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By constructing a novel index, the oil security attention index, this paper uses the heterogeneous autoregressi (HAR)‐type and its extended models to study whether oil security attention can predict oil volatility. Based on the definition of the different dimensions of oil security and three‐pass regression filter (TPRF) dimension reduction technology, combined with Google search volume data of 23 keywords related to oil security, the oil security attention index is constructed. Considering the potential nonlinear relationship between attention and oil volatility, we incorporate asymmetric effects in the new extended HAR‐type models. The research findings show that the oil security attention index we propose can capture the volatility of West Texas Intermediate. The out‐of‐sample results indicate that the extended models have better predictive power, which confirms the asymmetric relationship between oil security attention and oil volatility. In the robustness analysis, we compare TPRF with traditional principal component analysis (PCA) and partial least squares (PLS), and show that the oil security attention index constructed using TPRF has more favourable information than PCA and PLS to capture the oil security attention of the public. [ABSTRACT FROM AUTHOR]

This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on volatility. Considering day-of-the-week effects, structural breaks, or both, we propose three classes of HAR models to forecast electricity volatility based on existing HAR models. The estimation results of the models showed that day-of-the-week effects only improve the fitting ability of HAR models for electricity volatility forecasting at the daily horizon, whereas structural breaks can improve the in-sample performance of HAR models when forecasting electricity volatility at daily, weekly, and monthly horizons. The out-of-sample analysis indicated that both day-of-the-week effects and structural breaks contain additional ex ante information for predicting electricity volatility, and in most cases, dummy variables used to measure structural breaks contain more out-of-sample predictive information than those used to measure day-of-the-week effects. The out-of-sample results were robust across three different methods. More importantly, we argue that adding dummy variables to measure day-of-the-week effects and structural breaks can improve the performance of most other existing HAR models for volatility forecasting in the electricity market.

We introduce a coupled GARCH model for the intraday and overnight volatility, using the implied jump magnitude from option markets and the earnings calendar to model anticipated shocks. We estimate the model on DJIA and report on the accuracy of the forecasts. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to explore whether the out-of-sample model bias plays an important role in predicting volatility. Design/methodology/approach: Under the heterogeneous autoregressive realized volatility (HAR-RV) framework, we analyze the predictive power of out-of-sample model bias for the realized volatility (RV) of the Dow Jones Industrial Average (DJI) and the S&P 500 (SPX) indices from in-sample and out-of-sample perspectives respectively. Findings: The in-sample results reveal that the prediction model including the model bias can obtain bigger R2, and the out-of-sample empirical results based on several evaluation methods suggest that the prediction model incorporating model bias can improve forecast accuracy for the RV of the DJI and the SPX indices. That is, model bias can enhance the predictability of original HAR family models. Originality/value: The author introduce out-of-sample model bias into HAR family models to enhance model capability in predicting realized volatility. [ABSTRACT FROM AUTHOR]

Forecasting covariance matrices is a difficult task in many research fields since the predicted matrices should be at least positive semidefinite. This problem can be overcome by including constraints in the predictive model or through a parametrization of the matrices to be predicted. In this paper, we focus on the latter approach in a financial application and analyse four parametrizations of the covariance matrices of asset returns. The aim of the manuscript is to understand if the parametrizations of the covariance matrices exhibit differences in terms of predictive accuracy. To this end, we critically analyse their predictive performance through both a Monte Carlo simulation and an empirical application with daily and weekly realized covariance matrices of stock assets. Our findings highlight that the Cholesky decomposition and the parametrization recently introduced by Archakov and Hansen are the overall best-performing methods in terms of forecasting accuracy. [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 study investigates the role of oil futures price information on forecasting the US stock market volatility using the HAR framework. In-sample results indicate that oil futures intraday information is helpful to increase the predictability. Moreover, compared to the benchmark model, the proposed models improve their predictive ability with the help of oil futures realized volatility. In particular, the multivariate HAR model outperforms the univariate model. Accordingly, considering the contemporaneous connection is useful to predict the US stock market volatility. Furthermore, these findings are consistent across a variety of robust checks.

