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This study develops a novel approach for improving stock return volatility forecasts using volatility index information with the entropic tilting technique. Unlike traditional linear heteroskedasticity autoregressive methods with option‐implied information, we first derive predictive densities from traditional models, and then tilt using both the first and second moments of the risk‐neutral distribution, which enables us to capture the nonlinear effect in our specification. The empirical findings demonstrate a substantial enhancement in the forecasting accuracy of all models once the first‐ and second‐moment information is considered, where the improvement is both statistically and economically significant. These results have important implications for risk management in well‐established derivatives markets. [ABSTRACT FROM AUTHOR]

The contemporary world faces significant challenges in energy crises and climate change. To analyze the relationship between energy and climate, we explore the influence of the climate-related attention of G20 countries on renewable energy stock volatility forecasting under the framework of the extended GARCH-MIDAS model. In the context of COP26, we further adopt natural language processing technology and shrinkage approach to obtain Google search volume for 107 climate-related keywords and then construct new climate risk attention indicators. The in-sample parameter estimation results show that the climate attention of G20 countries has a remarkable positive effect on the renewable energy stock market volatility. The out-of-sample results demonstrate that the climate attention of different countries exerts varying influences on the volatility of the renewable energy stock market. Climate risk and energy issues are among the serious challenges facing the 21st century, and reducing greenhouse gas emissions and finding cleaner energy is an urgent task. As the response to climate change necessitates diverse strategies in various countries, our research can offer valuable guidance and serve as a reference for national energy transitions and the selection of alternative energy solutions. [ABSTRACT FROM AUTHOR]

This paper explores the predictive role of speculative sentiment on gold market volatility and its economic implications. Utilizing high frequency data for gold futures and speculative sentiment proxies for the gold and stock markets, we show that incorporating speculative sentiment in volatility models can improve volatility forecasts both in- and out-of-sample. While gold market sentiment helps improve the predictive accuracy of volatility models primarily in the short run, we show that cross-market speculative sentiment from the stock market has predictive ability at relatively longer forecast horizons. More importantly, incorporating speculative sentiment proxies in volatility models yields improved cumulative returns as well as Sharpe ratios for investors who allocate part of their portfolios in this traditional safe haven asset. Finally, examining the predictability of asymmetric risk patterns, we show that speculative sentiment in the gold market matters relatively more for good volatility forecasts and in the short and intermediate forecast horizons, while cross-market sentiment effect from the stock market generally applies to longer term forecasts for both the good and bad volatility metrics. Overall, our findings show that speculative sentiment contains significant incremental information over subsequent volatility patterns in gold, offering sizeable economic gains for a mean-variance investor in a practical investment setting. [ABSTRACT FROM AUTHOR]

This study develops a novel periodic regime-switching model (the PRS model) to improve the forecasting of stock market volatility by accounting for the information from non-trading and trading periods, including regular trading and after-hour trading. Empirical analysis of the Taiwan Futures Exchange (TAIFEX) demonstrates the significant improvements of the PRS model in both in-sample and out-of-sample periods. Our results also show that the introduction of after-hour trading sessions has provided valuable information for volatility forecasting in subsequent regular trading sessions, emphasizing the importance of considering diverse information flows across different trading and non-trading times. The PRS model effectively captures the dynamics of non-trading and trading sessions and the influence of unusual news arrivals and jumps on market volatility, contributing to investment and risk management strategies. [ABSTRACT FROM AUTHOR]

Because climate change broadcasts a large aggregate risk to the overall macroeconomy and the global financial system, we investigate how a temperature anomaly and/or its volatility affect the accuracy of forecasts of stock return volatility. To this end, we do not apply only the classical GARCH and GARCHX models, but rather we apply newly proposed model-free prediction methods, and use GARCH-NoVaS and GARCHX-NoVaS models to compute volatility predictions. These two models are based on a normalizing and variance-stabilizing transformation (NoVaS transformation) and are guided by a so-called model-free prediction principle. Applying the new models to data for South Africa, we find that climate-related information is helpful in forecasting stock return volatility. Moreover, the novel model-free prediction method can incorporate such exogenous information better than the classical GARCH approach, as revealed by the the squared prediction errors. More importantly, the forecast comparison test reveals that the advantage of applying exogenous information related to climate risks in prediction of the South African stock return volatility is significant over a century of monthly data (February 1910–February 2023). Our findings have important implications for academics, investors, and policymakers. [ABSTRACT FROM AUTHOR]

