Conditional Correlation Models of Autoregressive Conditional Heteroscedasticity With Nonstationary GARCH Equations

2014 ◽  
Vol 32 (1) ◽  
pp. 69-87 ◽  
Author(s):  
Cristina Amado ◽  
Timo Teräsvirta
Keyword(s):  
Conditional Correlation ◽  
Conditional Heteroscedasticity ◽  
Correlation Models ◽  
Autoregressive Conditional Heteroscedasticity

scholarly journals POSSIBILITY OF RUSSIAN BONDSPOTFOLIO DIVERSIFICATION THROUGH THE USE OF ISLAMIC BONDS DURING THE COVID-19 PANDEMIC

2021 ◽  
pp. 201-209
Author(s):  
Галина Львовна Толкаченко ◽  
Павел Андреевич Карасев
Keyword(s):  
Correlation Analysis ◽  
Negative Correlation ◽  
Sovereign Debt ◽  
Corporate Bonds ◽  
Suitable Model ◽  
Investment Management ◽  
Conditional Correlation ◽  
Conditional Heteroscedasticity ◽  
Dynamic Conditional Correlation ◽  
Autoregressive Conditional Heteroscedasticity

Диверсификация - один из важнейших элементов в инвестиционной деятельности. Инвесторы пытаются найти баланс при формировании портфеля и его реструктуризации, стремясь одновременно максимизировать доходность и минимизировать риски. Целью данной работы является оценка возможности диверсификации портфеля облигаций российского рынка с помощью включения альтернативной традиционным облигациям формы - сукук в условиях пандемии COVID-19. Представленный в статье анализ такой возможности составляет определенный элемент новизны. В качестве наиболее подходящей модели для корреляционного анализ выбрана «DCC-MGARCH» модель (динамическая модель авторегрессионной условной гетероскедастичности). Результаты исследования показывают, что инвесторы, предпочитающие долговые суверенные ценные бумаги России и корпоративные облигации российских компаний, имеют возможность диверсифицировать портфель путем включения исламских облигаций. Данный вывод объясняется наличием отрицательной корреляционной связи между индексом сукук и индексами российских облигаций, как корпоративных, так и суверенных. Diversification is one of key elements in investment management. Investors strive to find a balance in the formation of a portfolio and its restructuring, simultaneously maximizing profitability and minimizing risks. The purpose of this work is to assess the possibility of diversification of the Russian bonds portfolioby including an alternative to traditional bonds-sukuk. The DCC-MGARCH model (Dynamic Conditional Correlation Multivariate General Autoregressive Conditional Heteroscedasticity Model) was chosen as the most suitable model for correlation analysis. The results of the study show that investors who prefer Russian sovereign debt securities or corporate bonds of Russian companies couldeffectively diversify their portfolio by including Islamic bonds during the COVID-19 pandemic. This conclusion is explained by the presence of a negative correlation between the Dow Jones Sukuk Index as a proxy for sukuk market and the indices of Russian bonds, both corporate and sovereign.

scholarly journals When Elon Musk Changes his Tone, Does Bitcoin Adjust Its Tune?

2022 ◽  
Author(s):  
Toan Luu Duc Huynh
Keyword(s):  
Social Media ◽  
Textual Analysis ◽  
Causal Relation ◽  
Window Size ◽  
Media Content ◽  
Conditional Correlation ◽  
Conditional Heteroscedasticity ◽  
Dynamic Conditional Correlation ◽  
The Relationship ◽  
Autoregressive Conditional Heteroscedasticity

AbstractWe present a textual analysis that explains how Elon Musk’s sentiments in his Twitter content correlates with price and volatility in the Bitcoin market using the dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity model, allowing less sensitive to window size than traditional models. After examining 10,850 tweets containing 157,378 words posted from December 2017 to May 2021 and rigorously controlling other determinants, we found that the tone of the world’s wealthiest person can drive the Bitcoin market, having a Granger causal relation with returns. In addition, Musk is likely to use positive words in his tweets, and reversal effects exist in the relationship between Bitcoin prices and the optimism presented by Tesla’s CEO. However, we did not find evidence to support linkage between Musk’s sentiments and Bitcoin volatility. Our results are also robust when using a different cryptocurrency, i.e., Ether this paper extends the existing literature about the mechanisms of social media content generated by influential accounts on the Bitcoin market.

