The effects of uncertainty measures on commodity prices from a time-varying perspective

2021 ◽  
Vol 71 ◽  
pp. 100-114 ◽  
Author(s):  
Jianbai Huang ◽  
Yingli Li ◽  
Hongwei Zhang ◽  
Jinyu Chen
Keyword(s):  
Commodity Prices ◽  
Time Varying ◽  
Uncertainty Measures

scholarly journals A Time-Varying Gerber Statistic: Application of a Novel Correlation Metric to Commodity Price Co-Movements

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 339-354
Author(s):  
Bernardina Algieri ◽  
Arturo Leccadito ◽  
Pietro Toscano
Keyword(s):  
Commodity Prices ◽  
Commodity Price ◽  
Time Varying ◽  
Conditional Correlation ◽  
Forecasting Accuracy ◽  
Novel Approach ◽  
Correlation Models ◽  
Rolling Window ◽  
Auto Regressive ◽  
Energy Products

This study investigates the daily co-movements in commodity prices over the period 2006–2020 using a novel approach based on a time-varying Gerber correlation. The statistic is computed considering a set of probabilities estimated via non-traditional models that give a time-varying structure to the measure. The results indicate that there are several co-movements across commodities, that these co-movements change over time, and that they are tendentially positive. Conditional auto-regressive multithreshold logit models show higher forecasting accuracy for agricultural returns, while dynamic conditional correlation models are more accurate for energy products and metals. The proposed models are shown to be superior in terms of forecasting power to the benchmark method which is based on estimating the Gerber correlation moving a rolling window.

scholarly journals Heavy Metals: Might as Well Jump

2019 ◽  
Vol 7 (2) ◽  
pp. 33
Author(s):  
Neil A. Wilmot
Keyword(s):  
Heavy Metal ◽  
Commodity Prices ◽  
Time Varying ◽  
Fat Tails ◽  
Times Series ◽  
Complex Processes ◽  
Price Series ◽  
New Information ◽  
Complex Process ◽  
Rapid Changes

Financial times series, and commodity prices in particular, are known to exhibit fat tails in the distribution of prices. As with many natural resources price series, the arrival of new information can lead to unexpectedly rapid changes—or jump—in prices. This suggests that natural resource commodity prices should follow a more complex process than geometric Brownian motion (GBM), which is linked to the Gaussian distribution. The presence of jumps (discontinuities) in several heavy metal price series is investigated, as well as time-varying volatility. The results demonstrate that allowing for jumps and time-varying volatility provides statistically important improvements in the modelling or prices, relative to GBM. These complex processes contributed to the fatness of the tails in the distribution of heavy metal price returns.

scholarly journals Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors

2020 ◽  
Vol 102 (1) ◽  
pp. 17-33 ◽  
Author(s):  
Todd E. Clark ◽  
Michael W. McCracken ◽  
Elmar Mertens
Keyword(s):  
Stochastic Volatility ◽  
Survey Data ◽  
Stochastic Volatility Model ◽  
Forecast Errors ◽  
Time Varying ◽  
Survey Of Professional Forecasters ◽  
Macroeconomic Variables ◽  
Uncertainty Measures ◽  
Volatility Model ◽  
Estimate Uncertainty

We estimate uncertainty measures for point forecasts obtained from survey data, pooling information embedded in observed forecast errors for different forecast horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon specification of stochastic volatility. We apply our method to forecasts for various macroeconomic variables from the Survey of Professional Forecasters. Compared to simple variance approaches, our stochastic volatility model improves the accuracy of uncertainty measures for survey forecasts.

TIME-VARYING MARKET PRICE OF RISK AND AUTOREGRESSIVE ERROR STRUCTURE OF OIL PRICES

2020 ◽  
Vol 38 (1) ◽  
Author(s):  
Carla Gomes Costa de Souza ◽  
Fernando Antonio Lucena Aiube
Keyword(s):  
Market Price ◽  
Commodity Prices ◽  
Factor Models ◽  
Time Varying ◽  
Market Price Of Risk ◽  
Error Structure ◽  
Price Of Risk ◽  
Different Types ◽  
Oil Contracts ◽  
Smith Model

In this paper we investigate the inclusion of a time-varying market price of risk in oil price factor models. Additionally an autoregressive error structure is adopted to filter this property of financial series. We use the Schwartz and Smith model, which is well established in the literature on commodity prices. The analysis is easily extended to different types of factor models. The empirical application considered the future oil contracts traded on the NYMEX. We find that considering a time-varying market price of risk and the autoregressive structure improves the fit of the empirical data.

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.

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