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The purpose of this paper is to examine whether the superiority of the implied volatility from a stochastic volatility model over the implied volatility from the Black and Scholes model on the forecasting performance of future realized volatility still holds when intraday data are analyzed.

Two implied volatilities and a realized volatility on KOSPI200 index options are estimated every hour. The grander causality tests between an implied volatility and a realized volatility is carried out for checking the forecasting performance. A dummy variable is added to the grander causality test to examine the change of the forecasting performance when a specific environment is chosen. A trading simulation is conducted to check the economic value of the forecasting performance.

Contrary to the previous studies, the implied volatility from a stochastic volatility model is not superior to that from the Black and Scholes model for the intraday volatility forecasting even if both implied volatilities are informative on one hour ahead future volatility. The forecasting performances of both implied volatilities are improved under high volatile market or low return market.

The trading strategy using the forecasting power of an implied volatility earns positively, in particular, more positively under high volatile market or low return market. However, it looks risky to follow the trading strategy because the performance is too volatile. Between two implied volatilities, it is hardly to say that one implied volatility beats another in terms of the economic value.

This is the first study which shows the forecasting performances of implied volatilities on the intraday future volatility.

Using intraday data, the authors explore the forecast ability of one high frequency order flow imbalance measure (OI) based on the volume-synchronized probability of informed trading metric (VPIN) for predicting the realized volatility of the index futures on the China Securities Index 300 (CSI 300).

The authors employ the heterogeneous autoregressive model for realized volatility (HAR-RV) and compare the forecast ability of models with and without the predictive variable, OI.

The empirical results demonstrate that the augmented HAR model incorporating OI (HARX-RV) can generate more precise forecasts, which implies that the order imbalance measure contains substantial information for describing the volatility dynamics.

The study sheds light on the relation between high frequency trading behavior and volatility forecasting in China's index futures market and reveals the underlying market mechanisms of liquidity-induced volatility.

In this paper weekly volatility forecasts are considered with applications to risk management; in particular hedge ratios and VaR calculations, with the aim of identifying the most appropriate model for risk management practice.

The study considers a variety of models, including those typically employed within the risk management industry, such as averaging and smoothing techniques, as well as those favored in academic circles, such as the GARCH genre of models, and a more recent realized volatility approach which incorporates both the simplicity in construction favored by the finance industry and the flexibility and theoretical underpinnings recommended by academics.

The results support the view that this realized volatility measure provides not only superior volatility forecasts per se, but also allows for improved hedge ratio and VaR calculations.

The research findings carry practical implications for the conduct of risk management, namely that volatility forecasts are best obtained using the realized volatility approach.

It is therefore proposed that a future direction for risk management practice may be to utilize such measures, while more generally it is hoped that such approaches may improve the cross‐fertilization of ideas and practice between the academic and practitioner communities.

The purpose of this paper is to assess to what extent intraday data can explain and predict long-term memory.

This article analysed the presence of long-memory volatility in five Asian equity indices, namely, SENSEX, CNIA, NIKKEI225, KO11 and FTSTI, using five-min intraday return series from 05 January 2015 to 06 August 2015 using two approaches, i.e. conditional volatility and realized volatility, for forecasting long-term memory. It employs conditional-generalized autoregressive conditional heteroscedasticity (GARCH), i.e. autoregressive fractionally integrated moving average (ARFIMA)-FIGARCH model and ARFIMA-asymmetric power autoregressive conditional heteroscedasticity (APARCH) models, and unconditional volatility realized volatility using autoregressive integrated moving average (ARIMA) and ARFIMA in-sample forecasting models to estimate the persistence of the long-term memory.

Given the GARCH framework, the ARFIMA-APARCH long-memory model gave the better forecast results signifying the importance of accounting for asymmetric information when modelling volatility in a financial market. Using the unconditional realized volatility results from the Singapore and Indian markets, the ARIMA model outperforms the ARFIMA model in terms of forecast performance and provides reasonable forecasts.

The issue of long memory has important implications for the theory and practice of finance. It is well-known that accurate volatility forecasts are important in a variety of settings including option and other derivatives pricing, portfolio and risk management.

It could be said that using long-memory augmented models would give better results to investors so that they could analyse the market trends in returns and volatility in a more accurate manner and reach at an informed decision. This is useful to minimize the risks.

