scholarly journals Dynamic conditional correlation analysis of financial market interdependence: An application to Thailand and Indonesia

2007 ◽  
Vol 18 (4) ◽  
pp. 670-684 ◽  
Author(s):  
Gerard H. Kuper ◽  
Lestano
Keyword(s):  
Correlation Analysis ◽  
Financial Market ◽  
Conditional Correlation ◽  
Dynamic Conditional Correlation ◽  
Market Interdependence

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 Penerapan Model Dynamic Conditional Correlation GARCH Pada Data Saham

2021 ◽  
pp. 80-87
Author(s):  
Ika Fitriana ◽  
Erna Tri Herdiani ◽  
Georgina Maria Tinungki
Keyword(s):  
Stock Market ◽  
Financial Market ◽  
Stock Prices ◽  
Model Equation ◽  
Garch Model ◽  
Time Dependent ◽  
Conditional Correlation ◽  
Stock Volatility ◽  
Dynamic Conditional Correlation ◽  
A Company

Stock is one of the popular financial market instruments. Issuing shares are one of the company's choices when deciding to fund a company. The uncertainty of stock prices in the stock market is an important event to be taken into consideration in making a decision by investors so that a model is needed to describe a stock event. GARCH Dynamic Conditional Correlation (DCC) is a model with a conditional and variance time-dependent that describes the dynamics of stock volatility. This study discusses the DCC GARCH model equation which is applied to the LQ 45 data. The model obtained for BCA shares 𝑸t = +  +  so it can be concluded that DCC GARCH is more appropriate for BCA shares.

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.

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