scholarly journals Inflation inertia in Turkish economy: dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) and wavelet analysis

Pressacademia ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 324-337
Author(s):  
Caner Ozdurak ◽  
Cengiz Karatas
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abdelkader Derbali ◽  
Kamel Naoui ◽  
Lamia Jamel

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.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Muhammad Kamran ◽  
Pakeezah Butt ◽  
Assim Abdel-Razzaq ◽  
Hadrian Geri Djajadikerta

Purpose This study aims to address the timely question of whether Bitcoin exhibited a safe haven property against the major Australian stock indices during the first and second waves of the COVID-19 pandemic in Australia and whether such property is similar or different in one year time from the first wave of the COVID-19. Design/methodology/approach The authors used the bivariate Dynamic Conditional Correlation, Generalized Autoregressive Conditional Heteroskedasticity model, on the five-day returns of Bitcoin and Australian stock indices for the sample period between 23 April, 2011 and 19 April, 2021. Findings The results show that Bitcoin offered weak safe haven and hedging benefits when combined in a portfolio with S&P/ASX 200 Financials index, S&P/ASX 200 Banks index or S&P/ASX 300 Banks index. In regard to the S&P/ASX All Ordinaries Gold index, the authors found Bitcoin a risky candidate with inconsistent safe haven and hedging benefits. Against S&P/ASX 50 index, S&P/ASX 200 index and S&P/ASX 300 index, Bitcoin was nothing more than a diversifier. The outset of the second COVID-19 wave, which was comparatively more severe than the first, is also reflected in the results with considerably higher correlations. Originality/value There is a lack of in-depth empirical evidence on the safe haven capabilities of Bitcoins for various Australian stock indices during the first and second waves of the COVID-19 pandemic. The study bridges this void in research.


Pomorstvo ◽  
2020 ◽  
Vol 34 (2) ◽  
pp. 428-437
Author(s):  
Totakura Bangar Raju ◽  
Ayush Bavise ◽  
Pradeep Chauhan ◽  
Bhavana Venkata Ramalingeswar Rao

The International Grain Council (IGC) circulates two price indices which are the Grain and Oilseeds Index (GOI) and the Grain and Oilseeds Freight Market Index (GOFI). These two indices indicate the respective market prices. The GOI markets are affected by various factors like supply and demand, weather, freight markets, etc. This research article attempts to explore and analyse volatility in GOI and GOFI markets using various GARCH family models, that is Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) analysis. The multivariate Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity model (DCC GARCH) is used to find the spillovers between the two markets and thereby explore the effect of GOFI on GOI markets from the year 2013. The research article consists of four sections after introducing the subject namely a literature review, research methodology and models, analysis and conclusions of the study.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Halit Cinarka ◽  
Mehmet Atilla Uysal ◽  
Atilla Cifter ◽  
Elif Yelda Niksarlioglu ◽  
Aslı Çarkoğlu

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.


Econometrics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 28
Author(s):  
Vincenzo Candila

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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