How Commodity Investments Can Help a Portfolio

There are several benefits that commodities offer to a portfolio manager as a potential long-term investment option. Some of these points are highlighted below:

  • Commodities have negative correlation with other markets such as equities and bonds, which makes it a suitable choice for diversifying the portfolio.
  • In the past, both equities have exhibited similar risk-return profiles. There were positive returns from all the three markets – equity, bonds, and commodities – with commodities showing similar volatilities as equities. One can achieve overall risk reduction.
  • Return-timing diversification: Over the long periods, the returns of the portfolios tend to accrue in short periods. Financial assets perform poorly during economic recover (later periods), and during periods of inflation shock and contractionary monetary policy. During these times commodities tend to perform better. This provides the benefit of return-timing diversification.
  • Long commodity investments also act as a natural hedge for inflation exposure in long-term liabilities. It may not cover the entire inflation risk but a major part of it. This happens because commodity prices have high correlation with inflation. These commodities reflect prices in energy, metal, and agricultural commodities which are a component in calculating Consumer price Index.  Commodities are also effective in hedging unexpected inflation.

Data Science in Finance: 9-Book Bundle

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.