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Using the generalized dynamic factor model, this study constructs three predictors of crude oil price volatility: a fundamental (physical) predictor, a financial predictor, and a macroeconomic uncertainty predictor. Moreover, an event‐triggered predictor is constructed using data extracted from Google Trends. We construct GARCH‐MIDAS (generalized autoregressive conditional heteroskedasticity–mixed‐data sampling) models combining realized volatility with the predictors to predict oil price volatility at different forecasting horizons. We then identify the predictive power of the realized volatility and the predictors by the model confidence set (MCS) test. The findings show that, among the four indexes, the financial predictor has the most predictive power for crude oil volatility, which provides strong evidence that financialization has been the key determinant of crude oil price behavior since the 2008 global financial crisis. In addition, the fundamental predictor, followed by the financial predictor, effectively forecasts crude oil price volatility in the long‐run forecasting horizons. Our findings indicate that the different predictors can provide distinct predictive information at the different horizons given the specific market situation. These findings have useful implications for market traders in terms of managing crude oil price risk. [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]

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 view of the growing concern of Chinese stock fluctuation, this paper forecasts the Chinese stock volatility by extracting global stock information by combining forecasts of time-varying parameter (TVP) volatility models. First, we construct individual constant coefficient (CC) models and TVP models across 27 global stock markets, and then use several strategies to combine their forecasts. The results show global stock information does forecast the future volatility of Chinese stock market. Both the forecast accuracy and economic values can be further improved by using strategies combining TVP models with global stock information. Specifically, the median combination shows its superiority for the volatile Chinese stock market. Our findings are robust to different estimation window sizes, volatility proxies, and evaluation criteria. • We forecast the Chinese stock volatility across twenty-seven global stock markets. • Combinations of TVP models can significantly outperform those of CC models. • The median combination of TVP models is the best for the Chinese stock market. • Combinations of TVP models can generate sizeable economic gains. [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]

Summary We investigate the predictability of several range‐based stock volatility estimates and compare them with the standard close‐to‐close estimate, which is most commonly acknowledged as the volatility. The patterns of volatility changes are analysed using long short‐term memory recurrent neural networks, which are a state‐of‐the‐art method of sequence learning. We implement the analysis on all current constituents of the Dow Jones Industrial Average index and report averaged evaluation results. We find that the direction of changes in the values of range‐based estimates are more predictable than that of the estimate from daily closing values only. [ABSTRACT FROM AUTHOR]

We compare forecasts of the volatility of the Australian dollar exchange rate to alternative measures of ex post volatility. We develop and apply a simple test for the improvement in the ability of loss functions to distinguish between forecasts when the quality of a volatility estimator is increased. We find that both realized variance and the daily high–low range provide a significant improvement in loss function convergence relative to squared returns. We find that a model of stochastic volatility provides the best forecasts for models that use daily data, and the GARCH(1,1) model provides the best forecast using high‐frequency data. [ABSTRACT FROM AUTHOR]

A vector error correction model is proposed for forecasting realized volatility which takes advantage of the cointegration relation between realized volatility and implied volatility. The model is constructed by adding a cointegration error term to a vector-and-unit-root version of the heterogeneous autoregressive (HAR) model of Corsi (2009). The proposed model is easier to implement, extend, and interpret than fractional cointegration models. A Monte Carlo simulation and real data analysis reveal advantages of the proposed model over other existing models of Corsi (2009), Busch Christensen and Nielsen (2011), Cho and Shin (2016), and Bollerslev Patton, and Quaedvlieg (2016). [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]

This study proposes a threshold realized generalized autoregressive conditional heteroscedastic (GARCH) model that jointly models daily returns and realized volatility, thereby taking into account the bias and asymmetry of realized volatility. We incorporate this threshold realized GARCH model with skew Student‐t innovations as the observation equation, view this model as a sharp transition model, and treat the realized volatility as a proxy for volatility under this nonlinear structure. Through the Bayesian Markov chain Monte Carlo method, the model can jointly estimate the parameters in the return equation, the volatility equation, and the measurement equation. As an illustration, we conduct a simulation study and apply the proposed method to the US and Japan stock markets. Based on quantile forecasting and volatility estimation, we find that the threshold heteroskedastic framework with realized volatility successfully models the asymmetric dynamic structure. We also investigate the predictive ability of volatility by comparing the proposed model with the traditional GARCH model as well as some popular asymmetric GARCH and realized GARCH models. This threshold realized GARCH model with skew Student‐t innovations outperforms the competing risk models in out‐of‐sample volatility and Value‐at‐Risk forecasting. [ABSTRACT FROM AUTHOR]

