Univariate and Multivariate Stochastic Volatility Models: Estimation and Diagnostics

By Roman Liesenfeld, Jean-François Richard. 2003

288 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/c5f40795e598d198c34eb9ece04e54f22407018e

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. Manabu Asai
2. Michael Mcaleer
3. Esther Ruiz
4. S. J. Koopman
5. Maria Pacurar
6. Yasuhiro Omori
7. Carmen Broto
8. Siddhartha Chib

In Finance

1. Manabu Asai
2. Michael Mcaleer
3. S. J. Koopman
4. Yasuhiro Omori
5. Maria Pacurar
6. Esther Ruiz
7. Siddhartha Chib
8. Carmen Broto

Citations in the corpus

• Stochastic Conditional Intensity Processes (2006, methodology): “Liesenfeld and Richard (2003) recommended to iterate steps 1–3 about five times to improve the efficiency of the approximations.”
• Multivariate stochastic volatility (2009, methodology): “While the AGIS technique has limited applicability, the Efficient Importance Sampling (EIS) procedure proposed by Richard and Zhang (2004), and applied by Liesenfeld and Richard (2003, 2004), is applicable to models with more flexible classes of distributions for the latent variables.”
• The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures (2011, methodology): “…special case of our general model given by the equations (1), (2) and (3) with µt = 0, f(θ) = exp(θ), θi,t = αi,t, R = I and k = m = r. Shephard (1996), Barndorff-Nielsen and Shephard (2002), Liesenfeld and Richard (2003), and other studies estimate multiple component stochastic volatility models.”
• Stochastic volatility forecasting and risk management (2005, methodology): “, 1998) and efficient importance sampling (EIS) (Richard and Zhang, 1996; Liesenfeld and Richard, 2003).”
• Tails of correlation mixtures of elliptical copulas (2009, methodology): “Estimation of the model is done by simulated maximum likelihood using the efficient importance sampler [25].”
• Energy markets and CO2 emissions: Analysis by stochastic copula autoregressive model (2015, unknown): “The expression of k proposed by Liesenfeld and Richard (2003)[21] is: k(Λt; at) = p(λ|λt−1, ω)ξ(λt, at) (19) with ξ(λt, at) = exp(a1,tλt + a2,tλ 2 t ) (20) 10 to simplify the least square system above and make it linear.”
• Testing the assumptions behind importance sampling (2009, background): “Papers that carry out maximum likelihood analysis of SV models using importance sampling include 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), Durha”
• Alternative Asymmetric Stochastic Volatility Models (2009, unknown): “The EIS is much easier to implement computationally, and is flexible for the models discussed in this paper (see Liesenfeld and Richard (2003) for a detailed explanation of evaluating SV models via the EIS method).”
• Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models (2006, methodology): “For the ML-EIS approach, this flexibility has already been illustrated by Liesenfeld and Richard (2003).”
• Multivariate Stochastic Volatility: A Review (2006, methodology): “While the AGIS technique has limited applicability, a closely related method, namely the efficient importance sampling (EIS) procedure, as proposed by Liesenfeld and Richard ( 2003 2006 ), is applicable to models with more flexible classes of distributions for the latent variables.”
• Efficient high-dimensional importance sampling (2007, methodology): “Comparisons with alternative numerical evaluations of SV models are found in Liesenfeld and Richard (2003c) and Bauwens and Hautsch (2006).”
• Estimation methods for stochastic volatility models: a survey (2004, methodology): “With respect to the solution to the smoothing problem, Liesenfeld and Richard (2003) propose a procedure to estimate the underlying volatilities.”
• Autoregressive Stochastic Volatility Models with Heavy-tailed Distributions: A Comparison with Multifactor Volatility Models (2008, methodology): “Furthermore, the MFSV model is reduced to the two-component model of Liesenfeld and Richard (2003) when K = 2 and ψ1 = ψ2 = 0.”
• The Multi-State Latent Factor Intensity Model for Credit Rating Transitions (2008, unknown): “Bauwens and Hautsch (2003) adopt the simulated maximum likelihood method of Liesenfeld and Richard (2003) for the estimation of a single-state LFI model.”