Time-varying correlation between agricultural commodity and energy price dynamics with Bayesian multivariate DCC-GARCH models

2019 ◽  
Vol 526 ◽  
pp. 120807 ◽  
Author(s):  
Yegnanew A. Shiferaw
Keyword(s):  
Garch Models ◽  
Energy Price ◽  
Price Dynamics ◽  
Time Varying ◽  
Agricultural Commodity

scholarly journals Price Volatility for Selected Agricultural Commodities in Ethiopia: Evidence from GARCH Models

2021 ◽  
Vol 18 ◽  
pp. 1380-1388
Author(s):  
Tirngo Dinku ◽  
Worku Gardachw ◽  
Ngozi Adeleye
Keyword(s):  
Commodity Prices ◽  
Price Volatility ◽  
Bad News ◽  
Good News ◽  
Garch Models ◽  
Time Varying ◽  
Agricultural Commodities ◽  
Agricultural Commodity ◽  
Distributional Assumption ◽  
Egarch Model

This study models the volatility of returns for selected agricultural commodity prices in Ethiopia using the generalized autoregressive conditional heteroskedasticity (GARCH) approach. GARCH family models, specifically threshold GARCH and exponential GARCH were employed to analyze the time varying volatility of selected agricultural commodities prices from 2010 to 2021. The data analysis results revealed that, out of the GARCH specifications, the EGARCH model with the normal distributional assumption of residuals was a better fit model for the price volatility of “teff” and “red pepper” in which their return series reacted differently to the “good” and “bad” news. The study indicated the existence of a leverage effect, which implied that the “bad” news could have a larger effect on volatility than the “good” news of the same magnitude, and the asymmetric term was statistically significant.

QMLE of periodic time-varying bilinear– GARCH models

2018 ◽  
Vol 48 (13) ◽  
pp. 3291-3310
Author(s):  
Abdelouahab Bibi ◽  
Ahmed Ghezal
Keyword(s):  
Garch Models ◽  
Time Varying

scholarly journals Time-Varying Spot and Futures Oil Price Dynamics

2010 ◽  
Author(s):  
Guglielmo Maria Caporale ◽  
Davide Ciferri ◽  
Alessandro Girardi
Keyword(s):  
Oil Price ◽  
Price Dynamics ◽  
Time Varying

scholarly journals Asymptotics of Cholesky GARCH models and time-varying conditional betas

2018 ◽  
Vol 204 (2) ◽  
pp. 223-247 ◽  
Author(s):  
Serge Darolles ◽  
Christian Francq ◽  
Sébastien Laurent
Keyword(s):  
Garch Models ◽  
Time Varying ◽  
Conditional Betas

scholarly journals Covid-19 and the Technology Bubble 2.0: Evidence from DCC-MGARCH and Wavelet Approaches

2021 ◽  
pp. 109-127
Author(s):  
Caner Özdurak ◽  
Cengiz Karataş
Keyword(s):  
Time Scale ◽  
Jel Classification ◽  
Garch Models ◽  
Time Varying ◽  
Technology Stocks ◽  
Technology Bubble ◽  
Dot Com ◽  
As If

There has probably never been as big a divergence between markets and economies as there is in the pandemic period. This paper is an attempt to test the ‘time-varying’ and ‘time-scale dependent’ volatilities of major technology stocks, FAANG and Microsoft, for analyzing the possibility of a second technology bubble in the markets. Consistent with the results of DCC-GARCH models, our analysis based on the application of the Wavelet approach also indicates that major technology behave and move as if they were all one stock in the pandemic period which makes us to be cautious about a second dotcom crisis since %26 of S&P 500 market cap is driven by FAANG and Microsoft stocks. JEL classification numbers: C58, D53, O14. Keywords: Dot-com crisis, tech bubble, DCC-GARCH, FAANG, Wavelet.

scholarly journals Hybrid CUSUM Change Point Test for Time Series with Time-Varying Volatilities Based on Support Vector Regression

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 578
Author(s):  
Sangyeol Lee ◽  
Chang Kyeom Kim ◽  
Sangjo Lee
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Stock Price ◽  
Real Data ◽  
Support Vector ◽  
Garch Models ◽  
Time Varying ◽  
Tuning Parameters ◽  
Scope Of Application ◽  
Validation Set

This study considers the problem of detecting a change in the conditional variance of time series with time-varying volatilities based on the cumulative sum (CUSUM) of squares test using the residuals from support vector regression (SVR)-generalized autoregressive conditional heteroscedastic (GARCH) models. To compute the residuals, we first fit SVR-GARCH models with different tuning parameters utilizing a time series of training set. We then obtain the best SVR-GARCH model with the optimal tuning parameters via a time series of the validation set. Subsequently, based on the selected model, we obtain the residuals, as well as the estimates of the conditional volatility and employ these to construct the residual CUSUM of squares test. We conduct Monte Carlo simulation experiments to illustrate its validity with various linear and nonlinear GARCH models. A real data analysis with the S&P 500 index, Korea Composite Stock Price Index (KOSPI), and Korean won/U.S. dollar (KRW/USD) exchange rate datasets is provided to exhibit its scope of application.

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