This article attempts to examine the predictability of a significant number of uncertainty indices for the G7 stock market volatility based on a Heterogeneous AutoRegressive Realized Volatility (HARRV) model and a combination forecast framework during the global pandemic COVID-19. We include in our analysis the Infectious Disease Equity Market Volatility (IDEMV), the VIX, the Economic Policy Uncertainty (EPU), the Equity Market Volatility (EMV), the Geopolitical risk (GPR) as well as the crude oil futures' realized volatility. Out-of-sample evidence shows that models incorporating all uncertainty indices improve forecasting performance for most stock markets' volatility during a long out-of-sample period and also during the coronavirus period. The results are robust using an alternative volatility model, an alternative realized measure and a recursive window analysis. The predictability of the uncertainty indices is also evaluated through risk management and portfolio loss functions and results suggest that the LASSO combination and a HARRV model including all indices are the most profitable for all stock markets during the global pandemic. [ABSTRACT FROM AUTHOR]

This paper attempts to extend the approach of quantitative investment and provide investors with suggestions about volatility timing. Based on the volatility-managed portfolios strategy, we propose two volatility–tail risk-managed portfolios strategies by combining the volatility and tail risk forecasting methods. We subject seven indices in China's stock market to verify the performance of these two strategies. The empirical results show that volatility–tail risk-managed portfolios yield better performance than buy-and-hold portfolios and volatility-managed portfolios in terms of annualized average returns, Sharpe ratio, maximum drawdown and Calmer ratio. Furthermore, we find that the effectiveness also depends on the volatility clustering of portfolios and the selection of investment periods. [ABSTRACT FROM AUTHOR]

Insurers and pension funds face the challenges of historically low-interest rates and high volatility in equity markets, that have been accentuated due to the COVID-19 pandemic. Recent advances in equity portfolio management with a target volatility have been shown to deliver improved on average risk-adjusted return, after transaction costs. This paper studies these targeted volatility portfolios in applications to equity, balanced, and target-date funds with varying constraints on leverage. Conservative leverage constraints are particularly relevant to pension funds and insurance companies, with more aggressive leverage levels appropriate for alternative investments. We show substantial improvements in fund performance for differing leverage levels, and of most interest to insurers and pension funds, we show that the highest Sharpe ratios and smallest drawdowns are in targeted volatility-balanced portfolios with equity and bond allocations. Furthermore, we demonstrate the outperformance of targeted volatility portfolios during major stock market crashes, including the crash from the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

This paper uses the generalized autoregressive conditional heteroscedasticity mixing data sampling (GARCH‐MIDAS) model to construct three types of extended models. Geopolitical risk uncertainty is included in the study as an introduced variable, and its impact on the Shanghai Stock Exchange (SSE) 50 index volatility is analyzed. The empirical analysis shows that the GARCH‐MIDAS‐RV‐EPU model with China's EPU is the best in predicting the volatility of China's stock market when the information of economic policy uncertainty (EPU) and geopolitical risk uncertainty (GPR) of other countries are included. When the common information model composed of China's economic policy uncertainty index and geopolitical uncertainty index is used to predict the volatility of the SSE, the model's prediction is better. Finally, when the model confidence set (MCS) and the interval length index that changes the forecast outside the sample are used to retest each conclusion, the results are very robust. [ABSTRACT FROM AUTHOR]

The research study voyage commences with the foundational objective of fitting a suitable Generalized Autoregressive Conditional Heteroscedastic (GARCH) model to assess market volatility, a fundamental pillar of financial analysis. This research embarks on an ambitious quest to predict and understand stock market volatility within the realms of the DJIA and S&P 500 of USA and ATX index of Austria using different sophisticated GARCH models. The dataset used in this study comprises daily stock market data for two key indices: the S&P 500 Index, representing the USA stock market, and the ATX Index, representing the Austria stock market. Additionally, the DJIA Index, another representative of the USA stock market, was included. The dataset consists of 5967 daily observations over the specified time period from January 3, 2000, to September 21, 2023. The observation of results, analysis and discussion depicts that PARCH model shows most promising results and found suitable to model the volatility patterns of the selected indices. The findings and methodologies presented in this paper can be seen as a solid foundation upon which to build future investigations, refining our ability to anticipate market movements and make informed decisions in an uncertain financial landscape. In closing, this research not only contributes to the body of knowledge in financial econometrics but also underscores the importance of modeling long-term stock market behavior with precision and diligence. [ABSTRACT FROM AUTHOR]

