•We explore the role and effective usage of GPR in forecasting oil volatility.•The GPRS index is constructed by filtering the GPR larger than a threshold.•The GARCH-MIDAS-GPRS model performs the ...best.•GPRS but not GPR contains useful information for forecasting oil volatility.•Both GPR and GPRS help gain higher economic returns.
Motivated by the importance of geopolitical risk and its possible predictive power for oil volatility, this paper aims to quantitatively investigate the role of geopolitical risk (GPR), especially serious geopolitical risk (GPRS), in forecasting oil volatility. For research purposes, the GARCH-MIDAS model is extended by incorporating GPR and GPRS. Then, the new extensions are examined from the perspectives of both statistical and economic significance. In-sample results show that GPR and GPRS lead to oil market fluctuations, while the out-of-sample results strongly confirm that the GARCH-MIDAS-GPRS model with serious GPR significantly outperforms the GARCH-MIDAS model. Moreover, both GPR and GPRS help gain higher economic returns. In particular, serious geopolitical risk contains useful information for the recent future oil volatility and can provide the best economic gains. Oil market investors and government policymakers should pay more attention to extreme geopolitical events and serious geopolitical risk in the context of risk management and portfolio allocation.
Inspired by the commonly held view that international stock market volatility is equivalent to cross‐market information flow, we propose various ways of constructing two types of information flow, ...based on realized volatility (RV) and implied volatility (IV), in multiple international markets. We focus on the RVs derived from the intraday prices of eight international stock markets and use a heterogeneous autoregressive framework to forecast the future volatility of each market for 1 day to 22 days ahead. Our Diebold‐Mariano tests provide strong evidence that information flow with IV enhances the accuracy of forecasting international RVs over all of the prediction horizons. The results of a model confidence set test show that a market's own IV and the first principal component of the international IVs exhibit the strongest predictive ability. In addition, the use of information flows with IV can further increase economic returns. Our results are supported by the findings of a wide range of robustness checks.
•The precise structure of a dragon fruit pulp polysaccharide (DFPP) was identified.•DFPP had a molecular weight of 2.2×103kDa.•→4-β-d-GlcpA-1→, →6-β-d-Galp-1→ and →4-α-l-Rhap-1→ constituted the ...backbone.•α-l-Araf-1→5-α-l-Araf-1→ formed the branch chain.
Dragon fruit is a tropical fruit with good taste. It can bring health benefits to human body. As one of the major bioactive components in this fruit, the polysaccharides might contribute to the health benefits. However, the precise structure information remains unknown. A leading polysaccharide of dragon fruit pulp, DFPP, was purified and identified by NMR and GC–MS. →4-β-d-GlcpA-1→, →6-β-d-Galp-1→ and →4-α-l-Rhap-1→ constituted the backbone and α-l-Araf-1→5-α-l-Araf-1→ formed the branch chain. The precise structure was putatively identified as below. The molecular weight was 2.2×103kDa. The structure information of polysaccharides will be helpful to understand this fruit.
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This paper proposes a new mixed‐frequency approach to predict stock return volatilities out‐of‐sample. Based on the strategy of momentum of predictability (MoP), our mixed‐frequency approach has a ...model switching mechanism that switches between generalized autoregressive conditional heteroskedasticity (GARCH)‐class models that only use low‐frequency data and heterogeneous autoregressive models of realized volatility (HAR‐RV)‐type that only use high‐frequency data. The MoP model simply selects a forecast with relatively good past performance between the GARCH‐class and HAR‐RV‐type forecasts. The model confidence set (MCS) test shows that our MoP strategy significantly outperforms the competing models, which is robust to various settings. The MoP test shows that a relatively good recent past forecasting performance of the GARCH‐class or HAR‐RV‐type model is significantly associated with a relatively good current performance, supporting the success of the MoP model.
Highlights
This paper proposes a new mixed‐frequency approach to predict volatilities.
Our mixed‐frequency approach is based on the momentum of predictability (MoP).
Our MoP model has a model switching mechanism.
The MoP model significantly outperforms the competing models out‐of‐sample.
We demonstrate the existence of MoP between the GARCH‐class and HAR‐RV‐type models.
In this article, we investigate the impacts of jumps, cojumps and their signed components on forecasting oil futures price volatility in the framework of the heterogeneous autoregressive realized ...volatility model. Our empirical results reveal several noteworthy findings. First, the effects of signed jumps and cojumps based on the daily and intraday jump tests on future volatility are asymmetric, and the negative components are much more powerful in forecasting volatility. Moreover, our proposed models, including the signed jump and cojump components, are able to generate higher forecasting accuracy, and we find that disentangling the effects of positive and negative jumps and cojumps can significantly improve forecasts of future volatility. Lastly, our findings are reliable for various robustness checks and our study provides some new insights into forecasting oil price realized volatility, which are useful for researchers, market participants, and policymakers.
