Volatility is a measure of the asset return rate's estimated level of uncertainty and may be used to assess the riskiness of financial assets. We use the market capitalization of 200 stocks representing Korea as the primary analytical aim and evaluate the accuracy between them by analyzing the impacts of different hybrid models' hybrid neural networks, which are based on the returns of the KOSPI 200 stock index. By measuring the effectiveness of these models using four dissimilarity measures, we contrasted the performance of hybrid models that combine a single neural network and a single GARCH type model with that of hybrid neural networks that combine multiple GARCH models (MAE, MSE, HMAE, and HMSE). They are applied to anticipate the KOSPI 200 index data's actual volatility. Among these, hybrid neural networks that integrate more than one GARCH-type model have much better forecasting performance than neural network models that mix two or more or more GARCH-type models. GW-LSTM makes the least accurate forecast. We note that the hybrid model combining the three GARCH models shows a minor increase in predicting ability based on merging two and three models.
Using the VAR-DCC-GARCH model, the paper studies the dynamic correlation between Ruble closing price and WTI's oil price, and between Euro and WTI's oil price during the Russo-Ukrainian war. The results show that before the Russia Ukraine war, there was a strong correlation between the Ruble and WTI oil prices, and between the Euro and WTI oil prices. Their connection deteriorates sharply and became negative during the Russia Ukraine conflict. We speculate that European stock market investors will flee risky assets and turn to safe haven assets, and China may use Euros in oil trade settlement in the future.
We collect a total of 1830 data from January 2020 to June 2022 and use R for data processing and wavelet analysis. Moreover, we analyze the interactions between the COVID-19 pandemic, the Russian-Ukrainian war, crude oil price, the S&P 500 and economic policy uncertainty within a time-frequency frame work. As a result that the COVID-19 pandemic and the Russian-Ukrainian war has the extraordinary effects on the three indexes and the effect of the Russian- Ukrainian war on the crude oil price and US stock price higher than on the US economic uncertainty.
This paper analyzes the correlation between bitcoin, oil price fluctuations and the DOW Jones Industrial Index in the time-frequency framework. Coherent wavelet method applied to recent daily data in the United States (1863 in total). Our research has several implications and supports for policy makers and asset managers. We find that oil prices lead the U.S. market at both low and high frequencies throughout the observation period. This result suggests that sanctions against Russia by a number of countries, including the U.S., are influencing oil prices, while oil remains a major source of systemic risk to the U.S. economy and economic uncertainty between the international level is exacerbated by tensions between Russia and Ukraine.
According to the IPCC's Sixth Assessment Report (AR6) Working Group I report, Climate Change 2021, many changes in the climate system are directly linked to increased global warming, including extreme heat events, ocean heat waves, and increased frequency and intensity of heavy precipitation. The report, Climate Change 2021: The Natural Science Basis, released by the United Nations Intergovernmental Panel on Climate Change (IPCC) on Aug. 9. Climate change will intensify in all regions of the globe in the coming decades, with more frequent extreme heat and heavy rainfall events. The dual intensification of the intensity and frequency of extreme weather will have a dramatic impact on society as a whole and on humanity. Protecting the environment and slowing down global warming, thereby reducing the frequency and intensity of extreme weather events, will require concerted efforts by all in society. The main objective of this study is to understand the Internet-wide perception of extreme weather. This paper focuses on using a natural language processing (NLP) method, the Latent Dirichlet Allocation model, which can summarize and extract key information well. Our results found that comments on Twitter can be divided into 15 main topics.
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