Forecasting salmon market volatility is crucial for reducing future uncertainty for market participants. This study explores the efficacy of the Long Short-term Memory (LSTM) network, a deep learning technique, in forecasting multi-step ahead salmon market volatility. The performance of the LSTM is assessed against a constructed volatility proxy and the Autoregressive Moving Average (ARMA) model, a traditional benchmark in time-series analysis. Evaluation is performed across various forecasting horizons using different forecast error measures. Our findings indicate that the ARMA model outperforms the LSTM in predicting salmon market volatility, suggesting that any non-linear patterns in the salmon market volatility might be too insignificant for an LSTM model to exploit effectively. However, we observed a significant discrepancy between the actual volatility values and the forecasts obtained by both models, indicating the complexity of accurately predicting salmon market volatility. [ABSTRACT FROM AUTHOR]

This paper explores the forecasting power of some of the most informative indicators of economic uncertainty on carbon emission price volatility. We use one- and two-component GARCH-MIDAS models based on mixed frequency data and Model Confidence Set (MCS) testing with 'rolling scheme' forecast method to examine the forecasting performance of economic uncertainty indicators. We employ an economic policy uncertainty (EPU) indicator with China, US and global economic policy uncertainty constituents together with traditional uncertainty indicators, such as macroeconomic and financial market volatility. Our empirical findings show that generally economic policy uncertainty indicators contain more information of carbon emission price volatility than other indicators. Specifically, one-component GARCH-MIDAS model with the EPU indicator with China constituent and two-component model with EPU indicator have superior performance in forecasting carbon emission price volatility of the Guangdong pilot in China. Our study adds insights into factors that affect carbon emission price movements. [ABSTRACT FROM AUTHOR]

The shocks on certain market spread to other markets due to the financial linkages of global economy, which is known as volatility spillover effect. In this study, we propose a volatility forecasting model for global market indices using the spatial-temporal graph neural network (GNN). The volatility spillover between markets are reflected in the model by estimating the linkage between markets, which is the input of GNN, using the volatility spillover index. An empirical analysis is conducted on eight representative global market indices. From the out-of-sample results, we found the following features. First, the proposed spatial-temporal GNN spillover model outperforms the benchmark models in short- and mid-term forecasting. Second, the forecasting accuracy highly depends on the inclusion of the market index with a high volatility spillover effect. Including S&P500, which contains the highest net spillover index, effectively helps to forecast the volatilities of other markets. Third, the investor can gain economic gain by using predicted volatility from proposed model in the mean-variance framework. [ABSTRACT FROM AUTHOR]

This paper employs a shrinkage method named Adaptive Lasso (ALasso) to predict the realized volatility (RV) of the China's stock market with numerous predictors. We observed from the out‐of‐sample predictions that the ALasso model exhibits better predictive power than its competitors, implying that the ALasso method can select stronger predictors in the forecasting process than competing models. In addition, the predictability of ALasso method is better in low volatility periods than in high volatility periods. Finally, several robustness check methods, including different forecasting windows, different low and high volatility division, and different volatility measures, supported our results. [ABSTRACT FROM AUTHOR]

This paper investigates whether the cross‐sectional variance (CSV) of stock returns and its asymmetric components contain incremental information to predict stock market volatility under a high‐frequency, heterogeneous autoregressive (HAR) model framework. We present novel evidence that CSV is a powerful predictor of future realized volatility, both in‐ and out‐of‐sample, even after controlling for the well‐established predictors obtained from intraday data. Further analysis suggests that distinguishing between positive and negative CSV components in the forecasting model enhances the predictive capability of volatility models at all out‐of‐sample forecasting horizons, with the asymmetric HAR‐type‐ACSV model consistently outperforming all alternative HAR‐type variations. We argue that the asymmetries in the predictive relation between CSV and volatility are largely driven by the disagreement among market participants that spikes during bad times. Finally, economic analysis shows that incorporating CSV in the forecasting model can generate sizeable economic gains for a mean–variance investor, suggesting that out‐of‐sample predictive ability of CSV can be exploited in forward looking investment strategies to enhance investment returns. [ABSTRACT FROM AUTHOR]

Recently, deep neural networks have been widely used to solve financial risk modeling and forecasting challenges. Following this hotspot, this paper presents a mixture model for conditional volatility probability forecasting based on the deep autoregressive network and the Gaussian mixture model under the GARCH framework. An efficient algorithm for the model is developed. Both simulation and empirical results show that our model predicts conditional volatilities with smaller errors than the classical GARCH and ANN-GARCH models. [ABSTRACT FROM AUTHOR]

In this study, we provide the predictive linkage between global terrorist attacks and stock market volatility. We propose the predictive model by extending the prevailing heterogeneous autoregressive model for realized volatility (HAR-RV) with global terrorism and denote it as HAR-RV-GT. According to the Diebold – Mariano test and the model confidence set, we consistently find the superior forecasting performance delivered by the HAR-RV-GT model. For comprehensive empirical results, we extend the key finding to various settings including the consideration of popular jump and leverage effects, the use of more types of forecasting models, and the inclusion of long-horizon global terrorist attacks as well as the domestic terrorist attacks in the US. Additionally, the substantial economic gains based on a mean-variance investor confirm the valuable forecasting role of terrorist attacks. Our results are robust to a wide range of checks. [ABSTRACT FROM AUTHOR]