This paper investigates whether leverage effects and structural changes have positive effects on the volatility prediction of crude oil futures. On the basis of existing HAR models, this paper proposes three classes of new HAR models by considering leverage effects, structural changes, or both. The in‐sample and out‐of‐sample results show that leverage effects and structural changes contain significant information for predicting oil volatility. In most cases, structural changes have more in‐sample and out‐of‐sample incremental information than leverage effect, whereas leverage effects have more out‐of‐sample information for predicting 1‐day volatility. In addition, HAR models with leverage effects and structural changes have better in‐sample and out‐of‐sample performances than the corresponding other three classes of HAR models. The above results mean that leverage effects and structural changes should be considered while modelling and forecasting oil volatility. [ABSTRACT FROM AUTHOR]

We investigate whether and to what extent macroeconomic uncertainty predicts the volatility of commodity futures. By examining 26 commodities in six categories, we find that the measure of aggregate macroeconomic uncertainty based on a large dataset has a significant predictive effect for commodity volatility. The predictive relationship holds both in‐sample and out‐of‐sample after controlling for lagged volatility. The extent of the predictability differs by commodity category, with energy, precious metals, and industrial metals futures having the most significant effect. For all commodities, the predictive power of macroeconomic uncertainty is stronger in more recent data and during recessions. [ABSTRACT FROM AUTHOR]

We use heterogeneous autoregression (HAR) and two related HAR extension models to examine volatility forecasting performances before and after the launch of night trading sessions in the Shanghai Futures Exchange (SHFE) gold futures market. To capture fluctuations from external information and volatility of realized volatility (RV), we incorporate the trading volume and jumping into the HAR‐V‐J model in the first place and then incorporate a GARCH specification into the HAR‐GARCH model. Results showed that there were large fluctuations in SHFE gold futures market before the launch of night trading sessions and mostly stemmed from overnight fluctuation in the international gold futures market. After the launch of night trading sessions, the realized volatility has a clear trend of moderation. In the in‐sample estimation, both jump and external information are found to have significant explanatory power with the HAR‐V‐J model. Additionally, the volatility clustering and high persistence of the realized volatility were confirmed by the GARCH coefficients. Last but not the least, night trading sessions have significantly improved the out‐of‐sample forecasting performances of realized volatility models. Among them, the HAR‐V‐J model is the best‐performing model. This conclusion holds for various prediction horizons and has great practical values for investors and policymakers. [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]

This paper studies the volatility of Bitcoin and determines the importance of jumps and structural breaks in forecasting volatility. We show the importance of the decomposition of realized variance in the in‐sample regressions using 18 competing heterogeneous autoregressive (HAR) models. In the out‐of‐sample setting, we find that the HARQ‐F‐J model is the superior model, indicating the importance of the temporal variation and squared jump components at different time horizons. We also show that HAR models with structural breaks outperform models without structural breaks across all forecasting horizons. Our results are robust to an alternative jump estimator and estimation method. [ABSTRACT FROM AUTHOR]

This study investigates the impact of uncertainty on the volatility forecasting power of option‐implied volatility. Option‐implied volatility is a powerful predictor of future volatility, particularly during periods of high uncertainty. This is consistent with option‐implied volatility being largely determined by volatility‐informed traders (rather than directional traders) when uncertainty is high. New volatility forecasting models that incorporate such interaction outperform benchmark models, both in‐ and out‐of‐sample. The new models also better predict future volatility during the 2008 global financial crisis, for which benchmark models perform poorly. The results are robust to alternative choices of benchmark models, loss functions, and estimation windows. [ABSTRACT FROM AUTHOR]

This paper examines whether the equity market uncertainty (EMU) index contains incremental information for forecasting the realized volatility of crude oil futures. We use 5-min high-frequency transaction data for WTI crude oil futures and develop six heterogeneous autoregressive (HAR) models based on classical HAR-type models. The empirical results suggest that EMU contains more incremental information than the economic policy uncertainty (EPU) for forecasting the realized volatility of crude oil futures. More importantly, we argue that EMU is a non negligible additional predictive variable that can significantly improve the 1-day ahead predictive accuracy of all six HAR-type models, and improve the 1-week ahead forecasting performance of the HAR-RV, HAR-RV-J, HAR-RSV, HAR-RV-SJ models. These findings highlight a strong short-term and a weak mid-term predictive ability of EMU in the crude oil futures market. [ABSTRACT FROM AUTHOR]

