Leverage in a Stochastic Volatility Model (2005, unknown): “The marginal likelihood values from the encompassed model and ASV2 differs by 3.86, which suggests substantial evidence in favor of the encompassed specification against ASV2 according to Jeffrey’s Bayes factor scale (see Chib et al (2002) Section 2.3).”
MCMC ESTIMATION OF LÉVY JUMP MODELS USING STOCK AND OPTION PRICES (2011, background): “…Yt and vt+1 − vt follow a bivariate normal distribution 12 Earlier studies, such as Jacquier, Polson, and Rossi (1994), Kim, Shephard, and Chib (1998), Chib, Nardari, and Shephard (2002), and Maheu and McCurdy (2004) among others, apply MCMC methods to estimate discrete-time stochastic volatility…”
Monte Carlo methods for estimating, smoothing, and filtering one- and two-factor stochastic volatility models (2006, methodology): “SV-t models have also been estimated using likelihood-based techniques by Chib et al. (2002), Sandmann and Koopman (1998) and Liesenfeld and Richard (2003), but only with uncorrelated errors (note that (6) and (7) are equivalent if the errors are not correlated).”
Objective Bayesian analysis for the Student-t regression model (2008, background): “INTRODUCTION Many datasets exhibit heavy tail behaviour that can be well modelled with Student-t errors (Fernmndez & Steel, 1998; Lange et al., 1989; Vrontos et al., 2000; Chib et al., 2002; Jacquier et al., 2004).”
No-Arbitrage Semi-Martingale Restrictions for Continuous-Time Volatility Models subject to Leverage Effects, Jumps and i.i.d. Noise: Theory and Testable Distributional Implications (2007, unknown): “It is well known that ARCH models provide consistent volatility filters—as the underlying data is sampled at ever finer frequencies—for extracting the conditional variance process from return series driven by a pure diffusion; see, e.g., Nelson (1990, 1992).”
High‐dimensional posterior exploration of hydrologic models using multiple‐try DREAM(ZS) and high‐performance computing (2012, methodology): “In principle, the IF is similar to the inverse of the numerical efficiency measure of Geweke [1992] and can be computed from the last samples in the Markov chains [Chib et al., 2002].”
Nonparametric Filtering of the Realised Spot Volatility: A Kernel-Based Approach (2007, unknown): “…process is a latent variable, not observed by the econometrician, previous work on this have based the estimation of volatility models on the raw return data itself (Andersen and Lund, 1997; Chib et al, 2002) or the realised integrated volatility (Andersen et al, 2003; Bollerslev and Zhou, 2002).”
Common Drifting Volatility in Large Bayesian VARs (2012, unknown): “For example, in an asset return context, Chib, Nardari, and Shephard (2002, 2006) and Jacquier, Polson, and Rossi (1995) employed a factor structure multivariate stochastic volatility model.”
Alternative Asymmetric Stochastic Volatility Models (2009, unknown): “integration sampler of Chib, Nardari and Shephard (2002) .”