Investor sentiment and herding, as two important indicators of investors' beliefs, both have great impacts on stock market volatility. Based on the daily transaction data in the Chinese stock market, we measure investor sentiment and herding on monthly basis from 2005 to 2020. Then, two indicators are, respectively, introduced to GARCH-MIDAS model for volatility forecasting. Moreover, their forecasting abilities are compared with five GARCH-type models in four crisis periods and two up markets through the model confidence set (MCS) test. We find that herding can better explain the long-term component of volatility than investor sentiment in GARCH-MIDAS model. What is more, both investor sentiment and herding possess excellent out-of-sample forecasting performances. Finally, the forecasting ability of herding is overwhelming compared with investor sentiment in crisis periods, while investor sentiment presents better prediction accuracy than herding in up markets. Our research shows that the forecasting abilities of investor sentiment and herding on volatility may be related to the market states. [ABSTRACT FROM AUTHOR]

Purpose: 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). Design/methodology/approach: 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. Findings: 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. Originality/value: 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. [ABSTRACT FROM AUTHOR]

This paper presents two different approaches of volatility forecasting. One is based on option‐implied volatility (IV), the other involves conducting time series methods using historical volatility. With that purpose, we study eight developed stock markets, offering implied volatility indexes for the 2008 financial crisis. We evaluated the 1 month out‐of‐sample volatility forecast performance of two statistical‐based models, Mixed Data Sampling (MIDAS) and GARCH, and compared the results with option‐implied volatility indexes. Our results suggest that MIDAS produce superior forecast performance compared to GARCH model and IV method. While options are not available for all assets, we believe that MIDAS model can be a sophisticated tool for researchers and analysts to forecast future volatility with its ability to process high‐frequency data. [ABSTRACT FROM AUTHOR]

This research investigates whether the US stock volatility index (S&P 500 index) has the forecasting ability to predict the volatility of CAC index (France), DAX index (Germany), and FTSE index (the UK) by employing a multivariate heterogeneous autoregressive realized volatility jump (MHAR-RV-CJ) model. Our empirical results provide consolidated comparisons using univariate and multivariate models. The in-sample results show us the US volatility will improve the long-term volatility regression coefficient. Moreover, our proposed model, the MHAR-RV-CJ model, nearly surpasses all competing models at out-of-sample forecasting, indicating that considering the multivariate DCC-GARCH information between US-France, US-Germany, and US-UK stock markets and jump component structures can help to predict individual European stock market volatility. Unsurprisingly, several forecasting evaluation tests and further analysis (high/low volatility) confirm the robustness of our results. [ABSTRACT FROM AUTHOR]

In this paper, we improve the ordinary least squares (OLS) estimation approach by replacing a normally distributed error with a t-distributed error. Empirically, we investigate the predictability of the Chinese stock market volatility based on this modified approach. Results show that the modified OLS method with a t-distributed error has a significantly stronger forecasting power than its counterpart with a normally distributed error. From an asset allocation perspective, the modified OLS approach can help a mean-variance investor obtain sizeable utility gains. We also conduct two extended empirical analyses and further verify the superiority of the regression approach with a t-distributed error. Our results are robust to a series of settings. Finally, we find that the regression approach with a t-distributed error shows greater tolerance for outliers by assigning smaller weights to them, thereby highlighting its superior performance. [ABSTRACT FROM AUTHOR]

This paper focuses on distinguishing the predictive power of five newly developed uncertainty indices, that is, global and US economic policy uncertainty (GEPU and US EPU), global geopolitical risk (GPR), US monetary policy uncertainty (MPU) and equity market volatility (EMV), on crude oil market volatility. We construct the extended GARCH‐MIDAS models to assess the forecasting ability of these indices and then compare them with four commonly used oil volatility drivers: oil supply, oil demand, oil speculation and interest rate. Firstly, the in‐sample analysis suggests that all uncertainty indices and traditional determinants have significant impacts on oil volatility. Secondly, the out‐of‐sample evaluations suggest that US MPU, EMV and EPU indices are more powerful than other predictors to improve the forecasting accuracy of crude oil volatility, and US EMV exhibits the best prediction ability. Then, we find that EPU and MPU indices are more effective in forecasting high volatility in crude oil market, while traditional determinants together with US EMV index are more helpful in predicting low oil volatility. Finally, the forecasts of bad oil volatility can be improved by all predictors except for GEPU index, while the forecasts of good oil volatility can only be enhanced by US MPU, EMV and EPU. [ABSTRACT FROM AUTHOR]

