Iuri H. Ferreira, Marcelo C. Medeiros
arXiv 30 Dec 2021 · Econometrics · 2 citations (OpenAlex)
arXiv:2112.15108 · PDF · DOI · OpenAlex · Extracted main text
In this paper we examine the relation between market returns and volatility measures through machine learning methods in a high-frequency environment. We implement a minute-by-minute rolling window intraday estimation method using two nonlinear models: Long-Short-Term Memory (LSTM) neural networks and Random Forests (RF). Our estimations show that the CBOE Volatility Index (VIX) is the strongest candidate predictor for intraday market returns in our analysis, specially when implemented through the LSTM model. This model also improves significantly the performance of the lagged market return as predictive variable. Finally, intraday RF estimation outputs indicate that there is no performance improvement with this method, and it may even worsen the results in some cases.
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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 | Breiman, L (2001) Random forests | 0.737 | 3 | 2 | 100% |
| 2 | Bollerslev, T., G. Tauchen, and H. Zhou (2009) Expected Stock Returns and Variance Risk Premia | 0.644 | 2 | 2 | 100% |
| 3 | Chinco, A., A. D. Clark‐Joseph, and M. Ye (2019) Sparse Signals in the Cross‐Section of Returns | 0.644 | 2 | 2 | 100% |
| 4 | Masini, R. P., M. C. Medeiros, and E. F. Mendes (2021) Machine learning advances for time series forecasting self | 0.644 | 2 | 2 | 100% |
| 5 | Hochreiter, S. and J. Schmidhuber (1997) Long short-term memory | 0.644 | 2 | 2 | 100% |
| 6 | Campbell, J. Y. and S. B. Thompson (2008) Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average? | 0.405 | 1 | 1 | 100% |
| 7 | Corsi, F (2009) A simple approximate long-memory model of realized volatility | 0.405 | 1 | 1 | 100% |
| 8 | Fernandes, M., M. Medeiros, and M. Scharth (2014) Modeling and predicting the CBOE market volatility index | 0.405 | 1 | 1 | 100% |
| 9 | Bekaert, G. and M. Hoerova (2014) The VIX, the variance premium and stock market volatility | 0.405 | 1 | 1 | 100% |
| 10 | Martin, I (2017) What is the expected return on the market? | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 16 scored citations.