scholarly journals The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Halit Cinarka ◽  
Mehmet Atilla Uysal ◽  
Atilla Cifter ◽  
Elif Yelda Niksarlioglu ◽  
Aslı Çarkoğlu
Keyword(s):  
Case Reports ◽  
Time Varying ◽  
Conditional Correlation ◽  
Web Based ◽  
Specific Symptom ◽  
Sliding Windows ◽  
Dynamic Conditional Correlation ◽  
The United Kingdom ◽  
Correlation Models ◽  
Google Search

AbstractThis study aims to evaluate the monitoring and predictive value of web-based symptoms (fever, cough, dyspnea) searches for COVID-19 spread. Daily search interests from Turkey, Italy, Spain, France, and the United Kingdom were obtained from Google Trends (GT) between January 1, 2020, and August 31, 2020. In addition to conventional correlational models, we studied the time-varying correlation between GT search and new case reports; we used dynamic conditional correlation (DCC) and sliding windows correlation models. We found time-varying correlations between pulmonary symptoms on GT and new cases to be significant. The DCC model proved more powerful than the sliding windows correlation model. This model also provided better at time-varying correlations (r ≥ 0.90) during the first wave of the pandemic. We used a root means square error (RMSE) approach to attain symptom-specific shift days and showed that pulmonary symptom searches on GT should be shifted separately. Web-based search interest for pulmonary symptoms of COVID-19 is a reliable predictor of later reported cases for the first wave of the COVID-19 pandemic. Illness-specific symptom search interest on GT can be used to alert the healthcare system to prepare and allocate resources needed ahead of time.

scholarly journals Do the Determinants of Non-Performing Loans Have a Different Effect over Time? A Conditional Correlation Approach

2021 ◽  
Vol 14 (1) ◽  
pp. 21
Author(s):  
Mariagrazia Fallanca ◽  
Antonio Fabio Forgione ◽  
Edoardo Otranto
Keyword(s):  
Correlation Coefficients ◽  
Bank Loan ◽  
Time Varying ◽  
Conditional Correlation ◽  
Subprime Mortgage ◽  
Macroeconomic Variables ◽  
Correlation Models ◽  
Mortgage Crisis ◽  
The Us ◽  
Macroeconomic Indicators

Several studies have explored the linkage between non-performing loans and major macroeconomic indicators, using a wide variety of methodologies, sometimes with different results. This occurs, we argue, because these relationships are generally derived in terms of correlation coefficients evaluated in certain time spans, which represent a sort of average level of correlations. However, such correlations are necessarily time-varying, because the relationships between bank loan indicators and macroeconomic variables could be stronger during particular periods or in correspondence with important economic events. We propose an empirical exercise using dynamic conditional correlation models, with constant and time-varying parameters. Applying these models to quarterly delinquency rates and an array of macroeconomic variables for the US, for the period 1985–2019, we find that the correlation is often negligible in this period except during periods of economic crises, in particular the early 1990 crisis and the subprime mortgage crisis.

Financial market trend analysis based on autoregressive conditional heteroscedasticity model and BP neural network prediction

2021 ◽  
pp. 002072092098849
Author(s):  
Xinzhe Yin ◽  
Jinghua Li
Keyword(s):  
Neural Network ◽  
Trend Analysis ◽  
Financial Market ◽  
Prediction Method ◽  
Good Effect ◽  
Trend Prediction ◽  
Bp Network ◽  
Conditional Heteroscedasticity ◽  
Bp Model ◽  
Autoregressive Conditional Heteroscedasticity

Many experts and scholars at home and abroad have studied this topic in depth, laying a solid foundation for the research of financial market prediction. At present, the mainstream prediction method is to use neural network and autoregressive conditional heteroscedasticity to build models, which is a more scientific way, and also verified the feasibility of the way in many studies. In order to improve the accuracy of financial market trend prediction, this paper studies in detail the neural network system represented by BP and the autoregressive conditional heterogeneous variance model represented by GARCH. Analyze its structure and algorithm, combine the advantages of both, create a GARCH-BP model, and transform its combination structure and optimize the algorithm according to the uniqueness of the financial market, so as to meet the market as much as possible Characteristics. The novelty of this paper is the construction of the autoregressive conditional heteroscedasticity model, which lays the foundation for the prediction of financial market trends through the construction of the model. However, there are some shortcomings in this article. The overall overview of the financial market is not very clear, and the prediction of the BP network is not so comprehensive. Finally, through the actual data statistics of market transactions, the effectiveness of the GARCH-BP model was tested, analyzed and researched. The final results show that model has a good effect on the prediction and trend analysis of market, and its accuracy and availability greatly improved compared with the previous conventional approach, which is worth further study and extensive research It is believed that the financial market prediction model will become one of the mainstream tools in the industry after its later improvement.

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