The shipping index has the characteristics of violent fluctuation, so its volatility is difficult to predict. To better predict the volatility of the shipping market, this paper proposes an AR-SVR-GARCH model, which combines traditional time series analysis and modern machine learning methods. This model overcomes linear limitations of traditional methods. Meanwhile, this paper proposes 1another AR-SVR-GJR model which can explain the leverage effect. Empirical results show that the two models proposed in this paper have good volatility prediction ability in the dry bulk shipping market, the crude oil shipping market and the shipping stock market. This indicates that the proposed models have portability among different shipping markets. In addition, the AR-SVR-GARCH model and the AR-SVR-GJR model have stable volatility prediction performance in shipping markets during the financial crisis and in the recent time. [ABSTRACT FROM AUTHOR]

Over the past years, cryptocurrencies have drawn substantial attention from the media while attracting many investors. Since then, cryptocurrency prices have experienced high fluctuations. In this paper, we forecast the high-frequency 1 min volatility of four widely traded cryptocurrencies, i.e., Bitcoin, Ethereum, Litecoin, and Ripple, by modeling volatility to select the best model. We propose various generalized autoregressive conditional heteroscedasticity (GARCH) family models, including an sGARCH(1,1), GJR-GARCH(1,1), TGARCH(1,1), EGARCH(1,1), which we compare to a multivariate DCC-GARCH(1,1) model to forecast the intraday price volatility. We evaluate the results under the MSE and MAE loss functions. Statistical analyses demonstrate that the univariate GJR-GARCH model (1,1) shows a superior predictive accuracy at all horizons, followed closely by the TGARCH(1,1), which are the best models for modeling the volatility process on out-of-sample data and have more accurately indicated the asymmetric incidence of shocks in the cryptocurrency market. The study determines evidence of bidirectional shock transmission effects between the cryptocurrency pairs. Hence, the multivariate DCC-GARCH model can identify the cryptocurrency market's cross-market volatility shocks and volatility transmissions. In addition, we introduce a comparison of the models using the improvement rate (IR) metric for comparing models. As a result, we compare the different forecasting models to the chosen benchmarking model to confirm the improvement trends for the model's predictions. [ABSTRACT FROM AUTHOR]

We investigate the effectiveness of various volatility models using China's Shanghai Stock Exchange‐50 (SSE50) Index. Regarding in‐sample fit, the generalized autoregressive conditional heteroscedasticity (GARCH) and the variants of the GARCH model perform much better than the autoregressive conditional heteroscedasticity model. However, we do not observe any significant asymmetric volatility response to past returns in the GJR–GARCH model. For out‐of‐sample forecasting of the future realized volatility, in five of the seven options we investigate, the GARCH volatility forecast outperforms the option implied volatility. We formulate a trading strategy by exploiting the spread between the GARCH volatility forecast and the option implied volatility, and show robust profits when applied to the SSE50 options. [ABSTRACT FROM AUTHOR]

This paper introduces a novel class of volatility forecasting models that incorporate market realized (co)variances and semi(co)variances within the framework of a heterogeneous autoregressive (HAR) model. Our empirical analysis shows statistically and economically significant forecasting gains. For our most parsimonious market-HAR specification, stock volatility forecasting is improved by 9.80% points. Using a mixed sampling frequency market-HAR variant with low (high) sampling frequency for the stock (market) improves forecasting by a further 6.90% points. Our paper also develops noise-robust estimators to facilitate the use of realized semi(co)variances at high sampling frequencies. [ABSTRACT FROM AUTHOR]

This article examines whether incorporating investors' uncertainty, as captured by the conditional volatility of sentiment, can help forecasting volatility of stock markets. In this regard, using the Markov-switching multifractal (MSM) model, we find that investors' uncertainty can substantially increase the accuracy of the forecasts of stock market volatility according to the forecast encompassing test. We further provide evidence that the MSM outperforms the dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model. [ABSTRACT FROM AUTHOR]

In this study, the volatilities of CSI300 index futures, SSE50 index futures, and CSI500 index futures are modeled and predicted based on both parametric and nonparametric modeling approaches. Four ARMA-GARCH-type models and four HAR-type models are taken as the framework of volatility prediction. The last one-third of transaction data are used as the testing sample and the rolling window approach is adopted for prediction. The best predictive models for these three stock index futures vary with the properties of the futures, while volatility prediction based on the HAR-type models always has a higher accuracy than the ARMA-GARCH-type models. Moreover, we find that the property of target assets influences the performance of models, and the choice of extended models in prediction is suggested to be based on the peculiarity in the sample. [ABSTRACT FROM AUTHOR]