In this article, we discuss whether price limit hits (PLH) contain information for volatility forecasting. Using Chinese stock market as sample, we find that PLH display significant forecasting power for future volatilities. Furthermore, the predictive effects on volatility are asymmetric between upper price limit hits (UPLH) and lower price limit hits (LPLH), with more pronounced effect for LPLH. These results are robust after controlling for jump, leverage effect, and volume in HAR-RV models, and they hold in crisis sub-sample and other measures of PLH. Finally, we provide a possible explanation for the predictive ability of PLH and suggest that the number of PLH can be used as a proxy for investor sentiment. [ABSTRACT FROM AUTHOR]

In order to improve the forecasting accuracy of the volatilities of the markets, we propose the hybrid models based on artificial neural networks with multi-hidden layers in this paper. Specifically, the hybrid models are built using the estimated volatilities obtained from GARCH family models and Google domestic trends (GDTs) as input variables. We further carry out many experiments varying the number of layers and activation functions to obtain the accurate hybrid model for forecasting volatility. The proposed models are applied to forecast weekly and monthly volatilities of S&P 500 index to verify their accuracy. The performance comparison results show that the hybrid models with GDTs outperform clearly the predicted results with GARCH family models and the hybrid models without GDTs in forecasting the volatility of actual market. We also provide the experiment results with graphs to illustrate the efficiency of models. [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]

Abstract Accurately predicting volatility plays an important role in many financial interactions and has already attracted considerable attention from both academics and practitioners. Much effort has been devoted to looking for key determinants of volatility and developing more powerful models. In this paper, we investigate the influences of high frequency event impact and low frequency macroeconomic fundamentals on volatility. To handle the mixed frequency data, we develop a modified GARCH-MIDAS model, multiple factors GARCH-MIDAS (MF-GARCH-MIDAS), to allow for a long-run component driven by multiple factors sampled at different frequencies. We present the technical details of the MF-GARCH-MIDAS model and apply it to predict monthly volatility of the US DJIA. In our empirical illustration, we consider weekly Google trends (GT) as a proxy for event impact and use three US macroeconomic variables: quarterly GDP, monthly PPI, and monthly IP. Our empirical findings support that event impact measured by GT is also a source of volatility besides macroeconomic fundamentals. The economic implications are significant in that both event impact and macroeconomic fundamentals really matter for volatility forecasting and the combination GT+PPI+GDP in M2 performs best in predicting volatility of DJIA. Highlights • We develop a novel multiple factors GARCH-MIDAS (MF-GARCH-MIDAS) model. • It allows for a long-run volatility driven by multiple factors sampled at different frequencies. • It is used to investigate the usefulness of Google trends in predicting stock market volatility. • The empirical results show favorable evidence about our proposed model. • We find that Google trends really matters for volatility forecasting. [ABSTRACT FROM AUTHOR]

Abstract This study examines the volatility forecasting performance of various historical and implied volatility measures. We compare the informational efficiency of lagged realized volatility, GARCH-family volatilities, out-of-the-money (OTM) and at-the-money (ATM) implied volatilities, and the market volatility index (VKOSPI) using univariate and encompassing regression analyses. We find that historical and implied volatility both have good predictive ability, but are biased estimators of future volatility. Furthermore, the information content of the implied volatility constructed from slightly OTM options encompasses that of the deep OTM and ATM options. In general, the VKOSPI exhibits the best forecasting performance among the volatility measures analyzed in this study. However, incorporating GJR–GARCH volatility, which exhibits the best performance among the GARCH-family volatilities, in the prediction model possibly improves the explanatory power of the VKOSPI. Highlights • We compare the forecasting abilities of various volatility measures. • Both historical and implied volatilities contain predictive information. • The implied volatility of the slightly out-of-the-money options performs well. • VKOSPI exhibits the superior forecasting performance. [ABSTRACT FROM AUTHOR]

