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.

scholarly journals A Time-Varying Gerber Statistic: Application of a Novel Correlation Metric to Commodity Price Co-Movements

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 339-354
Author(s):  
Bernardina Algieri ◽  
Arturo Leccadito ◽  
Pietro Toscano
Keyword(s):  
Commodity Prices ◽  
Commodity Price ◽  
Time Varying ◽  
Conditional Correlation ◽  
Forecasting Accuracy ◽  
Novel Approach ◽  
Correlation Models ◽  
Rolling Window ◽  
Auto Regressive ◽  
Energy Products

This study investigates the daily co-movements in commodity prices over the period 2006–2020 using a novel approach based on a time-varying Gerber correlation. The statistic is computed considering a set of probabilities estimated via non-traditional models that give a time-varying structure to the measure. The results indicate that there are several co-movements across commodities, that these co-movements change over time, and that they are tendentially positive. Conditional auto-regressive multithreshold logit models show higher forecasting accuracy for agricultural returns, while dynamic conditional correlation models are more accurate for energy products and metals. The proposed models are shown to be superior in terms of forecasting power to the benchmark method which is based on estimating the Gerber correlation moving a rolling window.

scholarly journals ON THE STATIONARITY OF DYNAMIC CONDITIONAL CORRELATION MODELS

2016 ◽  
Vol 33 (3) ◽  
pp. 636-663 ◽  
Author(s):  
Jean-David Fermanian ◽  
Hassan Malongo
Keyword(s):  
Markov Chains ◽  
Sufficient Conditions ◽  
Stationary Solutions ◽  
Garch Models ◽  
Conditional Correlation ◽  
Dynamic Conditional Correlation ◽  
Correlation Models

We provide conditions for the existence and the uniqueness of strictly stationary solutions of the usual Dynamic Conditional Correlation GARCH models (DCC-GARCH). The proof is based on Tweedie’s (1988) criteria, after having rewritten DCC-GARCH models as nonlinear Markov chains. We also study the existence of their moments and discuss the tightness of our sufficient conditions.

scholarly journals Analytical Gradients of Dynamic Conditional Correlation Models

2020 ◽  
Vol 13 (3) ◽  
pp. 49 ◽  
Author(s):  
Massimiliano Caporin ◽  
Riccardo (Jack) Lucchetti ◽  
Giulio Palomba
Keyword(s):  
Parameter Estimation ◽  
Computational Simulation ◽  
Conditional Variance ◽  
Full Model ◽  
Conditional Correlation ◽  
Dynamic Conditional Correlation ◽  
Correlation Models ◽  
Variance Parameters ◽  
Improved Accuracy ◽  
Analytical Gradients

We provide the analytical gradient of the full model likelihood for the Dynamic Conditional Correlation (DCC) specification by Engle (2002), the generalised version by Cappiello et al. (2006), and of the cDCC model by Aielli(2013). We discuss how the gradient might be further extended by introducing elements related to the conditional variance parameters, and discuss the issue arising from the estimation of constrained and/or reparametrised versions of the model. A computational simulation compares analytical versus numerical gradients, with a view to parameter estimation; we find that analytical differentiation yields more efficiency and improved accuracy.

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