Related Experiment Video
Updated: Oct 10, 2025
03:53
Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
Published on: November 10, 2023
1.5K
COVID-19 impact on commodity futures volatilities
1School of Business and Research Academy of Belt & Road, Ningbo University, China.
Finance Research Letters
|December 14, 2021
Summary
The COVID-19 pandemic increased long-term volatility in major commodity futures markets. Daily infection rates had varied impacts on short-term volatility, offering insights for risk management and trading.
Area of Science:
- Economics
- Finance
- Econometrics
Background:
- The COVID-19 pandemic significantly disrupted global supply chains, impacting economic activity and financial markets.
- Commodity futures markets are sensitive to supply chain disruptions and economic uncertainty.
Purpose of the Study:
- To investigate the impact of the COVID-19 pandemic on the long-run and short-term volatilities of major commodity futures.
- To extend existing ARMA-GARCH models for analyzing pandemic-related market volatility.
Main Methods:
- Utilized extended ARMA-GARCH models to analyze volatility patterns.
- Examined data from four major commodity futures markets.
Main Results:
- The pandemic demonstrably enhanced long-run volatilities across all analyzed commodity futures.
- The daily speed of COVID-19 infections exhibited mixed effects on short-term (instantaneous) volatilities.
Conclusions:
- The study provides valuable insights for global supply chain risk management.
- Findings are applicable to commodity options trading and regulatory oversight of inflation risk.
More Related Videos
Related Concept Videos
01:10Volatilization
604
Volatilization gravimetry is an analytical technique that measures the mass lost due to the volatilization of the substance. This technique is used to estimate the amount of volatile material in a sample. To perform this method, heat a known amount of the sample to a high temperature in a crucible or other suitable vessel. The volatile substance in the sample evaporates, and the vapor is completely expelled from the crucible either by heating the sample or bubbling a stream of inert gas through...
604
01:11Variability: Analysis
231
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
The range is a simple measure of variability, indicating the difference between the highest and...
231
01:10Coefficient of Variation
6.2K
The coefficient of variation measures the dispersion of the data points or distribution around the mean. Using the coefficient of variation, we can compare two data series with drastically different means or different units of measurement. The coefficient of variation for a sample and a population is expressed as a percentage of the ratio of standard deviation to the mean.
The coefficient of variation is a practical statistical tool in finance. It allows investors to assess the volatility or...
The coefficient of variation is a practical statistical tool in finance. It allows investors to assess the volatility or...
6.2K
00:59Uncertainty: Overview
1.1K
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
1.1K
01:15Chebyshev's Theorem to Interpret Standard Deviation
4.6K
Chebyshev’s theorem, also known as Chebyshev’s Inequality, states that the proportion of values of a dataset for K standard deviation is calculated using the equation:
4.6K
01:26Factors Affecting the Risk of Infection
12.7K
The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
12.7K