•We investigate the impacts of jumps, cojumps and their signed components on oil volatility.•The effects of signed jumps and cojumps are asymmetric.•Our proposed models can generate higher forecasting accuracy.•Disentangling the effects of positive and negative jumps and cojumps can significantly improve forecasts.
This paper presents a method for aspect based sentiment classification tasks, named convolutional multi-head self-attention memory network ( CMA-MemNet ) . This is an improved model based ...on memory networks, and makes it possible to extract more rich and complex semantic information from sequences and aspects. In order to fix the memory networkʼ s inability to capture context-related information on a word-level, we propose utilizing convolution to capture n-gram grammatical information. We use multi-head self-attention to make up for the problem where the memory network ignores the semantic information of the sequence itself. Meanwhile, unlike most recurrent neural network ( RNN ) long short term memory ( LSTM ) , gated recurrent unit ( GRU ) models, we retain the parallelism of the network. We experiment on the open datasets SemEval-2014 Task 4 and SemEval-2016 Task 6. Compared with some popular baseline methods, our model performs excellently.
This paper proposes a simple but efficient way to improve the predictability of stock returns. Instead of torturously constructing new powerful predictors, we readily select existing predictors that ...have low correlations and thus provide complementary information. Our forecasting strategy is to use the selected predictors based on a multivariate regression model. In our forecasting strategy, less powerful predictors are also useful for forecasting stock returns if they could provide complementary information. The empirical results show that our forecasting strategy outperforms not only the univariate regression models that use each predictor's information separately but also combination approaches that use all predictors jointly. We also document that our strategy extracts significantly more useful information from the complementary predictors than the competing models. In addition, from an asset allocation perspective, a mean-variance investor realizes substantial economic gains. Furthermore, the evidence based on Monte Carlo simulations supports the feasibility of our forecasting strategy.
•This paper proposes a simple but efficient strategy to predict stock returns.•Our strategy is to seek useful predictors that can provide complementary information.•Our strategy yields more accurate return forecasts than alternative benchmark models.•Investors realize sizeable economic gains using our strategy to allocate assets.•The evidence from Monte Carlo simulations supports the feasibility of our strategy.
In this paper, we review studies of oil volatility prediction from a new perspective: that of investors who require economic evaluations of forecasting performance. Our results indicate that no ...single volatility model outperforms all of the competing models, of which GARCH and realized volatility models are the most popular. Most studies evaluate forecasting performance using two criteria: value at risk and hedging effectiveness. Parameter instability and model uncertainty are technical issues that affect out-of-sample performance. Most studies assess volatility forecasts from the perspectives of portfolio management and derivative pricing. Whether oil volatility can predict economic variables and the asset pricing implications of oil volatility for financial markets are important topics that require attention.
To improve the predictability of crude oil futures market returns, this paper proposes a new combination approach based on principal component analysis (PCA). The PCA combination approach combines ...individual forecasts given by all PCA subset regression models that use all potential predictor subsets to construct PCA indexes. The proposed method can not only guard against over-fitting by employing the PCA technique but also reduce forecast variance due to extensive forecast combinations, thus benefiting from both the combination of information and the combination of forecasts. Showing impressive out-of-sample forecasting performance, the PCA combination approach outperforms a benchmark model and many related competing models. Furthermore, a mean–variance investor can realize sizeable utility gains by using the PCA combination forecasts relative to the competing forecasts from an asset allocation perspective.
Low-cost and earth-abundant coal has been considered to have a unique structural superiority as carbon sources of carbon quantum dots (CQDs). However, it is still difficult to obtain CQDs from raw ...coal due to its compactibility and lower reactivity, and the majority of the current coal-based CQDs usually emit green or blue fluorescence. Herein, a facile two-step oxidation approach (K
FeO
pre-oxidation and H
O
oxidation) was proposed to fabricate bandgap tunable CQDs from anthracite. The K
FeO
pre-oxidation can not only weaken the non-bonding forces among coal molecules which cause the expansion of coal particles, but also form a large number of active sites on the surface of coal particles. The above effects make the bandgap tunable CQDs (blue, green, or yellow fluorescence) can be quickly obtained from anthracite within 1 h in the following H
O
oxidation by simply adjusting the concentration of H
O
. All the as-prepared CQDs contain more than 30 at% oxygen, and the average diameters of which are <10 nm. The results also indicate that the high oxygen content only can create new energy states inside the band gap of CQDs with average diameter more than 3.2 ± 0.9 nm, which make the as-prepared CQDs emit green or yellow fluorescence.
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