# Reducing overestimating and underestimating volatility via the augmented blending-ARCH model

arXiv: https://arxiv.org/abs/2203.12456 · HTML: https://mlsanity.com/paper/2203.12456/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 59 references that are in the map.
- First posted on arXiv 15 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Matrix Decomposition and Applications](https://mlsanity.com/paper/2201.00145/) (2022)
- [A rigorous introduction to linear models](https://mlsanity.com/paper/2105.04240/) (2021)
- [Exploring Classic Quantitative Strategies](https://mlsanity.com/paper/2202.11309/) (2022)
- [Machine learning modeling for time series problem: Predicting flight ticket prices](https://mlsanity.com/paper/1705.07205/) (2017)
- [Hyperprior on symmetric Dirichlet distribution](https://mlsanity.com/paper/1708.08177/) (2017)

## Built on by (most cited first, 5 of 5)

- [Gradient Descent, Stochastic Optimization, and Other Tales](https://mlsanity.com/paper/2205.00832/) (2022)
- [Autoencoding Conditional GAN for Portfolio Allocation Diversification](https://mlsanity.com/paper/2207.05701/) (2022)
- [AdaSmooth: An Adaptive Learning Rate Method based on Effective Ratio](https://mlsanity.com/paper/2204.00825/) (2022)
- [A note on VIX for postprocessing quantitative strategies](https://mlsanity.com/paper/2207.04887/) (2022)
- [A Hybrid Approach on Conditional GAN for Portfolio Analysis](https://mlsanity.com/paper/2208.07159/) (2022)

## Abstract (arXiv)

SVR-GARCH model tends to "backward eavesdrop" when forecasting the financial time series volatility in which case it tends to simply produce the prediction by deviating the previous volatility. Though the SVR-GARCH model has achieved good performance in terms of various performance measurements, trading opportunities, peak or trough behaviors in the time series are all hampered by underestimating or overestimating the volatility. We propose a blending ARCH (BARCH) and an augmented BARCH (aBARCH) model to overcome this kind of problem and make the prediction towards better peak or trough behaviors. The method is illustrated using real data sets including SH300 and S&P500. The empirical results obtained suggest that the augmented and blending models improve the volatility forecasting ability.