This paper explores the volatility forecasting implications of a model in which the high-frequency market microstructure noise is related to the true underlying volatility. The contribution of this paper is to propose a theoretical framework under which the realized variance, based on the highest frequency to compute returns, may improve volatility forecasting if the noise variance is an affine function of the fundamental volatility. In this new setting, we extend the work of Andersen et al. (2011) and quantify the predictive ability of several measures of integrated variance. We find that the traditional realized variance based on the highest frequency returns outperforms alternative realized measures. We also evaluate the usefulness of our approach by conducting an empirical application and show several improvements resulting from the assumption of time-varying noise variance. [ABSTRACT FROM AUTHOR]

• By considering information from China, we forecast gold futures volatility in China particularly based on intraday regressions. • RV in the rest of the day (from previous market close to the last half an hour) can strongly predict the RV in the last half an hour before the market close. • And the models which use the information in intraday trading time intervals separately show better model forecasting performance. • Including RVs during night trading time into our basic models based on day information enhance the forecasting performance. • We reconfirm the predictive power of US information in gold futures market from the intraday perspective. This study investigates the intraday realized volatility (RV) forecasting of gold futures in China. To predict the RV in the last half an hour before the day trading close (LH), we decompose the whole trading period into several intervals. The empirical results show that gold futures RVs in day trading intervals can predict the RV in LH, with RV in ROD interval (from night trading close to the last half an hour before day trading close) having stronger predictive power. Models which use gold futures RVs in separate partitions during ROD deliver better forecasting performance in and out of sample. Meanwhile, gold futures RVs in night trading are also informative even after controlling the RVs in day trading. Further, we reconfirm the predictive power of US gold futures information from the intraday perspective. [ABSTRACT FROM AUTHOR]

This paper introduces a parsimonious and yet flexible semiparametric model to forecast financial volatility. The new model extends a related linear nonnegative autoregressive model previously used in the volatility literature by way of a power transformation. It is semiparametric in the sense that the distributional and functional form of its error component is partially unspecified. The statistical properties of the model are discussed and a novel estimation method is proposed. Simulation studies validate the new method and suggest that it works reasonably well in finite samples. The out-of-sample forecasting performance of the proposed model is evaluated against a number of standard models, using data on S&P 500 monthly realized volatilities. Some commonly used loss functions are employed to evaluate the predictive accuracy of the alternative models. It is found that the new model generally generates highly competitive forecasts. [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 article, we account for the first time for long memory, regime switching and the conditional time-varying volatility of volatility (heteroscedasticity) to model and forecast market volatility using the heterogeneous autoregressive model of realized volatility (HAR-RV) and its extensions. We present several interesting and notable findings. First, existing models exhibit significant nonlinearity and clustering, which provide empirical evidence on the benefit of introducing regime switching and heteroscedasticity. Second, out-of-sample results indicate that combining regime switching and heteroscedasticity can substantially improve predictive power from a statistical viewpoint. More specifically, our proposed models generally exhibit higher forecasting accuracy. Third, these results are widely consistent across a variety of robustness tests such as different forecasting windows, forecasting models, realized measures, and stock markets. Consequently, this study sheds new light on forecasting future volatility. [ABSTRACT FROM AUTHOR]

Abstract Modeling volatility with reasonable accuracy is essential in asset allocation, asset pricing, and risk management. In this paper we use the ensemble empirical mode decomposition method and Zhang et al. (2008, 2009)'s method to decompose realized volatility into different volatility components. Then, we propose two new heterogeneous autoregressive (HAR) models by combining with the volatility components and leverage effect. Finally, we use high-frequency data for the S&P 500 as the study sample and perform parameter estimations on eight HAR-type models (including two new models). The results indicate that our models that are used to model 1-day, 1-week and 1-month future volatilities have an advantage over other existing HAR-type models. This advantage is substantial in the case of 1-month future volatility. In addition, the leverage contains significant in-sample prediction information for future volatility. Highlights • We propose two new heterogeneous autoregressive models. • Our models exhibit better performance than other heterogeneous autoregressive models. • The low-frequency volatility contains much predictive information for future volatility. • The leverage contains much in-sample prediction information for future volatility. [ABSTRACT FROM AUTHOR]