In this study, we explore the linkage between high frequency trading activity and market volatility. Though the findings of existing studies for this topic until now are rather inconclusive, they agree with the impact HFT exert to volatility. In our design, we take advantage of the trading activity to predict real time trading. We use the VIXY which is an ETF of VIX and focuses on short term performance, finding that the ask side of high frequency trading activities has far more significant predictability for both the level of VIXY and return of VIXY, while the bid side seems to be trivial. [ABSTRACT FROM AUTHOR]

This study mainly explores whether the implied volatility indices of international stock markets and crude oil contain useful information in predicting the realized volatility (RV) of the German stock market. We use the standard predictive regression model, principal component analysis, five combination methods, and two shrinkage models to generate forecasts of DAX index volatility. First, the in-sample results indicate that almost all of the implied volatility indices considered have significant predictive power for the RV of the German DAX index. Second, the out-of-sample predictions suggest that the two shrinkage models exhibit the best out-of-sample predictions. Furthermore, a mean-variance investor can allocate portfolios through volatility predictions based on shrinkage models to achieve considerable economic gains. Finally, our conclusions are supported by numerous robustness checks. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the ability of market‐based intermediary capital risk to predict the volatility of crude oil futures. Using shocks to the market‐based intermediary capital ratio (IC_RF) of primary dealers as a predictor, this paper shows that IC_RF exhibits statistically and economically significant predictive power for the volatilities of crude oil futures, from both in‐sample and out‐of‐sample perspectives. The revealed predictability is also of economic significance, because it enables examining of the performance of portfolios that are constructed on IC_RF‐based forecasts of oil volatility. Additionally, IC_RF contains robust predictive power beyond what is embedded in traditional fundamental variables. Further analysis provides solid evidence supporting the superiority of asymmetric and regime change models over linear specifications in forecasting oil realized volatility, and it only provides weak evidence for implied oil volatility. [ABSTRACT FROM AUTHOR]

We investigate the information content of five uncertainty indices for the US natural gas futures volatility forecasting. Our investigation is based on the GARCH‐MIDAS framework. The in‐sample outcomes suggest that most of uncertainty indices have a crucial effect on natural gas futures volatility. And the out‐of‐sample prediction results indicate that the geopolitical risk (GPR) and equity market volatility (EMV) indices contain more useful information for natural gas futures volatility. Moreover, according to the empirical results of special periods, we observe that the EMV index exhibits superior predictive ability under the periods of postcrisis, expansions, and low volatility. Our results are confirmed by several robustness checks. [ABSTRACT FROM AUTHOR]

This paper explores the effects of global economic policy uncertainty (GEPU) on conditional volatility in the gold futures market using Markov regime‐switching GARCH‐MIDAS models. The in‐sample empirical results suggest that GEPU indeed contains predictive information for the gold futures market, and higher GEPU leads to higher volatility within the gold futures market. Moreover, the novel model, which adds Markov regime switching with time‐varying transition probabilities and the GEPU index, achieves relatively better performance than those of the other competing models from a statistical point of view. Furthermore, we discuss the asymmetric effects of different changes in GEPU on the gold futures market and the models' performances with different horizons, and we find that our new model has better predictive performance under negative changes in GEPU than under positive changes in GEPU. Further discussion also confirms that our previous findings are robust during two special cases, the global financial crisis and European debt crisis, during which the market suffered from fierce fluctuations and was fraught with considerable uncertainty. [ABSTRACT FROM AUTHOR]

This paper examines the use of survey-based measures in volatility forecasting. We argue that an aggregate volatility forecast built up from individual forecasts should be the sum of individual expected volatilities and the dispersion in mean return forecasts. We use data coming from a repeated survey to capture volatility expectations and mean returns of investors, and to produce aggregate volatility forecasts. Our survey-based volatility forecasts are consistent and quantitatively similar with forecasts based on GARCH and implied volatility models. This result is robust to both in-sample and out-of-sample comparisons and in response to news. [ABSTRACT FROM AUTHOR]

This paper investigates the benefits of jointly using several realized measures in predicting daily price volatility, Value-at-Risk, and Expected Shortfall in the Australian electricity markets of New South Wales, Queensland, and Victoria. We propose using Realized GARCH-type models with multiple measurement equations based on robust estimators to account for market microstructure noise and jumps in electricity price series. The model specifications that combine information from multiple realized measures improve the in-sample fit of the data. The out-of-sample analysis shows that use of the jump-robust medRV estimator significantly increases the accuracy of volatility forecasts, while in forecasting Value-at-Risk and Expected Shortfall at different risk levels, the standard GARCH(1,1) also performs remarkably well. [ABSTRACT FROM AUTHOR]

