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A Copula-based Quantile Model for Crude oil Return-Volatility Dependence Modelling: Case of Iran Heavy Oil
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Mohammad Sayadi *   1, Mohsen Ebrahimi2 , Atefeh Davari2 |
1- Kharazmi University , m.sayadi@khu.ac.ir
2- Kharazmi University |
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Abstract: (2061 Views) |
The main purpose of this study is to investigate the relationship between Iran’s heavy crude oil price returns and volatility dependence using the Copula-based quantile model (CQM). CQM is an efficient tool for analyzing nonlinear time series models as it has no need for initial assumptions. We use monthly data from January 1990 to December 2019. We use the Hadrick-Prescott filter to calculate oil price fluctuations and use quantiles in both quartile and percentile form. The Kolmogorov-Smirnov test and the descriptive statistics confirm the fat-tail distribution of the variables. It is thus not possible to calculate a direct relationship between crude oil price return and volatility. Based on the results of CQM estimation, a positive and significant relationship is verified between crude oil price volatility and returns in quantiles (0.05, 0.1, 0.2, 0.25, 0.3, 0.8, 0.9, and 0.95) which relate to periods of instability and crisis (war and economic sanctions). This finding can be theoretically explained through the channel of the precautionary demand effect on the oil price. The hypothesis of equality of variances was rejected within different quantiles based on ANOVA test result. Moreover, there is a significant relationship between one-period lag of oil price volatility (Xt-1) and oil returns within different quantiles. Using these results, investors can more effectively manage the risk of investing in the oil market as well as other financial assets related to the oil market.
JEL Classification: Q31, Q43, F51, F52
Keywords: Return, Volatility, Quantile Copula Regression, Crude Oil Price
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| Keywords: Return, Volatility, Quantile Copula Regression, Crude Oil Price |
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Full-Text [PDF 773 kb]
(582 Downloads)
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Type of Study: Research |
Subject:
Oil-Market
Received: 2021/05/6 | Accepted: 2021/09/26 | Published: 2022/01/31 | ePublished: 2022/01/31
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