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An implied assumption in the asymmetric conditional autoregressive range (ACARR) model is that upward range is independent of downward range. This paper scrutinizes this assumption on a broad variety of stock indices. Instead of independence, we find significant cross‐interdependence between the upward range and the downward range. Regression test shows that the cross‐interdependence cannot be explained by leverage effect. To include the cross‐interdependence, a feedback asymmetric conditional autoregressive range (FACARR) model is proposed. Empirical studies are performed on a variety of stock indices, and the results show that the FACARR model outperforms the ACARR model with high significance for both in‐sample and out‐of‐sample forecasting. [ABSTRACT FROM AUTHOR]

Abstract In this study, we first investigate that whether the US EPU index can contain useful predictive information to help in forecasting European stock markets. Using the out-of-sample forecasts, we can obtain several noteworthy findings. First, the EPU index of European countries seems not to significantly increase the forecasts accuracy of these stock markets. Second, we determine that this model including the US EPU index can achieve better forecasting performance, strongly supports that it contains useful predictive information with respect to the European stock markets. Third, based on the US expansions and recessions, we find that the US EPU index can provide more useful forecasting information and can substantially increase the predictive ability for the European stock markets during the recessions than during the expansions. Highlights • We investigate the impacts of US EPU index on forecasting European stock markets volatility. • The of European own EPU index seems not to significantly increase the forecasts accuracy. • This model including the US EPU index can achieve better forecasting performance. • The US EPU index can provide more useful forecasting information during the recessions than during the expansions. [ABSTRACT FROM AUTHOR]

This paper discusses mixture periodic GARCH (M-PGARCH) models that constitute very flexible class of nonlinear time series models of the conditional variance. It turns out that they are more parsimonious comparatively to MPARCH models. We first provide some probabilistic properties of this class of models. We thus propose an estimation method based on the expectation-maximization algorithm. Finally, we apply this methodology to model the spot rates of the Algerian dinar against euro and US dollar. This empirical analysis shows that M-PGARCH models yield the best performance among the competing models. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to evaluate the forecasting performance of linear and non-linear generalized autoregressive conditional heteroskedasticity (GARCH)-class models in terms of their in-sample and out-of-sample forecasting accuracy for the Tadawul All Share Index (TASI) and the Tadawul Industrial Petrochemical Industries Share Index (TIPISI) for petrochemical industries. We use the daily price data of the TASI and the TIPISI for the period of 10 September 2007 to 26 February 2015. The results suggest that the Asymmetric Power of ARCH (APARCH) model is the most accurate model in the GARCH class for forecasting the volatility of both the TASI and the TIPISI in the context of petrochemical industries, as this model outperforms the other models in model estimation and daily out-of-sample volatility forecasting of the two indices. This study is useful for the dataset examined, because the results provide a basis for traders, policy-makers, and international investors to make decisions using this model to forecast the risks associated with investing in the Saudi stock market, within certain limitations. [ABSTRACT FROM AUTHOR]

Given the complex characteristics of crude oil price volatility, a new hybrid forecasting method based on the hidden Markov, exponential generalized autoregressive conditional heteroskedasticity, and least squares support vector machine models is proposed, and the forecasting performance of the new method is compared with that of well‐recognized generalized autoregressive conditional heteroskedasticity class and other related forecasting methods. The results indicate that the new hybrid forecasting method can significantly improve forecasting accuracy of crude oil price volatility. Furthermore, the new method has been demonstrated to be more accurate for the forecast of crude oil price volatility particularly in a longer time horizon. [ABSTRACT FROM AUTHOR]

The measurement of volatility is of fundamental importance in finance. Standard market practice adopted for volatility estimation from option prices leads to a considerable loss of information and the introduction of an element of arbitrariness in the volatility index computation. We propose to adopt fuzzy regression methods in order to include all the available information from option prices, and to obtain an informative volatility index. In fact, the obtained fuzzy volatility indices not only offer a most possible value, but also a lower and an upper bound for the interval of possible values, providing investors with an additional source of information. We also propose a defuzzification procedure to select a representative value within this interval. Moreover, we investigate the occurrence of truncation and discretization errors in volatility index computation by adopting an interpolation-extrapolation method. We also test the forecasting power of each volatility index on future realized volatility. [ABSTRACT FROM AUTHOR]

