scholarly journals Copula-Based Dynamic Conditional Correlation Multiplicative Error Processes

2012 ◽  
Author(s):  
Taras Bodnar ◽  
Nikolaus Hautsch
Keyword(s):  
Conditional Correlation ◽  
Dynamic Conditional Correlation ◽  
Multiplicative Error

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 Multivariate Analysis of Cryptocurrencies

Econometrics ◽  
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.

scholarly journals SINCRONIZACIÓN DE LOS CICLOS ECONÓMICOS DE MÉXICO Y ESTADOS UNIDOS: UN ENFOQUE DE CORRELACIÓN DINÁMICA

2021 ◽  
Vol 81 (319) ◽  
pp. 37
Author(s):  
Dulce Albarrán Macías ◽  
Pablo Mejía Reyes ◽  
Francisco López Herrera
Keyword(s):  
United States ◽  
Business Cycles ◽  
Great Recession ◽  
The United States ◽  
Conditional Correlation ◽  
Dynamic Conditional Correlation ◽  
Dynamic Correlation ◽  
Time Period ◽  
The Common ◽  
The Business Cycle

<p>El objetivo de este documento es analizar la sincronización de los ciclos económicos de México y Estados Unidos durante el periodo 1981-2017 mediante la estimación de un coeficiente de correlación condicional dinámica que permite tener una estimación para cada periodo de tiempo. Los resultados, obtenidos a partir de distintos indicadores de producción y métodos de eliminación de tendencia, muestran un aumento desde la apertura de la economía mexicana a mediados de la década de 1980, especialmente durante las recesiones de 2001-2002 y 2008-2009 y también una serie de descensos aislados, explicados por diferencias en los ritmos de crecimiento de ambas economías, y una declinación sostenida en la fase pos-Gran Recesión que se explica principalmente por reducciones en el comercio exterior.</p><p> </p><p align="center">SYNCHRONIZATION OF THE BUSINESS CYCLES OF MEXICO AND THE UNITED STATES: A DYNAMIC CORRELATION APPROACH</p><p align="center"><strong>ABSTRACT</strong></p><p>The objective of this paper is to analyze the business cycle synchronization of Mexico and the United States over the period 1981-2017 by estimating a dynamic conditional correlation coefficient that allows us to have an estimate for each time period. The results, obtained from different production indicators and different de-trending methods, show an increase in this synchronization after the opening of the Mexican economy in the mid-eighties, especially during the common recessions of 2001-2002 and 2008-2009, and some isolated drops explained by differences in the growth rates of both economies as well as a sustained decline in the post-Great Recession phase resulting from the decline of international trade.</p>

Book Reviews

2011 ◽  
Vol 49 (1) ◽  
pp. 150-150
Keyword(s):  
Risk Management ◽  
Stanford University ◽  
Conditional Correlation ◽  
New Paradigm ◽  
Dynamic Conditional Correlation ◽  
New Methods ◽  
Northwestern University ◽  
Large Systems ◽  
Correlation Performance

Jules H. van Binsbergen of Northwestern University, Stanford University, and NBER reviews “Anticipating Correlations: A New Paradigm for Risk Management” by Robert Engle. The EconLit Abstract of the reviewed work begins, “Presents a collection of new methods for estimating and forecasting correlations for large systems of assets. Discusses correlation economics; correlations in theory; models for correlation; dynamic conditional correlation; dynamic conditional correlation performance; the MacGyver method; generalize….”

COVID-19 news in USA and in China: Which is suitable in explaining the nexus among Bitcoin and Gold?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abdelkader Derbali ◽  
Kamel Naoui ◽  
Lamia Jamel
Keyword(s):  
Design Methodology ◽  
Conditional Heteroskedasticity ◽  
Conditional Correlation ◽  
Predictive Capacity ◽  
Content Type ◽  
Dynamic Conditional Correlation ◽  
The Hours ◽  
Important Correlation ◽  
The Impact ◽  
First Time

Purpose The purpose of this paper is to examine empirically the impact of COVID-19 pandemic news in USA and in China on the dynamic conditional correlation between Bitcoin and Gold. Design/methodology/approach This paper offers a crucial viewpoint to the predictive capacity of COVID-19 surprises and production pronouncements for the dynamic conditional correlation (DCC) among Bitcoin and Gold returns and volatilities using generalized autoregressive conditional heteroskedasticity-DCC-(1,1) through the period of study since July 1, 2019 to June 30, 2020. To assess the unexpected impact of COVID-19, this study pursues the Kuttner’s (2001) methodology. Findings The empirical findings indicate strong important correlation among Bitcoin and Gold if COVID-19 surprises are integrated in variance. This study validates the financialization hypothesis of Bitcoin and Gold. The correlation between Bitcoin and Gold begin to react significantly further in the case of COVID-19 surprises in USA than those in China. Originality/value This paper contributes to the literature on assessing the impact of COVID-19 confirmed cases surprises on the correlation between Bitcoin and Gold. This paper gives for the first time an approach to capture the COVID-19 surprise component. Also, this study helps to improve financial backers and policymakers' comprehension of the digital currencies' market elements, particularly in the hours of amazingly unpleasant and inconspicuous occasions.

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