This research enhances the literature by estimating the influence of intraday variables on daily volatility. This is one of very few studies that uses conditional GARCH framework models and unconditional realized volatility estimates for forecasting long-term memory. The authors find that the methods complement each other.

This study aims to show that the best-performing realized measures vary across markets when it comes to forecast real estate investment trust (REIT) volatility. This finding provides little guidance for practitioners on which one to use when facing a new market. The authors attempt to fill the hole by seeking a common estimator, which can study for different markets.

The authors do so by drawing upon the general forecasting literature, which finds that combinations of individual forecasts often outperform even the best individual forecast. The authors carry out the study by first introducing a number of commonly used realized measures and then considering several different combination strategies. The authors apply all of the individual measures and their different combinations to three major global REIT markets (Australia, UK and US).

The findings show that both unconstrained and constrained versions of the regression-based combinations consistently rank among the group of best forecasters across the three markets under study. None of their peers can do it including the three simple combinations and all of the individual measures. The conclusions are robust to the choice of evaluation metrics and of the out-of-sample evaluation periods.

The study provides practitioners with easy-to-follow insights on how to forecast REIT volatility, that is, use a regression-based combination of individual realized measures. The study has also extended the thin real estate literature on using high-frequency data to examine REIT volatility.

This study aims to critically evaluate the competitiveness of Transformer-based models in financial forecasting, specifically in the context of stock realized volatility forecasting. It seeks to challenge and extend upon the assertions of Zeng et al. (2023) regarding the purported limitations of these models in handling temporal information in financial time series.

Employing a robust methodological framework, the study systematically compares a range of Transformer models, including first-generation and advanced iterations like Informer, Autoformer, and PatchTST, against benchmark models (HAR, NBEATSx, NHITS, and TimesNet). The evaluation encompasses 80 different stocks, four error metrics, four statistical tests, and three robustness tests designed to reflect diverse market conditions and data availability scenarios.

The research uncovers that while first-generation Transformer models, like TFT, underperform in financial forecasting, second-generation models like Informer, Autoformer, and PatchTST demonstrate remarkable efficacy, especially in scenarios characterized by limited historical data and market volatility. The study also highlights the nuanced performance of these models across different forecasting horizons and error metrics, showcasing their potential as robust tools in financial forecasting, which contradicts the findings of Zeng et al. (2023)

This paper contributes to the financial forecasting literature by providing a comprehensive analysis of the applicability of Transformer-based models in this domain. It offers new insights into the capabilities of these models, especially their adaptability to different market conditions and forecasting requirements, challenging the existing skepticism created by Zeng et al. (2023) about their utility in financial forecasting.

This paper verifies whether popular Internet information from Internet forum and search engine exhibit useful content for forecasting the volatility in Chinese stock market.

First, the authors’ study commences with several HAR-RV-type models, then the study amplifies them respectively with the posting volume and search frequency to construct HAR-IF-type and HAR-BD-type models. Second, from in-sample and out-of-sample analysis, the authors empirically investigate the interpretive ability, forecasting performance (statistic and economic). Third, various robustness checks are utilized to reconfirm the authors’ findings, including alternative forecast window, alternative evaluation method and alternative stock market. Finally, the authors further discuss the forecasting performance in different forecast horizons (h = 5, 10 and 20) and asymmetric effect of information from Internet forum.

From in-sample perspective, the authors discover that posting volume exhibits better analytical ability for Chinese stock volatility than search frequency. Out-of-sample results indicate that forecasting models with posting volume could achieve a superior forecasting performance and increased economic value than competing models.

These findings can help investors and decision-makers obtain higher forecasting accuracy and economic gains.

This study enriches the existing research findings about the volatility forecasting of stock market from two dimensions. First, the authors thoroughly investigate whether the Internet information could enhance the efficiency and accuracy of the volatility forecasting concerning with the Chinese stock market. Second, the authors find a novel evidence that the information from Internet forum is more superior to search frequency in volatility forecasting of stock market. Third, they find that this study not only compares the predictability of the posting volume and search frequency simply, but it also divides the posting volume into “good” and “bad” segments to clarify its asymmetric effect respectively.

