scholarly journals Price Volatility Transmission in China’s Hardwood Lumber Imports

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1147
Author(s):  
Xiudong Wang ◽  
Zhonghua Yin ◽  
Ruohan Wang
Keyword(s):  
Price Volatility ◽  
Forest Products ◽  
Tectona Grandis ◽  
Policy Implications ◽  
Conditional Correlation ◽  
Price Fluctuations ◽  
Dynamic Conditional Correlation ◽  
Volatility Transmission ◽  
Hardwood Lumber ◽  
Timber Trade

Hardwood lumber is the principal part of the global hardwood timber trade. China has become the largest importer of hardwood lumber in the world. However, China’s hardwood lumber imports are affected by price volatility. Thus, we investigated the price volatility transmission of China’s hardwood lumber imports. We aimed to detect the source, path, and intensity of the volatility transmission in China’s hardwood lumber imports, and reveal the intrinsic interactions between price volatilities. To date, there is little research on the price fluctuations of forest products. This paper provides an empirical analysis on the volatility transmission in China’s forest product imports. We selected four types of major hardwood lumber imports to China; that is, teak (Tectona grandis L.F.), merbau (Merbau), sapele (Entandrophragma), and casla (Terminalia spp.) (The Latin names of tree species are given in parentheses), and used their daily prices from 4 August 2010 to 15 April 2020. The Baba–Engle–Kraft–Kroner (BEKK) multivariate models and dynamic conditional correlation (DCC) models were employed. The empirical results indicate that there is an intrinsic relationship between the price fluctuations in China’s hardwood lumber imports. The volatility transmission chain originates from casla; it is transmitted along the casla→sapele→merbau→teak pathway. The direction of transmission is from lower prices to higher prices. The dynamic conditional correlation of each link in the chain does not exhibit any particular time trend. This suggests that volatility transmission is a crucial price mechanism in China’s hardwood lumber imports. Our findings have important policy implications for hedging timber price risks and designing timber trade policies.

scholarly journals Exploring the Dynamic Links between GCC Sukuk and Commodity Market Volatility

2018 ◽  
Vol 6 (3) ◽  
pp. 72 ◽  
Author(s):  
Nader Naifar
Keyword(s):  
Management Practices ◽  
Commodity Prices ◽  
Precious Metals ◽  
Price Volatility ◽  
Commodity Price ◽  
Conditional Correlation ◽  
Dynamic Conditional Correlation ◽  
Commodity Price Volatility ◽  
The Impact ◽  
Risk Ratios

This study investigates the impact of commodity price volatility (including soft commodities, precious metals, industrial metals, and energy) on the dynamics of corporate sukuk returns. Using a sample of sukuk indices from Gulf Cooperation Council (GCC) countries, we study the dynamic conditional correlation using a multivariate generalized autoregressive conditional heteroskedasticity dynamic conditional correlation (GARCH-DCC) process. Empirical results show a time-varying negative correlation between GCC sukuk returns and commodity prices. In fact, a negative conditional correlation among assets of a given portfolio implies higher gain-to-risk ratios. An understanding of volatility and dynamic co-movements in financial and commodity markets is important for portfolio allocation and risk management practices.

scholarly journals Commodity Price Correlation And Time Varying Hedge Ratios

2014 ◽  
Vol 30 (4) ◽  
pp. 1053
Author(s):  
Amine Lahiani ◽  
Khaled Guesmi
Keyword(s):  
Stock Market ◽  
Value At Risk ◽  
Price Volatility ◽  
Commodity Price ◽  
Conditional Value At Risk ◽  
Time Varying ◽  
Conditional Correlation ◽  
Dynamic Conditional Correlation ◽  
Hedging Strategies ◽  
Hedge Ratios

<p>This paper examines the price volatility and hedging behavior of commodity futures indices and stock market indices. We investigate the weekly hedging strategies generated by return-based and range-based asymmetric dynamic conditional correlation (DCC) processes. The hedging performances of short and long hedgers are estimated with a semi-variance, low partial moment and conditional value-at-risk. The empirical results show that range-based DCC model outperforms return-based DCC model for most cases.</p>

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>

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