arXiv 19 Jul 2019 · Econometrics
arXiv:1907.08522 · PDF · DOI · OpenAlex · Extracted main text
The heterogeneous autoregressive (HAR) model is revised by modeling the joint distribution of the four partial-volatility terms therein involved. Namely, today's, yesterday's, last week's and last month's volatility components. The joint distribution relies on a (C-) Vine copula construction, allowing to conveniently extract volatility forecasts based on the conditional expectation of today's volatility given its past terms. The proposed empirical application involves more than seven years of high-frequency transaction prices for ten stocks and evaluates the in-sample, out-of-sample and one-step-ahead forecast performance of our model for daily realized-kernel measures. The model proposed in this paper is shown to outperform the HAR counterpart under different models for marginal distributions, copula construction methods, and forecasting settings.
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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 | Eric Hillebrand and Marcelo C Medeiros (2010) The benefits of bagging for forecast models of realized volatility | 1.000 | 7 | 3 | 100% |
| 2 | Oleg Sokolinskiy and Dick van Dijk (2011) Forecasting volatility with copula-based time series models | 1.000 | 6 | 4 | 100% |
| 3 | Fulvio Corsi (2009) A simple approximate long-memory model of realized volatility | 1.000 | 6 | 3 | 100% |
| 4 | Josip Arnerić, Tea Poklepović, and Juin Wen Teai (2018) Neural network approach in forecasting realized variance using high-frequency data | 0.928 | 4 | 3 | 100% |
| 5 | Ole E Barndorff-Nielsen, P Reinhard Hansen, Asger Lunde, and Neil Sh… (2009) Realized kernels in practice: Trades and quotes | 0.874 | 6 | 2 | 100% |
| 6 | Tim Bollerslev, Andrew J Patton, and Rogier Quaedvlieg (2016) Exploiting the errors: A simple approach for improved volatility forecasting | 0.843 | 3 | 3 | 100% |
| 7 | Roger B Nelsen (2007) An introduction to copulas | 0.843 | 3 | 3 | 100% |
| 8 | Andrew J Patton and Kevin Sheppard (2015) Good volatility, bad volatility: Signed jumps and the persistence of volatility | 0.843 | 3 | 3 | 100% |
| 9 | Harry Joe and Dorota Kurowicka (2011) Dependence modeling: vine copula handbook | 0.811 | 4 | 2 | 100% |
| 10 | Ole E Barndorff-Nielsen, Peter Reinhard Hansen, Asger Lunde, and Nei… (2008) Designing realized kernels to measure the ex post variation of equity prices in the presence of noise | 0.693 | 5 | 1 | 100% |
Showing the top 10 of 70 scored citations.