Modeling dynamic volatility under uncertain environment with fuzziness and randomness

The problem related to predicting dynamic volatility in financial market plays a crucial role in many contexts. We build a new generalized Barndorff-Nielsen and Shephard (BN-S) model suitable for uncertain environment with fuzziness and randomness. This new model considers the delay phenomenon between price fluctuation and volatility changes, solves the problem of the lack of long-range dependence of classic models. Through the experiment of Dow Jones futures price, we find that compared with the classical model, this method effectively combines the uncertain environmental characteristics, which makes the prediction of dynamic volatility has more ideal performance

Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning

This paper models stochastic process of price time series of CSI 300 index in Chinese financial market, analyzes volatility characteristics of intraday high-frequency price data. In the new generalized Barndorff-Nielsen and Shephard model, the lag caused by asynchrony of market information is considered, and the problem of lack of long-term dependence is solved. To speed up the valuation process, several machine learning and deep learning algorithms are used to estimate parameter and evaluate forecast results. Tracking historical jumps of different magnitudes offers promising avenues for simulating dynamic price processes and predicting future jumps. Numerical results show that the deterministic component of stochastic volatility processes would always be captured over short and longer-term windows. Research finding could be suitable for influence investors and regulators interested in predicting market dynamics based on realized volatility

Individual Tree Species Classification Based on Convolutional Neural Networks and Multitemporal High-Resolution Remote Sensing Images

The classification of individual tree species (ITS) is beneficial to forest management and protection. Previous studies in ITS classification that are primarily based on airborne LiDAR and aerial photographs have achieved the highest classification accuracies. However, because of the complex and high cost of data acquisition, it is difficult to apply ITS classification in the classification of large-area forests. High-resolution, satellite remote sensing data have abundant sources and significant application potential in ITS classification. Based on Worldview-3 and Google Earth images, convolutional neural network (CNN) models were employed to improve the classification accuracy of ITS by fully utilizing the feature information contained in different seasonal images. Among the three CNN models, DenseNet yielded better performances than ResNet and GoogLeNet. It offered an OA of 75.1% for seven tree species using only the WorldView-3 image and an OA of 78.1% using the combinations of WorldView-3 and autumn Google Earth images. The results indicated that Google Earth images with suitable temporal detail could be employed as auxiliary data to improve the classification accuracy

Impacts of Rice–Rape Rotation on Major Soil Quality Indicators of Soil in the Karst Region

Arable land resources in karst regions are relatively scarce. The original crop rotation pattern can no longer meet the requirements of productivity development, while different crop rotation patterns have different impacts on the physicochemical properties of the soil. Through field experiments and laboratory analysis, the physicochemical properties and pollution characteristics of the soil during different crop growing stages in rice–rape rotation were investigated systematically. The main results are as follows. During the rice–rape rotation, fine sand in the topsoil experienced the greatest variation. During the rotation, pH variation in the subsoil was greater than that in the topsoil. The soil in paddy fields was poorly ventilated, and the rotation could reduce the redox potential of the soil. In the rotation process, the soil organic matter in the topsoil was higher than that in the subsoil, but the variation of soil organic matter in the topsoil was lower than that in the subsoil. The worst Cd pollution of the topsoil occurred in the seedling stage of rice, while that of the subsoil occurred in the flowering stage of rape; the comprehensive pollution index of Cr and Cd in the subsoil was higher than that in the topsoil. It is of great significance to investigate efficient crop rotation patterns under the conditions of the current productivity for promoting sustainable increases of rape and rice yield, maintaining soil fertility, and improving the soil

The evolution of dual-phase core–shell structure and mechanical properties induced by Co addition in as-cast high-entropy intermetallic

In this work, three as-cast L12-type HEIs with different content of Co element were carefully examined combined with TEM, SEM and XRD technologies. The main matrix of three alloys was L12 ordered phase, and Si,Ti-rich grain-boundary precipitate phase generated with the addition of Co. In Co1 alloy, distinct dual-phase core–shell structure was found within grain due to the precipitation of extra FCC disordered secondary phase, which leads to a better strength-plasticity combination (compressive strength of 3441 ± 50 MPa, and fracture strain of 46.0 ± 1.5%) than Co0 and Co0.5 alloy at room temperature. With the increase of temperature, particular anomalous yield effect was discovered in Co1 alloy. It is clear that SFs became main deformation substructure in Co1 alloy at elevated temperature, and the interaction between non-coplanar SFs motivated the generation of L-C Locks, which assists the K-W Locks to pin dislocations movement and enhance the yield strength from room temperature to about 650 ℃. In addition, the interaction between SFs resulted in the formation of nano-spaced SF network, which contributes to the dynamic Hall-Petch effect. Finally, excellent high-temperature yield strength was achieved in as-cast Co1 alloy, which is higher than room-temperature yield strength until 950 ℃

Study of golden pompano (Trachinotus ovatus) freshness forecasting method by utilising Vis/NIR spectroscopy combined with electronic nose

Golden pompano (Trachinotus ovatus) quality forecasting method utilising Vis/NIR spectroscopy combined with electronic nose (EN) was investigated in this article. Responses of Vis/NIR spectroscopy and EN to pompanos stored at 4°C were measured for 6 days. Physical/chemical indexes including texture, total volatile basic nitrogen, pH, total viable counts, and human sensory evaluation were synchronously examined as quality references. Chemometric methods including principal component analysis (PCA) and stochastic resonance (SR) were employed for spectroscopic and EN data analysis. Physicochemical examination demonstrated that fish quality decreased rapidly during storage. PCA qualitatively classified freshness degree of pompano samples, while SR signal-to-noise ratio (SNR) spectrum using SNR maximum quantitatively characterised quality for all samples. Golden pompano quality predictive models were developed based on spectroscopy, EN, and spectroscopy combined with EN, respectively. Results demonstrated that the model developed based on spectroscopy combined with EN presented a forecasting accuracy of 93.3%