Is New Energy Driven by Crude Oil, High-tech Sector or Low-carbon Notion? New Evidence from High-frequency Data

Energy ◽  
2021 ◽  
pp. 120770
Author(s):  
Fang Qu ◽  
Yufeng Chen ◽  
Biao Zheng
Keyword(s):  
Crude Oil ◽  
High Frequency ◽  
High Frequency Data ◽  
High Tech ◽  
Low Carbon ◽  
Frequency Data ◽  
High Tech Sector ◽  
New Energy ◽  
New Evidence

scholarly journals Forecasting the Covolatility of Coffee Arabica and Crude Oil Prices: A Multivariate GARCH Approach with High-Frequency Data

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Dawit Yeshiwas ◽  
Yebelay Berelie
Keyword(s):  
Crude Oil ◽  
High Frequency ◽  
Realized Volatility ◽  
Multivariate Garch ◽  
High Frequency Data ◽  
Frequency Data ◽  
Asset Return ◽  
Coffee Arabica ◽  
Forecasting Performance ◽  
Intraday Data

Forecasting the covolatility of asset return series is becoming the subject of extensive research among academics, practitioners, and portfolio managers. This paper estimates a variety of multivariate GARCH models using weekly closing price (in USD/barrel) of Brent crude oil and weekly closing prices (in USD/pound) of Coffee Arabica and compares the forecasting performance of these models based on high-frequency intraday data which allows for a more precise realized volatility measurement. The study used weekly price data to explicitly model covolatility and employed high-frequency intraday data to assess model forecasting performance. The analysis points to the conclusion that the varying conditional correlation (VCC) model with Student’s t distributed innovation terms is the most accurate volatility forecasting model in the context of our empirical setting. We recommend and encourage future researchers studying the forecasting performance of MGARCH models to pay particular attention to the measurement of realized volatility and employ high-frequency data whenever feasible.

Test for volatility spillover effects in Japan’s oil futures markets by a realized variance approach

2019 ◽  
Vol 36 (2) ◽  
pp. 224-239 ◽  
Author(s):  
Tadahiro Nakajima
Keyword(s):  
Crude Oil ◽  
Granger Causality ◽  
High Frequency ◽  
High Frequency Data ◽  
Volatility Spillover ◽  
Frequency Data ◽  
Oil Futures ◽  
Content Type ◽  
Daily Data ◽  
Egarch Model

Purpose The purpose of this paper is twofold. First, the paper examines the risk transmission between crude oil and petroleum product prices of Japan’s oil futures market. Second, it compares the performance of two tests for Granger causality using realized variance (RV) and the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model. Design/methodology/approach The author measures the daily RV of crude oil, kerosene and gasoline futures listed on the Tokyo Commodity Exchange using high-frequency data, and he examines the Granger causality in variance between these variables using the vector autoregression model. Further, the author estimates the EGARCH model based on daily data and test for Granger causality in variance between commodity futures using Hong’s (2001) approach. Findings The results of the RV approach reveal that the hypothesis on the existence of a mutual volatility spillover between crude oil and petroleum product markets is accepted. However, the results of the conventional approach indicate that all the hypotheses on Granger causalities in variance are rejected. The methodology based on intraday high-frequency data exhibits higher power than the conventional approach based on daily data. Originality/value This is the first paper to investigate Japan’s oil market using RV. The authors conclude that the approach based on RV is universally adoptable when testing for Granger causality in variance.

Sign in / Sign up

Export Citation Format

Share Document