In this paper, we investigate whether the model-free implied volatility index iVX officially launched by the Shanghai Stock Exchange has incremental explanatory power for future volatility in the SSE 50 ETF. In particular, we concentrate on Heterogeneous Autoregressive model of realized volatility and iVX (HAR-RV-iVX). We use both in-sample and out-of-sample predictive regressions to empirically indicate that the iVX significantly improves the forecasting performance of the realized volatility of SSE 50 ETF. [ABSTRACT FROM AUTHOR]

Models based on factors such as size or value are ubiquitous in asset pricing. Therefore, portfolio allocation and risk management require estimates of the volatility of these factors. While realized volatility has become a standard tool for liquid assets, this measure is difficult to obtain for asset pricing factors such as size and value that include smaller illiquid stocks that are not traded at a high frequency. Here, we provide a simple approach to estimate the volatility of these factors. The efficacy of this approach is demonstrated using Monte Carlo simulations and forecasts of the market volatility. [ABSTRACT FROM AUTHOR]

The major aim of this empirical study is to estimate the volatility time series returns for a cluster of international stock markets, such as: Switzerland, Austria, China and Hong Kong. The paper demonstrates statistical modeleling in order to capture volatility clusters and changes in long and short term volatility impact. The econometric approch is based on randomly selected daily closing return collected for the main indices of stock markets in Switzerland, Austria, China and Hong Kong for the sample period January 2003 to September 2021. We used various statistical properties to test normalities based on using GARCH family models for estimating financial market volatility. Moreover, the sampled time interval includes two extreme events such as the global financial crisis (GFC) of 2007--2008 and the recent COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

We decompose economic uncertainty into "good" and "bad" components according to the sign of innovations. Our results indicate that bad uncertainty provides stronger predictive content regarding future market volatility than good uncertainty. The asymmetric models with good and bad uncertainties forecast market volatility in a better way than the symmetric models with overall uncertainty. The combination for asymmetric uncertainty models significantly outperforms the benchmark of autoregression, as well as the combination for symmetric models. The revealed volatility predictability is further demonstrated to be economically significant in the framework of portfolio allocation. [ABSTRACT FROM AUTHOR]

To forecast the volatility of Chinese stock market, we use information from 28 international markets rather than relying on the Chinese market alone. A common factor, which we call the international volatility index, is constructed by the first principal component of all the 28 cross‐national stock market volatilities. We then add the international volatility index into the prevailing heterogeneous autoregressive model of realized volatility (HAR‐RV). The in‐sample estimation results show that the impact of this common index on future Chinese market volatility is statistically significant and positive. More importantly, the out‐of‐sample forecasting results suggest that our proposed model outperforms competing models including the HAR‐RV, kitchen sink model, and combination approaches. The results are similar when we use a wide range of robustness checks. Furthermore, the international volatility index also yields the highest economic value from an asset allocation perspective. [ABSTRACT FROM AUTHOR]

This paper introduces a novel generalized autoregressive conditional heteroskedasticity–mixed data sampling–extreme shocks (GARCH‐MIDAS‐ES) model for stock volatility to examine whether the importance of extreme shocks changes in different time ranges. Based on different combinations of the short‐ and long‐term effects caused by extreme events, we extend the standard GARCH‐MIDAS model to characterize the different responses of the stock market for short‐ and long‐term horizons, separately or in combination. The unique timespan of nearly 100 years of the Dow Jones Industrial Average (DJIA) daily returns allows us to understand the stock market volatility under extreme shocks from a historical perspective. The in‐sample empirical results clearly show that the DJIA stock volatility is best fitted to the GARCH‐MIDAS‐SLES model by including the short‐ and long‐term impacts of extreme shocks for all forecasting horizons. The out‐of‐sample results and robustness tests emphasize the significance of decomposing the effect of extreme shocks into short‐ and long‐term effects to improve the accuracy of the DJIA volatility forecasts. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of the economic policy uncertainty (EPU) indexes of China and the G7 countries on Chinese stock market volatility and further constructs a new diffusion index based on these indexes using principal component analysis (PCA) to achieve enhanced predictive ability. The in-sample results indicate that the EPU indexes of China and some of the G7 countries show a significantly negative impact on future volatility. Moreover, our constructed diffusion index also has a significantly negative impact. Furthermore, the out-of-sample results show that this diffusion index exhibits a significantly higher forecast accuracy than the EPU itself and combination forecasts. Finally, various robustness checks are consistent with our main conclusions. Overall, we construct a new and useful indicator that can substantially increase forecast accuracy with respect to the Chinese stock market. [ABSTRACT FROM AUTHOR]

