The estimation and determinants of emerging market country risk and the dynamic conditional correlation GARCH model

The purpose of this paper is to compare the stability, in terms of contagion, of conventional and Islamic banks in Malaysia. We use a DCC-GARCH model to estimate the dynamic conditional correlation (a measure of financial contagion) for a sample of one Islamic bank and eight conventional banks during the period from March 31, 2004 to March 18, 2014. From the empirical findings, we show that the conditional correlation between the returns of conventional and Islamic banks in Malaysia increased during the period of financial crisis. This finding implies the existence of a financial contagion effect between Islamic and conventional banks in Malaysia. Also, we find that financial contagion represents a major factor for the transmission of risk between Islamic and conventional banks.

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Penerapan Model Dynamic Conditional Correlation GARCH Pada Data Saham

ESTIMASI: Journal of Statistics and Its Application ◽

10.20956/ejsa.v2i2.10569 ◽

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.

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Speculative Bubbles and Contagion: Analysis of Volatility's Clusters During the Dotcom Bubble Based on the Dynamic Conditional Correlation Model

SSRN Electronic Journal ◽

10.2139/ssrn.3004481 ◽

2017 ◽

Author(s):

Maximilian-Benedikt Herwarth Kohn ◽

Pedro L. Valls Pereira

Keyword(s):

Correlation Model ◽

Conditional Correlation ◽

Speculative Bubbles ◽

Dynamic Conditional Correlation

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

Scientific Reports ◽

10.1038/s41598-021-93836-y ◽

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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Multivariate Analysis of Cryptocurrencies

Econometrics ◽

10.3390/econometrics9030028 ◽

2021 ◽

Vol 9 (3) ◽

pp. 28

Author(s):

Vincenzo Candila

Keyword(s):

Great Recession ◽

Market Value ◽

Conditional Correlation ◽

Dynamic Conditional Correlation ◽

Mixed Frequency ◽

The Great Recession ◽

The World ◽

Asymmetric Garch ◽

Portfolio Managers ◽

Points Of View

Recently, the world of cryptocurrencies has experienced an undoubted increase in interest. Since the first cryptocurrency appeared in 2009 in the aftermath of the Great Recession, the popularity of digital currencies has, year by year, risen continuously. As of February 2021, there are more than 8525 cryptocurrencies with a market value of approximately USD 1676 billion. These particular assets can be used to diversify the portfolio as well as for speculative actions. For this reason, investigating the daily volatility and co-volatility of cryptocurrencies is crucial for investors and portfolio managers. In this work, the interdependencies among a panel of the most traded digital currencies are explored and evaluated from statistical and economic points of view. Taking advantage of the monthly Google queries (which appear to be the factors driving the price dynamics) on cryptocurrencies, we adopted a mixed-frequency approach within the Dynamic Conditional Correlation (DCC) model. In particular, we introduced the Double Asymmetric GARCH–MIDAS model in the DCC framework.

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