One of the main applications of conditional volatility modeling and forecasting of financial assets is the value-at-risk (VaR) estimation that is used by financial institutions for reporting the daily capital in risk. It remains a question on whether realized volatility (RV) models that incorporate the use of intraday data produce better VaR forecasts compared to methodologies that are based solely on daily returns. This study provides extensive comparison of out-of-sample volatility and VaR forecast performance on three equity market indices: S&P500, FTSE100, and DAX30 using 13 risk models that consist of 5 GARCH specifications, 4 ARFIMAX specifications and 4 HARX specifications. The out-of-sample volatility forecasts are evaluated by various loss functions and simple scoring procedures in order to identity the model that produces the overall best volatility forecasts. For VaR forecasts, the models are evaluated using a two-stage backtesting procedure where the models undergo unconditional and conditional coverage tests to eliminate underperforming models and the qualified models are then evaluated using the quadratic probability score (QPS) function that is computed based on various VaR loss functions. The results showed that RV models outperform GARCH models for volatility forecasts, but a simple EGARCH model outperforms the rest models for most of the VaR forecasts. The results also indicated that capturing the asymmetric behavior of volatility dynamics is essential for accurate volatility and VaR forecasts. The findings of this study provide useful information for market risk regulation, financial risk management and further investigations such as extension to derivative markets and options pricing.

In this paper, we use a comprehensive look to investigate whether the G7 stock markets can contain predictive information to help in forecasting the Chinese stock market volatility. Our out-of-sample empirical results indicate the kitchen sink (HAR-RV-SK) model is able to attain better performance than the benchmark model (HAR-RV) and other models, implying that the G7 stock markets can help in predicting the one-day volatility of the Chinese stock market. Moreover, the kitchen sink strategy can beat the strategy of the simple combination forecasts. Finally, the G7 stock markets can indeed contain useful information, which can increase the accuracy forecasts of the Chinese stock market. [ABSTRACT FROM AUTHOR]

In this research, we first investigate whether economic policy uncertainty (EPU) index can increase the HAR-RV-type models' forecast accuracy. In addition, we explore how EPU index can be effectively used to gain larger economic values in the oil futures market. To this end, this research provides a new perspective on setting thresholds for EPU and examines whether these thresholds can help improve both the forecast accuracy and economic values. Empirical results suggest that the HAR-RV-type models including EPU can generate more accurate forecasts and economic values. The HAR-RV-type models including above-threshold EPU can further improve the forecast accuracy and yield higher economic values by setting specific thresholds for a range of horizons. The findings highlight the importance of EPU and effective way of using EPU in risk management and portfolio strategies that is crucial for investors and policymakers. [ABSTRACT FROM AUTHOR]

In this study, we first investigate the impacts of leverage effect and economic policy uncertainty (EPU) on future volatility in the framework of regime switching. Out-of-sample results show that the HAR-RV including the leverage effect and economic policy uncertainty with regimes can achieve higher forecast accuracy than RV-type and GARCH-class models. Our robustness results further imply that these factors in the framework of regime switching can substantially improve the HAR-RV’s forecast performance. [ABSTRACT FROM AUTHOR]

Chinese stock index futures experienced an unusual bull and bear markets around 2015, but its volatility dynamic is a mystery for investors and regulators. Modeling and forecasting volatility is a feasible way to reveal volatility transmission process. In this paper, we establish 4 HAR-type models involving jumps, realized semivariances, signed jumps and Baidu Index, to forecast the realized volatility of CSI 300 index futures. Based on the framework of HAR modeling, four novel HAR-type models are proposed by adding Baidu Index as independent variable. During the modeling process, two decompositions of realized volatility including continuous and jump variances, upside and downside realized semivariances are considered. To reduce the robustness of market microstructure noise, optimal sampling frequency for calculating realized volatilities is determined by sequential correlation approach, the statistic Zmed of ADS jump test, realized semivariances and signed jump are revised based on realized kernel estimator. The newly MCS test is employed to evaluate the out-of-sample forecast performances. In-sample and out-of-sample analysis of forecast models are carried out on CSI 300 index futures, which shows important conclusions:1) Most of the predictable variation in realized volatility stems from continuous volatility rather than jump variance, and future realized volatility is more related to historical downside semivariances (bad volatility) than upside semivariances (good volatility); 2) good volatility and bad volatility exhibit asymmetric impact effect that good (bad) volatility generate negative (positive) impact on future realized volatility; 3) Decomposition of upside and downside realized semivariances outperforms that of continuous and jump variances; 4) Baidu index can significantly improve the forecasting performances of HAR-type models both in-sample and out-of-sample testing; 5) Signed jumps bear valuable information of both market volatility and directions, and HAR-RV-SJ-BI is the best model among all forecast models specified in our paper. Our findings have import implications for investors and policymakers of Chinese stock index futures. [ABSTRACT FROM AUTHOR]

