---
title: Effectiveness of Short-Term Market Swings in Predicting Realized Volatility
url: https://www.ml-quant.com/papers/repec/eee-finlet-v-58-y-2023-i-pd-s1544612323010012/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:eee:finlet:v:58:y:2023:i:pd:s1544612323010012
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1544612323010012%3Bh%3Drepec%3Aeee%3Afinlet%3Av%3A58%3Ay%3A2023%3Ai%3Apd%3As1544612323010012
featured: 2023-12-20
citations: unknown
topic: Derivatives & Volatility
---


# Effectiveness of Short-Term Market Swings in Predicting Realized Volatility

The article assesses the new VIX1D volatility index's effectiveness in predicting short-term market fluctuations and realized volatility.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1544612323010012%3Bh%3Drepec%3Aeee%3Afinlet%3Av%3A58%3Ay%3A2023%3Ai%3Apd%3As1544612323010012
- Identifier: RePEc:eee:finlet:v:58:y:2023:i:pd:s1544612323010012
- Released: 2023-12-20
- First featured: Quant Letter No. 30 (2023-12-20): https://www.ml-quant.com/issues/2023-12-20/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [TimesNet for Realized Volatility Prediction](https://www.ml-quant.com/papers/ssrn/4660025/): The study shows that the TimesNet model is effective in predicting stock volatility, particularly during extreme market movements, making it a strong neural network benchmark in volatility research.
- [Harnessing Volatility Cascades with Ensemble Learning](https://www.ml-quant.com/papers/ssrn/4682793/): A modification to the base learner in bootstrap aggregation and boosting can significantly improve predictive accuracy in volatility forecasting, addressing substantial errors from parameter estimation.
- [SpotV2Net: Multivariate Intraday Spot Volatility Forecasting via Vol-of-Vol-Informed Graph Attention Networks](https://www.ml-quant.com/papers/arxiv/2401.06249/): Intraday Volatility Forecasting: The article introduces SpotV2Net, a new model for predicting intraday spot volatility using a Graph Attention Network, which has shown better accuracy in predicting Dow Jones Industrial Average index prices.
- [TSMixer and Realized Volatility Prediction](https://www.ml-quant.com/papers/ssrn/4713756/): Stock Volatility Forecasting with Neural Networks: The TSMixer neural network model has proven to be more effective than traditional models in predicting stock market volatility, indicating a possible shift towards simpler models in the future.
- [Stock Volatility Prediction Based on Transformer Model Using Mixed-Frequency Data](https://www.ml-quant.com/papers/arxiv/2309.16196/): A new model combining macroeconomic indicators, stock technical indicators, and Baidu search indices significantly improves stock volatility prediction, reducing error from 1.00 to 0.86.
- [Combining Deep Learning and GARCH Models for Financial Volatility and Risk Forecasting](https://www.ml-quant.com/papers/arxiv/2310.01063/): The research introduces a hybrid method for predicting the volatility and risk of financial tools by merging GARCH time series models with deep learning neural networks, finding that while this approach improves volatility predictions, it doesn't necessarily enhance Value-at-Risk and Expected Shortfall forecasts.
