Forecasting Aluminum Prices with Commodity Currencies

Pincheira, Pablo; Hardy, Nicolás

doi:10.2139/ssrn.3511564

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…Using a Bayesian graphical SVAR approach (BGSVAR, see [40]), they find some evidence of Granger causality mainly from the global stock markets (and some individual markets) IV over the BRICS IV (notably, they find that the commodity market is important exclusively in South Africa. One possible explanation for this result is the strong relationship between major exporting economies and global commodity Complexity prices, extensively reported in [41][42][43][44][45]). is result is somewhat consistent with previous evidence relating to global factors and BRICS stock markets [46].…”

Section: Realized Volatility Forecasting Methodologiesmentioning

confidence: 90%

The Volatility Forecasting Power of Financial Network Analysis

et al. 2020

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This investigation connects two crucial economic and financial fields, financial networks, and forecasting. From the financial network’s perspective, it is possible to enhance forecasting tools, since econometrics does not incorporate into standard economic models, second-order effects, nonlinearities, and systemic structural factors. Using daily returns from July 2001 to September 2019, we used minimum spanning tree and planar maximally filtered graph techniques to forecast the stock market realized volatility of 26 countries. We test the predictive power of our core models versus forecasting benchmarks models in and out of the sample. Our results show that the length of the minimum spanning tree is relevant to forecast volatility in European and Asian stock markets, improving forecasting models’ performance. As a new contribution, the evidence from this work establishes a road map to deepening the understanding of how financial networks can improve the quality of prediction of financial variables, being the latter, a crucial factor during financial shocks, where uncertainty and volatility skyrocket.

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Section: Realized Volatility Forecasting Methodologiesmentioning

confidence: 90%

The Volatility Forecasting Power of Financial Network Analysis

et al. 2020

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Short-Term Forecasting of Global Energy and Metal Prices: VAR and VECM Approaches

Balioz

2022

VNBU

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This study introduces a set of multivariate models with the aim of forecasting global prices of 1) crude oil, 2) natural gas, 3) iron ore, and 4) steel. Various versions of vector autoregression and error-correction models are applied to monthly data for the short-term prediction of nominal commodity prices six months ahead, and to examine forecast accuracy. The fundamentals for metal and energy price predictions include inter alia, stock changes, changes in commodity production volumes, export volumes by the largest players, changes in the manufacturing sector of the largest consumers, the state of global real economic activity, freight rates, recession, and so on. Kilian's (2009) index of global real economic activity is found to be a useful proxy for global demand and a reliable input in forecasting both energy and metal prices. The findings suggest that models with smaller lag orders tend to outperform those with a higher number of lags. At the same time, selected individual models, while showing a standalone high performance, have varying forecast precision during different periods, and no individual model outperforms others consistently throughout the forecast horizon.

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Forecasting Aluminum Prices with Commodity Currencies

Cited by 2 publications

References 8 publications

The Volatility Forecasting Power of Financial Network Analysis

The Volatility Forecasting Power of Financial Network Analysis

Short-Term Forecasting of Global Energy and Metal Prices: VAR and VECM Approaches

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