No-constant strategy is considered for the heterogenous autoregressive (HAR) model of Corsi, which is motivated by smaller biases of its estimated HAR coefficients than those of the constant HAR model. The no-constant model produces better forecasts than the constant model for four real datasets of the realized volatilities (RVs) of some major assets. Robustness of forecast improvement is verified for other functions of realized variance and log RV and for the extended datasets of all 20 RVs of Oxford-Man realized library. A Monte Carlo simulation also reveals improved forecasts for some historic HAR model estimated by Corsi. [ABSTRACT FROM AUTHOR]

Weekends and holidays lead to gaps in daily financial data. Standard models ignore these irregularities. Because this issue is particularly important for persistent time series, we focus on volatility modelling, specifically modelling of realized volatility. We suggest a simple way of adjusting volatility models, which we illustrate on an AR(1) model and the HAR model of Corsi (2009). We investigate daily series of realized volatilities for 21 equity indices around the world, covering more than 15 years, and we find that our extension improves the volatility models—both in sample and out of sample. For HAR models and for consecutive trading days, the mean squared error decreased by 2.34% in average and for the QLIKE loss function by 1.41%. [ABSTRACT FROM AUTHOR]

• We investigate range estimators for forecasting realized volatility. • We apply rolling window forecasts for the G7 markets. • Using QLIKE, HMSE and MCS evaluation techniques. • Results suggest no single range model outperforms. • Nonetheless, results are generally supportive of simpler range estimators. This paper investigates whether range estimators contain important information in forecasting future realized volatility. We use widely applied range-based estimators: Parkinson, Garman-Klass, Roger-Satchell, and Yang-Zhang within a HAR-RV-X framework. Overnight volatility and close-to-close volatility estimators are also included, and the forecasting exercise is applied to G7 stock markets using a rolling window. Using QLIKE, HMSE and MCS forecast criteria, several noteworthy points are reported. The overall findings suggest that while no single model dominates, overnight return volatility achieves the most consistent performance. For example, HAR-RV model forecasts for CAC and DAX indices are improved only by overnight volatility, with some evidence also for SPX. For other indices, forecasts are improved by Parkinson and/or Garman-Klass volatility estimators. Of note, simpler range estimators outperform more complex range estimators. The findings could be important for investors in managing portfolio risk. [ABSTRACT FROM AUTHOR]

This paper uses high-frequency continuous intraday electricity price data from the EPEX market to estimate and forecast realized volatility. Three different jump tests are used to break down the variation into jump and continuous components using quadratic variation theory. Several heterogeneous autoregressive models are then estimated for the logarithmic and standard deviation transformations. Generalized autoregressive conditional heteroskedasticity (GARCH) structures are included in the error terms of the models when evidence of conditional heteroskedasticity is found. Model selection is based on various out-of-sample criteria. Results show that decomposition of realized volatility is important for forecasting and that the decision whether to include GARCH-type innovations might depend on the transformation selected. Finally, results are sensitive to the jump test used in the case of the standard deviation transformation. [ABSTRACT FROM AUTHOR]

We discuss several multivariate extensions of the Multiplicative Error Model to take into account dynamic interdependence and contemporaneously correlated innovations (vector MEM or vMEM). We suggest copula functions to link Gamma marginals of the innovations, in a specification where past values and conditional expectations of the variables can be simultaneously estimated. Results with realized volatility, volumes and number of trades of the JNJ stock show that significantly superior realized volatility forecasts are delivered with a fully interdependent vMEM relative to a single equation. Alternatives involving log-Normal or semiparametric formulations produce substantially equivalent results. [ABSTRACT FROM AUTHOR]