This study aims to verify whether posting volume and search frequency contain predictive content for estimating the volatility in Chinese stock market.

The forecasting model with posting volume can achieve a superior forecasting performance and increases economic value than competing models.

The results are robust in alternative forecast window, alternative evaluation method and alternative market index.

The posting volume still can help to forecast future volatility for mid- and long-term forecast horizons. Additionally, the role of posting volume in forecasting Chinese stock volatility is asymmetric.

While numerous empirical studies have tried to model and forecast the oil price volatility over the years, such attempts using the crude oil volatility index (OVX) rarely exist. In order to conceal this void, the purpose of this paper is to investigate whether including OVX in the realized volatility (RV) models improve the accuracy of predictions.

At the empirical stage, the authors employ several measures to frame the RV of crude oil futures returns. In particular, the authors use three different range-based RV estimators recommended by Parkinson (1980), Rogers and Satchell (1991) and Alizadeh et al. (2002), respectively.

The findings reveal that the information content of crude OVX helps to provide more accurate volatility predictions in comparison to the base-line RV model which contains only historical oil volatilities. Besides, the forecast encompassing test further suggests that the modified RV model (when OVX is introduced in the base-line RV model) forecast encompasses the conventional RV forecast in majority of the cases.

Since forecasting oil price volatility plays a vital role in portfolio optimization, derivatives pricing, optimum asset allocation decisions and risk management, the findings of this study thus carry important implications for energy economists, investors and policymakers.

This paper adds to the existing literature, since it is one of the initial studies to explore whether OVX is informative about the realized variance of the US oil market returns. The findings recommend that the information content of oil implied volatilities should be taken into account when modeling the US oil market volatility. In addition, range-based measures should be utilized while estimating the RV.

This paper examines the relationship between volatility, sentiment and returns in terms of levels and changes for both lower and higher data frequencies using quantile regression (QR) method.

In the first step, the study applies the Granger causality test to understand the causal relationship between realized volatility, returns and sentiment as levels and changes. In the second step, the study employs a QR method to investigate whether investor sentiment and returns can predict realized volatility. This regression method gives robust results irrespective of distributional assumptions and to outliers in the dependent variable.

Empirical results show that the VIX volatility index is a better fear gauge of market-wide investors' sentiments and has a predictive power for future realized volatility in terms of levels and changes for both higher and lower data frequencies. This study provides evidence that the relationship between realized volatility, investor sentiment and returns, respectively, is not symmetric for all quantiles of QR, as opposed to OLS regression. Furthermore, this work supports the behavioral theory beyond leverage hypothesis in explaining the asymmetric relation between returns and volatility at higher and lower data frequencies.

This paper adds to the limited understanding of investor sentiment’s impact on volatility by proposing a QR model which provides a more complete picture of the relationship at all parts of the volatility distribution for both higher and lower data frequencies and in terms of levels and changes. To the author knowledge, this is the first paper to study the volatility responses to positive and negative sentiment changes for developed market and to use both lower and higher data frequencies as well as data in terms of levels and changes.

The purpose of this paper is to investigate the role of volatility risk on stock return predictability specified on two global financial crises: the dot-com bubble and recent financial crisis.

Using a broad sample of stock options traded on the American Stock Exchange and the Chicago Board Options Exchange from January 2001 to December 2010, the effect of different idiosyncratic volatility forecasting measures are examined on future stock returns in four different periods (Bear and Bull markets).

First, the authors find clear and robust empirical evidence that the implied idiosyncratic volatility is the best stock return predictor for every sub-period both in Bear and Bull markets. Second, the cross-section firm-specific characteristics are important when it comes to stock returns forecasts, as the latter have mixed positive and negative effects on Bear and Bull markets. Third, the authors provide evidence that short selling constraints impact negatively on stock returns for only a Bull market and that liquidity is meaningless for both Bear and Bull markets after the recent financial crisis.

These results would be helpful to disclose more information on the best idiosyncratic volatility measure to be implemented in global financial crises.

This study empirically analyses the effect of different idiosyncratic volatility measures for a period that involves both the dotcom bubble and the recent financial crisis in four different periods (Bear and Bull markets) and contributes the existing literature on volatility measures, volatility risk and stock return predictability in global financial crises.