Using the Arrow–Debreu state‐contingent pricing methodology, this paper derives a U.S. government bond market volatility index (GBVX). I show that GBVX is an unbiased predictor for the next 30 day realised volatility of the Treasury note futures return. GBVX also subsumes the information of GARCH, EWMA and historical volatility measures. Furthermore, GBVX serves as an effective predictor for the future realised volatilities of a wide class of fixed income portfolios. The results suggest GBVX as a powerful instrument for volatility forecasting in the fixed income markets. [ABSTRACT FROM AUTHOR]

This article extends the HAR‐CJN model proposed by Andersen, Bollerslev, and Huang (Journal of Econometrics, 2011, 160, 176–189) and explores the role of overnight information and leverage effects in improving volatility forecasting. To explore the interaction between different components of daily volatility, this paper attempts to separately model the dynamics of continuous variation, the discontinuous jump, and the overnight return variance by including leverage effects. The findings show that lagged continuous and discontinuous jump variations generate significant impacts on future continuous segments, discontinuous jump segments, and the overnight return variance. Furthermore, in addition to the usual leverage effects, additional leverage effects with respect to overnight returns are found to play a significant role in volatility forecasting. Finally, out‐of‐sample forecasts are investigated; the results show that the new HAR‐CJN model can describe and predict daily volatility more accurately than other HAR models. [ABSTRACT FROM AUTHOR]

• This paper mainly investigates the impacts of the high-low estimator on the Chinese oil futures volatility. • The high-low estimator constructed based on daily prices contains useful information. • Adding regime switching to the models is helpful to improve forecasting accuracy. This paper mainly investigates whether the high-low estimator has valuable information to predict the Chinese oil futures volatility. The results show that the high-low estimator constructed based on daily prices contains useful information to predict the Chinese oil futures volatility. Moreover, adding regime switching to the models is helpful to improve forecasting accuracy, especially combining regime switching and the high-low estimator. This paper tries to provide new evidence for Chinese oil futures volatility prediction. [ABSTRACT FROM AUTHOR]

This paper proposes the Real-Time GARCH-MIDAS model to model and forecast volatility. An empirical application to the Shanghai Stock Exchange Composite Index (SSEC) and Shenzhen Stock Exchange Component Index (SZSEC) of China shows that the Real-Time GARCH-MIDAS model outperforms competing models in terms of both empirical return fitting and out-of-sample volatility forecasting. Moreover, the superior forecasting performance of the Real-Time GARCH-MIDAS model is robust to alternative rolling windows, alternative benchmark models, alternative MIDAS lags and alternative volatility proxy. Further discussion illustrates the flexibility of the Real-Time GARCH-MIDAS model. • The Real-Time GARCH-MIDAS model is proposed to model and forecast volatility. • The model is able to account for the high persistence of volatility. • The model can accommodate the current information and volatility of volatility. • The model outperforms competing models in out-of-sample volatility forecasting. • The robustness of the superior forecasting performance of the model is examined. • Further discussion illustrates the flexibility of the model. [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.

This article aims to study the schemes of forecasting the volatilities of Chinese futures markets and sector stocks. An improved method based on the cyclical two-component model (CTCM) introduced by Harris et al. in 2011 is provided. The performance of CTCM is compared with the benchmark model: Heterogeneous Autoregressive model of Realized Volatility type (HAR-RV type). The impact of open interest for futures market is included in HAR-RV type model. We employ 3 different evaluation rules to determine the most efficient models when the results of different evaluation rules are inconsistent. The empirical results show that CTCM is more accurate than HAR-RV type in both estimation and forecasting. The results also show that the realized range-based tripower volatility (RTV) is the most efficient estimator for both Chinese futures markets and sector stocks.

In this paper, we study the volatility forecasts in the Bitcoin market, which has become popular in the global market in recent years. Since the volatility forecasts help trading decisions of traders who want a profit, the volatility forecasting is an important task in the market. For the improvement of the forecasting accuracy of Bitcoin’s volatility, we develop the hybrid forecasting models combining the GARCH family models with the machine learning (ML) approach. Specifically, we adopt Artificial Neural Network (ANN) and Higher Order Neural Network (HONN) for the ML approach and construct the hybrid models using the outputs of the GARCH models and several relevant variables as input variables. We carry out many experiments based on the proposed models and compare the forecasting accuracy of the models. In addition, we provide the Model Confidence Set (MCS) test to find statistically the best model. The results show that the hybrid models based on HONN provide more accurate forecasts than the other models.

