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2020 | OriginalPaper | Buchkapitel

6. How to Forecast Commodity Price Movements: Time Series Models

verfasst von : Lingjie Ma

Erschienen in: Quantitative Investing

Verlag: Springer International Publishing

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Abstract

In this chapter, we focus on commodity pricing and investment with time series models. For stock selection strategies, the ability to forecast individual stock returns is critical and usually relies on company-level factors, such as profitability, management quality, etc. In commodity investing, a deep understanding of the geopolitical dynamics and identification of macro-level factors are very important for a successful strategy. A stock selection strategy requires cross-sectional analysis, while commodity investing requires time series analysis. In this chapter, we get into details about the special features of time series models, introduce the concepts of unit root, spurious relationship, and cointegration, and show how they can be employed for quantitative investing in crude oil and pair trading.

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Fußnoten
1
For a detailed discussion about R packages, please refer the R section at the end of this chapter.
 
2
Proven oil reserves data come from the BP Statistical Review of World Energy June 2018 report. Reserves as a share of world total reserves and oil production in barrels also come from BP’s report.
 
3
The price of gold is based on the London PM price, measured by USD per ounce.
 
4
We will discuss the combination of fundamental and quantitative analysis in detail in Chap. 9.
 
Literatur
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Metadaten
Titel
How to Forecast Commodity Price Movements: Time Series Models
verfasst von
Lingjie Ma
Copyright-Jahr
2020
DOI
https://doi.org/10.1007/978-3-030-47202-3_6