Dan Li, Adam Clements, Christopher Drovandi
arXiv 10 Jun 2019 · Statistics — Applications · publishedEconometrics and Statistics (2020) · 12 citations (OpenAlex)
arXiv:1906.03828 · PDF · DOI · OpenAlex · Extracted main text
The advantages of sequential Monte Carlo (SMC) are exploited to develop parameter estimation and model selection methods for GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) style models. It provides an alternative method for quantifying estimation uncertainty relative to classical inference. Even with long time series, it is demonstrated that the posterior distribution of model parameters are non-normal, highlighting the need for a Bayesian approach and an efficient posterior sampling method. Efficient approaches for both constructing the sequence of distributions in SMC, and leave-one-out cross-validation, for long time series data are also proposed. Finally, an unbiased estimator of the likelihood is developed for the Bad Environment-Good Environment model, a complex GARCH-type model, which permits exact Bayesian inference not previously available in the literature.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bekaert, G., Engstrom, E., and Ermolov, A (2015) Bad environments, good environments: A non-Gaussian asymmetric volatility model | 1.000 | 9 | 4 | 100% |
| 2 | Chopin, N (2002) A sequential particle filter method for static models | 0.874 | 6 | 2 | 100% |
| 3 | Bürkner, P.-C., Gabry, J., and Vehtari, A (2019) Approximate leave-future-out cross-validation for time series models | 0.874 | 5 | 2 | 100% |
| 4 | Bollerslev, T (1986) Generalized autoregressive conditional heteroskedasticity | 0.811 | 4 | 2 | 100% |
| 5 | Andrieu, C. and Roberts, G. O (2009) The pseudo-marginal approach for efficient Monte Carlo computations | 0.644 | 2 | 2 | 100% |
| 6 | Black, F (1976) Studies of stock market volatility changes | 0.644 | 2 | 2 | 100% |
| 7 | Chopin, N., Jacob, P. E., and Papaspiliopoulos, O (2013) SMC2: an efficient algorithm for sequential analysis of state space models | 0.644 | 2 | 2 | 100% |
| 8 | Del Moral, P., Doucet, A., and Jasra, A (2006) Sequential Monte Carlo samplers | 0.644 | 2 | 2 | 100% |
| 9 | Duan, J.-C. and Fulop, A (2015) Density-tempered marginalized sequential Monte Carlo samplers | 0.644 | 2 | 2 | 100% |
| 10 | Engle, R. F (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 47 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Recurrent Conditional Heteroskedasticity | 0.405 | 1 | 1 |
| 2 | Deep Learning Enhanced Realized GARCH | 0.405 | 1 | 1 |
| 3 | A Bayesian Ensemble Projection of Climate Change and Technological Impacts on Future Crop Yields | 0.000 | 1 | 1 |