This paper investigates the dynamics of stock price volatility for different vessel-type segments of the U.S, water transportation industry. We measure market exposure by a portfolio of tanker, dry bulk, container, and gas stocks to examine tail behavior and tail risk dependence. The role of mixture distributions in predicting future volatility is studied from both statistical and economic perspectives. We further test for predictability in co-movements in the tails of sectors returns. Findings indicate that large losses are strongly correlated, supporting asymmetric transmission processes for financial contagion. Finally, using a non-parametric approach, we extend the model to the multivariate case and assess the value of volatility and correlation timing in optimal portfolio selection. The results can help to improve the understanding of time-varying volatility, correlation and tail systemic risk of shipping stock markets, and consequently, have implications for risk management and asset allocation practices, as well as regulatory policies. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of global economic policy uncertainty (GEPU) on the volatility of the Chinese stock market and whether GEPU has predictive power for the volatility of the Chinese stock market. We apply the generalized autoregressive conditional heteroscedastic mixed data sampling (GARCH–MIDAS) model to evaluate the impact of the monthly GEPU index on the daily Shanghai Composite index. Our empirical results show that GEPU has a positive and significant influence on the volatility of the Chinese stock market, which reflects that Chinese stock market has been gradually integrated into the global economy. In addition, the forecasts generated by the GARCH–MIDAS model with GEPU and RV (Realized Volatility) produce substantially smaller errors than the GARCH (1,1) model and the GARCH–MIDAS model with RV (measured by RMSE, RMAE, RMSD, RMAD loss functions and DM test), which confirms the important role of GEPU for predicting the volatility of the Chinese stock market. Overall, our findings imply that GEPU is an additional driving factor of long-term volatility in the Chinese stock market. [ABSTRACT FROM AUTHOR]

We forecast the realized volatility of crude oil futures market using the heterogeneous autoregressive model for realized volatility and its various extensions. Out-of-sample findings indicate that the inclusion of jumps does not improve the forecasting accuracy of the volatility models, whereas the “leverage effect” pertaining to the difference between positive and negative realized semi-variances can significantly improve the forecasting accuracy in predicting the short- and medium-term volatility. However, the signed jump variations and its decomposition couldn’t significantly enhance the models’ forecasting accuracy on the long-term volatility. [ABSTRACT FROM AUTHOR]

This paper investigates volatility forecasting for crude oil and natural gas. The main objective of our research is to determine whether the heterogeneous autoregressive ( HAR ) model of Corsi (2009) can be outperformed by harnessing information from a related energy commodity. We find that on average, information from related commodity does not improve volatility forecasts, whether we consider a multivariate model, or various univariate models that include this information. However, superior volatility forecasts are produced by combining forecasts from various models. As a result, information from the related commodity can be still useful, because it allows us to construct wider range of possible models, and averaging across various models improves forecasts. Therefore, for somebody interested in precise volatility forecasts of crude oil or natural gas, we recommend to focus on model averaging instead of just including information from related commodity in a single forecast model. [ABSTRACT FROM AUTHOR]

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]

Abstract: In this study we propose several new variables, such as continuous realized semi‐variance and signed jump variations including jump tests, and construct a new heterogeneous autoregressive model for realized volatility models to investigate the impacts that those new variables have on forecasting oil price volatility. In‐sample results indicate that past negative returns have greater effects on future volatility than that of positive returns, and our new signed jump variations have a significantly negative influence on the future volatility. Out‐of‐sample empirical results with several robust checks demonstrate that our proposed models can not only obtain better performance in forecasting volatility but also garner larger economic values than can the existing models discussed in this paper. [ABSTRACT FROM AUTHOR]