This paper investigates the information content of the CBOE Gold ETF Volatility Index (GVZ) and jumps in forecasting realized volatility of the Shanghai gold futures market. We find strong in-sample evidence that the GVZ and jumps are significant and both greatly improve next day volatility forecasts. Also, these results are robust when the recent financial crisis is considered. Further, out-of-sample analysis confirms that the GVZ and jumps are important factors in forecasting future volatility. More important, we show that the GVZ outperforms jumps in terms of forecasting performance. [ABSTRACT FROM AUTHOR]

• Introduce the hybrid model CEEMDAN-LSTM to forecast RV of stock price index. • The MCS test is adopted as evaluation criterion of forecast performance. • Hybrid models with CEEMDAN outperform their corresponding single models. • CEEMDAN-LSTM performs the best in both emerging and developed markets. The realized volatility (RV) financial time series is non-linear, volatile, and noisy. It is not easy to accurately forecast RV with a single forecasting model. This paper adopts a hybrid model integrating Long Short-Term Memory (LSTM) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to forecast the RV of CSI300, S&P500, and STOXX50 indices. After the empirical study, four loss functions MSE, MAE, HMSE, HMAE, and the model confidence set (MCS) test are taken as the evaluation criteria. This paper selected Back Propagation Neural Networks (BP), Elman Neural Networks (Elman), Support Vector Regression Machine (SVR), autoregression (AR), heterogeneous autoregressive (HAR), and their hybrid models with CEEMDAN as the comparison. The test results show that CEEMDAN-LSTM has the best performance in forecasting RV in emerging and developed markets. Besides, the performance of single models is inferior to their corresponding hybrid models with CEEMDAN. And the empirical results are robust with the "sliding window" approach. [ABSTRACT FROM AUTHOR]

Given the increasing share of wind energy in the portfolio of energy sources, there is the need for a more thorough understanding of its uncertainties due to changing weather conditions. To account for the uncertainty in predicting wind power production, this article examines the volatility forecasting abilities of different GARCH-type models for wind power production. Moreover, due to characteristic features of the wind power process, such as heteroscedasticity and nonlinearity, we also investigate the use of a Markov regime-switching GARCH (MRS-GARCH) model on forecasting volatility of wind power. Realized volatility, which is derived from lower-scale data, serves as a benchmark for latent volatility. We find that the MRS-GARCH model significantly outperforms traditional GARCH models in predicting the volatility of wind power, while the exponential GARCH model is superior among traditional GARCH models. [ABSTRACT FROM AUTHOR]

The goal of this paper is twofold. First, using five of the most actively traded stocks in the Brazilian financial market, this paper shows that the normality assumption commonly used in the risk management area to describe the distributions of returns standardized by volatilities is not compatible with volatilities estimated by EWMA or GARCH models. In sharp contrast, when the information contained in high frequency data is used to construct the realized volatility measures, we attain the normality of the standardized returns, giving promise of improvements in Value-at-Risk statistics. We also describe the distributions of volatilities of the Brazilian stocks, showing that they are nearly lognormal. Second, we estimate a simple model of the log of realized volatilities that differs from the ones in other studies. The main difference is that we do not find evidence of long memory. The estimated model is compared with commonly used alternatives in out-of-sample forecasting experiment.

• An effective way to forecast stock volatility by selecting dynamic thresholds of the VIX is explored. • Selecting thresholds for the VIX can significantly improve the forecast accuracy. • The above-threshold VIX has a better forecasting performance during expansions. In this paper, an effective way to forecast stock volatility by selecting dynamic thresholds of the VIX is explored. We examine the predictability of the VIX and its above-threshold values for the S&P 500. Our results indicate that selecting thresholds for the VIX can significantly improve the forecast accuracy. From the out-of-sample R 2 statistics, we find that the above-threshold VIX has a better forecasting performance during expansions. [ABSTRACT FROM AUTHOR]

This article examines the forecasting performance of two-scale realized volatility (TSRV) measure in comparison to that of the conventional sparsely sampled realized volatility (SSRV) measure, using selected volatility forecasting models. There is evidence that the forecasts based on TSRV are more efficient and less biased than those based on SSRV, for all the forecasting models employed. This implies that the quality of forecast predominantly depends on the quality of estimate, and not on the forecasting model. With TSRV estimates, the exponentially weighted moving average models for daily forecasts, and the random walk model for weekly and monthly forecasts, marginally dominate the other models on efficiency and bias criteria. [ABSTRACT FROM AUTHOR]