arXiv 8 Nov 2021 · Econometrics
arXiv:2111.04267 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces a novel Ito diffusion process to model high-frequency financial data, which can accommodate low-frequency volatility dynamics by embedding the discrete-time non-linear exponential GARCH structure with log-integrated volatility in a continuous instantaneous volatility process. The key feature of the proposed model is that, unlike existing GARCH-Ito models, the instantaneous volatility process has a non-linear structure, which ensures that the log-integrated volatilities have the realized GARCH structure. We call this the exponential realized GARCH-Ito (ERGI) model. Given the auto-regressive structure of the log-integrated volatility, we propose a quasi-likelihood estimation procedure for parameter estimation and establish its asymptotic properties. We conduct a simulation study to check the finite sample performance of the proposed model and an empirical study with 50 assets among the S&P 500 compositions. The numerical studies show the advantages of the new proposed model.
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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 | Kim, D. and Wang, Y (2016) Unified discrete-time and continuous-time models and statistical inferences for merged low-frequency and high-frequency financia… self | 1.000 | 9 | 4 | 100% |
| 2 | Song, X., Kim, D., Yuan, H., Cui, X., Lu, Z., Zhou, Y., and Wang, Y (2021) Volatility analysis with realized garch-itô models self | 1.000 | 6 | 5 | 100% |
| 3 | Hansen, P. R., Huang, Z., and Shek, H. H (2012) Realized garch: a joint model for returns and realized measures of volatility | 1.000 | 5 | 4 | 100% |
| 4 | Aẗ-Sahalia, Y. and Xiu, D (2016) Increased correlation among asset classes: Are volatility or jumps to blame, or both? | 1.000 | 5 | 3 | 100% |
| 5 | Jacod, J., Li, Y., Mykland, P. A., Podolskij, M., and Vetter, M (2009) Microstructure noise in the continuous case: the pre-averaging approach | 1.000 | 5 | 3 | 100% |
| 6 | Xiu, D (2010) Quasi-maximum likelihood estimation of volatility with high frequency data | 0.928 | 4 | 3 | 100% |
| 7 | Barndorff-Nielsen, O. E., Hansen, P. R., Lunde, A., and Shephard, N (2008) Designing realized kernels to measure the ex post variation of equity prices in the presence of noise | 0.737 | 3 | 2 | 100% |
| 8 | Corsi, F (2009) A simple approximate long-memory model of realized volatility | 0.737 | 3 | 2 | 100% |
| 9 | Zhang, L (2006) Efficient estimation of stochastic volatility using noisy observations: A multi-scale approach | 0.737 | 3 | 2 | 100% |
| 10 | Aẗ-Sahalia, Y., Fan, J., and Xiu, D (2010) High-frequency covariance estimates with noisy and asynchronous financial data | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 34 scored citations.