Estimating volatility from recent high frequency data, we revisit the question of the smoothness of the volatility process. Our main result is that log-volatility behaves essentially as a fractional Brownian motion with Hurst exponent H of order 0.1, at any reasonable timescale. This leads us to adopt the fractional stochastic volatility (FSV) model of Comte and Renault [Long memory in continuous-time stochastic volatility models. Math. Finance, 1998, 8(4), 291-323]. We call our model Rough FSV (RFSV) to underline that, in contrast to FSV, . We demonstrate that our RFSV model is remarkably consistent with financial time series data; one application is that it enables us to obtain improved forecasts of realized volatility. Furthermore, we find that although volatility is not a long memory process in the RFSV model, classical statistical procedures aiming at detecting volatility persistence tend to conclude the presence of long memory in data generated from it. This sheds light on why long memory of volatility has been widely accepted as a stylized fact. [ABSTRACT FROM AUTHOR]

Stock market volatility forecasting is an important and interesting topic of research due to its impact on trading decisions. This behavior is particularly important in emerging economies in Latin America, and moreover, in the larger stock markets of this region (Brazil, Mexico, and Chile). The Latin American region is highly influenced by macroeconomic factors; therefore, it is relevant to discover ways in which the market index forecast accuracy can be improved. Thus, in this study, we present a novel methodology: first, we forecast the volatility of each market using different GARCH models. Then, we use Markov Switching to determine the states of external factors. Subsequently, these states are combined in a ANFIS model to determine individual impact on each index, and finally, we use an ANN algorithm to improve the forecast accuracy of the best GARCH model forecast with the combined effects of all the external factors. The results indicate that this methodology manages to improve prediction in terms of MAPE and RMSE, thus providing a more accurate volatility estimation. [ABSTRACT FROM AUTHOR]

This paper evaluates the accuracy of several hundred one-day-ahead value at risk (VaR) forecasts for predicting Australian electricity returns. We propose a class of observation-driven time series models referred to as asymmetric exponential generalised autoregressive score (AEGAS) models. The mechanism to update the parameters over time is provided by the scaled score of the likelihood function in the AEGAS model. Based on this new approach, the results provide a unified and consistent framework for introducing time-varying parameters in a wide class of non-linear models. The Australian energy markets is known as one of the most volatile and, when compared to some well-known models in the recent literature as benchmarks the fitting and forecasting results demonstrate the superior performance and considerable flexibility of proposed model for electricity markets. [ABSTRACT FROM AUTHOR]

This paper provides an evaluation of the predictive performance of the volatility of three cryptocurrencies and three currencies with recognized stores of value using daily and hourly frequency data. We combined the traditional GARCH model with the machine learning approach to volatility estimation, estimating the mean and volatility equations using Support Vector Regression (SVR) and comparing to GARCH family models. Furthermore, the models’ predictive ability was evaluated using Diebold-Mariano test and Hansen’s Model Confidence Set. The analysis was reiterated for both low and high frequency data. Results showed that SVR-GARCH models managed to outperform GARCH, EGARCH and GJR-GARCH models with Normal, Student’s t and Skewed Student’s t distributions. For all variables and both time frequencies, the SVR-GARCH model exhibited statistical significance towards its superiority over GARCH and its extensions. [ABSTRACT FROM AUTHOR]

We test for the performance of a series of volatility forecasting models (GARCH 1,1; EGARCH 1,1; CGARCH) in the context of several indices from the two oldest cross-border exchanges (Euronext; OMX). Our findings overall indicate that the EGARCH (1,1) model outperforms the other two, both before and after the outbreak of the global financial crisis. Controlling for the presence of feedback traders, the accuracy of the EGARCH (1,1) model is not affected, something further confirmed for both the pre and post crisis periods. Overall, ARCH effects can be found in the Euronext and OMX indices, with our results further indicating the presence of significant positive feedback trading in several of our tests. [ABSTRACT FROM AUTHOR]

Abstract: Using a new variable to measure investor sentiment we show that the sentiment of German and European investors matters for return volatility in local stock markets. A flexible empirical similarity (ES) approach is used to emulate the dynamics of the volatility process by a time‐varying parameter that is created via the similarity of realized volatility and investor sentiment. Out‐of‐sample results show that the ES model produces significantly better volatility forecasts than various benchmark models for DAX and EUROSTOXX. Regarding other international markets no significant difference between the forecasts can be observed. [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]