• Volatility forecasts are improved by stacking machine learning algorithms. • Merging Artificial Neural Networks with other models increases its forecasting power. • Regardless of the volatility model, high volatile regimes lead to higher error rates. • Risk measurements precision is increased by stacking machine learning models. An appropriate calibration and forecasting of volatility and market risk are some of the main challenges faced by companies that have to manage the uncertainty inherent to their investments or funding operations such as banks, pension funds or insurance companies. This has become even more evident after the 2007–2008 Financial Crisis, when the forecasting models assessing the market risk and volatility failed. Since then, a significant number of theoretical developments and methodologies have appeared to improve the accuracy of the volatility forecasts and market risk assessments. Following this line of thinking, this paper introduces a model based on using a set of Machine Learning techniques, such as Gradient Descent Boosting, Random Forest, Support Vector Machine and Artificial Neural Network, where those algorithms are stacked to predict S&P500 volatility. The results suggest that our construction outperforms other habitual models on the ability to forecast the level of volatility, leading to a more accurate assessment of the market risk. [ABSTRACT FROM AUTHOR]

Theory suggests a strong link between monetary policy rate uncertainty and equity return volatility, since asset pricing models assume the risk-free rate to be a key factor for equity prices. Given this, our paper uses historical monthly data for the United Kingdom over 1833:01 to 2018:07, to show that monetary policy uncertainty increases stock market volatility within sample, which in turn continues to hold under various robustness analyses. In addition, we show that the information on monetary policy uncertainty also adds value to forecasting out-ofsample equity market volatility. [ABSTRACT FROM AUTHOR]

In this study, we explore the effect of cojumps within the agricultural futures market, and cojumps between the agricultural futures market and the stock market, on stock volatility forecasting. Also, we take into account large and small components of cojumps. We have several noteworthy findings. First, large jumps may lead to more substantial fluctuations and are more powerful than small jumps. The effect of cojumps and their decompositions on future volatility are mixed. Second, a model including large and small cojumps between the agricultural futures market and the stock market can achieve a higher forecasting accuracy, implying that large and small cojumps contain more useful predictive information than cojumps themselves. Third, our conclusions are robust based on various robustness tests such as the realized kernel, expanding forecasts, different forecasting windows, different jump tests, and different threshold values. [ABSTRACT FROM AUTHOR]

In this study, we first investigate the impacts of economic policy uncertainty (EPU), monetary policy uncertainty (MPU), and both of them on oil market volatility. We have several noteworthy findings. First, the EPU index can significantly increase the predictive ability compared to benchmark model for the oil market. Second, the high MPU index leads to high fluctuations with respect to oil market, and can remarkably help in forecasting oil volatility. Third, we first find that the MPU and EPU have useful complementary information, and considered both of them together is more powerful to predict oil volatility than separate them. Our conclusions are robust to different forecasting windows, measures and monetary policy uncertainty. • The EPU index can significantly increase the predictive ability compared to benchmark model for the oil market. • The high MPU index leads to high fluctuations with respect to oil market. • The MPU and EPU have useful complementary information. • Considered both of them together is more powerful to predict oil volatility than separate them. • Our conclusions are robust. [ABSTRACT FROM AUTHOR]

We develop a new generalized autoregressive conditional heteroskedasticity (GARCH) model that accounts for the information spillover between two markets. This model is used to detect the usefulness of the CBOE volatility index (VIX) for improving the performance of volatility forecasting and option pricing. We find the significant ability of VIX to predict stock volatility both in‐sample and out‐of‐sample. VIX information also helps to greatly reduce the option pricing error. The proposed volatility spillover GARCH model performs better than the related approaches proposed by Kanniainen et al. (2014, J Bank Finance, 43, pp. 200‐211) and P. Christoffersen et al. (2014, J Financ Quant Anal, 49, pp. 663–697). [ABSTRACT FROM AUTHOR]

This study develops a dairy implied volatility index (DVIX), derived from New Zealand Exchange traded options on whole milk powder (WMP) futures. We document an inverse return–volatility relation which is asymmetric, where increases in WMP futures prices are associated with larger absolute changes in the DVIX than decreases. In sample, the results strongly suggest that the DVIX has a high information content regarding conditional variance and that the inclusion of historical information further improves the predictive power. Out of sample, we find that the DVIX provides substantial information about future realized volatility. We also document that a combination of historical volatility and the DVIX provides the best out‐of‐sample forecasts. [ABSTRACT FROM AUTHOR]