On filtering and estimation of a threshold stochastic volatility model

Elliott, Robert J.; Liew, Chuin Ching; Siu, Tak Kuen

doi:10.1016/j.amc.2011.05.052

Cited by 8 publications

(5 citation statements)

References 24 publications

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“…According to Giesecke et al [15], the series of parameter estimation θn(τ) τ generated by dividing n when τ → n(τ) and the parameter estimation θ∞ τ that lead to the global optimization of the likelihood function satisfy the following equation:…”

Section: Theorem 3 (Asymptotic Properties Of Parameter Estimation)mentioning

confidence: 99%

“…Thus, it is reasonable to attempt methods other than interpolation to solve the filtering equations controlling the maximum likelihood estimation. Elliott et al [15] used a stochastic EM technique to solve the filtering equations and provide asymptotic estimates of the filtering control equations. Giesecke et al [16] suggested a method for estimating the default intensity based on filtered likelihood estimation and obtained an analytical solution for parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

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Research on Financial Default Model with Stochastic Intensity Using Filtered Likelihood Method

Liu

2023

Mathematics

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This paper investigates the financial default model with stochastic intensity by incomplete data. On the strength of the process-designated point process, the likelihood function of the model in the parameter estimation can be decomposed into the factor likelihood term and event likelihood term. The event likelihood term can be successfully estimated by the filtered likelihood method, and the factor likelihood term can be calculated in a standardized manner. The empirical study reveals that, under the filtered likelihood method, the first model outperforms the other in terms of parameter estimation efficiency, convergence speed, and estimation accuracy, and has a better prediction effect on the default data in China’s financial market, which can also be extended to other countries, which is of great significance in the default risk control of financial institutions.

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Section: Theorem 3 (Asymptotic Properties Of Parameter Estimation)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Financial Default Model with Stochastic Intensity Using Filtered Likelihood Method

Liu

2023

Mathematics

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“…For more examples on the threshold stochastic volatility seeAsai and McAleer (2004,Chen et al (2008Chen et al ( , 2013,Elliott et al (2011), Ghosh et al (2015,Wu and Zhou (2015) andWirjanto et al (2016), among others.…”

mentioning

confidence: 99%

Integrated nested Laplace approximations for threshold stochastic volatility models

Bermudez

Marín

Rue

et al. 2024

Econometrics and Statistics

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The aim of the paper is to implement the integrated nested Laplace (INLA) approximations, known to be very fast and efficient, for a threshold stochastic volatility model. INLA replaces MCMC simulations with accurate deterministic approximations. We use proper although not very informative priors and Penalizing Complexity (PC) priors. The simulation results favor the use of PC priors, specially when the sample size varies from small to moderate. For these sample sizes, they provide more accurate estimates of the model's parameters, but as sample size increases both type of priors lead to reliable estimates of the parameters. We also validate the estimation method in-sample and out-of-sample by applying it to six series of returns including stock market, commodity and cryptocurrency returns and by forecasting their one-day-ahead volatilities, respectively. Our empirical results support that the TSV model does a good job in forecasting the one-day-ahead volatility of stock market and gold returns but faces difficulties when the volatility of returns is extreme, which occurs in the case of cryptocurrencies.

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“…For more examples on the negative correlation between returns and future volatility seeYu (2005), and on the threshold stochastic volatility seeAsai and McAleer (2004, 2005, 2011,Chen et al (2008Chen et al ( , 2013,Elliott et al (2011), Ghosh et al (2015,Wu and Zhou (2015) andWirjanto et al (2016), among others.…”

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confidence: 99%

Data cloning estimation for asymmetric stochastic volatility models

Bermudez

Marín

Veiga

2020

Econometric Reviews

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The paper proposes the use of data cloning (DC) to the estimation of general asymmetric stochastic volatility (ASV) models with flexible distributions for the standardized returns. These models are able to capture the asymmetric volatility, the leptokurtosis and the skewness of the distribution of returns. Data cloning is a general technique to compute maximum likelihood estimators, along with their asymptotic variances, by means of a Markov chain Monte Carlo (MCMC) methodology. The main aim of this paper is to illustrate how easily general ASV models can be estimated and consequently studied via data cloning. Changes of specifications, priors and sampling error distributions are done with minor modifications of the code. Using an intensive simulation study, the finite sample properties of the estimators of the parameters are evaluated and compared to those of a benchmark estimator that is also user-friendly.The results show that the proposed estimator is computationally efficient and robust, and can be an effective alternative to the exiting estimation methods applied to ASV models. Finally, we use data cloning to estimate the parameters of general ASV models and forecast the one-step-ahead volatility of S&P 500 and FTSE-100 daily returns.

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On filtering and estimation of a threshold stochastic volatility model

Cited by 8 publications

References 24 publications

Research on Financial Default Model with Stochastic Intensity Using Filtered Likelihood Method

Research on Financial Default Model with Stochastic Intensity Using Filtered Likelihood Method

Integrated nested Laplace approximations for threshold stochastic volatility models

Data cloning estimation for asymmetric stochastic volatility models

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