Stock market volatility forecasting: Do we need high-frequency data?

Lyócsa, Štefan; Molnár, Péter; Výrost, Tomáš

doi:10.1016/j.ijforecast.2020.12.001

Cited by 44 publications

(14 citation statements)

References 69 publications

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“…Our findings are in line with recent studies that have highlighted the importance of capturing the nuances of market behaviour through high-frequency data. For instance, recent studies [ 70 , 71 ] have demonstrated the effectiveness of high-frequency data in improving the accuracy of volatility forecasts for stock markets. Similarly, recent studies such as [ 32 , 33 , 72 ] have shown that incorporating high-frequency data can lead to more robust VaR estimates by better capturing the intraday dynamics of financial markets.…”

Section: Discussionmentioning

confidence: 99%

High-frequency enhanced VaR: A robust univariate realized volatility model for diverse portfolios and market conditions

Kuang

2024

PLoS ONE

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In the field of financial risk management, the accuracy of portfolio Value-at-Risk (VaR) forecasts is of critical importance to both practitioners and academics. This study pioneers a comprehensive evaluation of a univariate model that leverages high-frequency intraday data to improve portfolio VaR forecasts, providing a novel contrast to both univariate and multivariate models based on daily data. Existing research has used such high-frequency-based univariate models for index portfolios, it has not adequately studied their robustness for portfolios with diverse risk profiles, particularly under changing market conditions, such as during crises. Our research fills this gap by proposing a refined univariate long-memory realized volatility model that incorporates realized variance and covariance metrics, eliminating the necessity for a parametric covariance matrix. This model captures the long-run dependencies inherent in the volatility process and provides a flexible alternative that can be paired with appropriate return innovation distributions for VaR estimation. Empirical analyses show that our methodology significantly outperforms traditional univariate and multivariate Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) models in terms of forecasting accuracy while maintaining computational simplicity and ease of implementation. In particular, the inclusion of high-frequency data in univariate volatility models not only improves forecasting accuracy but also streamlines the complexity of portfolio risk assessment. This research extends the discourse between academic research and financial practice, highlighting the transformative impact of high-frequency data on risk management strategies within the financial sector.

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Section: Discussionmentioning

confidence: 99%

High-frequency enhanced VaR: A robust univariate realized volatility model for diverse portfolios and market conditions

Kuang

2024

PLoS ONE

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“…Nonetheless, high frequency data analysis does not necessarily offer better volatility estimates. Lyócsa et al (2021) respond to the assertion that high-frequency volatility models outperform low-frequency volatility models stating that such a conclusion is reached when low-frequency volatility models are estimated from daily closing returns. Hence, they study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices.…”

Section: Literature Reviewmentioning

confidence: 96%

Bitcoin Volatility Estimate Applying Rogers and Satchell Range Model

Cadenas-Morales,

Ortiz

2022

ijfirm

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Purpose:The objectives of this paper are, first, to synthesize the main events that have influenced the development and volatility of bitcoin, and second to present and apply the Rogers and Satchell range model (1993) for the measurement of bitcoin volatility. Desigh/methodology/Approach: Findings: The results are useful for risk management, searching profitable investments, diversification of portfolios, and the application of a reliable risk parameter for the valuation of bitcoin financial and real options. The evidence suggests further studies extending it, considering leptokurtosis and other moments of the distribution of the series. Practical implications: Finally, the study highlights the need for clear regulations on bitcoin and other cryptocurrencies to ensure a fruitful future co-existence with digital currencies created by the central banks. The work suggests the creation of additional IMF's SpecialDrawing Rights in lieu of a global cryptocurrency. That would help to overcome the problems created by the Covid-19 pandemic and member countries would retain their monetary sovereignty. Originality value: These objectives underline the originality and contribution of the work. This is the first time this model is fully recognized and used, highlighting its advantages.

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“…Volatility modeling and forecasting are important components in financial research and play important roles in a variety of financial applications including investment decisions and portfolio choices (Ly ocsa et al, 2021). In previous decades, many studies have developed volatility forecasting models based on various factors.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting volatility with investor pessimism index: Exploring the predictive power of search queries

Chu,

2023

Journal of Forecasting

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This paper discusses whether investor sentiment contains useful information to predict volatility by constructing investor pessimism index based on Baidu, which is the largest search engine in China. We discover that the investor pessimism index is significantly negatively correlated with contemporaneous returns but predicts following days' returns reversals. Furthermore, the heterogeneous autoregressive model of realized volatility (HAR‐RV) with investor pessimism index performs better than other HAR‐RV‐type benchmark models. We perform a series of robustness tests, which include alternative construction of investor pessimism index, crisis subsample, alternative predictive model, alternative measures of volatility, and considering the role of institutional investor. Our results remain robust. Taken together, investor pessimism index constructed in this paper seem to be a suitable proxy to measure investor sentiment in China and can enhance the accuracy of volatility forecasting in the Chinese stock market.

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Stock market volatility forecasting: Do we need high-frequency data?

Cited by 44 publications

References 69 publications

High-frequency enhanced VaR: A robust univariate realized volatility model for diverse portfolios and market conditions

High-frequency enhanced VaR: A robust univariate realized volatility model for diverse portfolios and market conditions

Bitcoin Volatility Estimate Applying Rogers and Satchell Range Model

Forecasting volatility with investor pessimism index: Exploring the predictive power of search queries

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