Reprint

Commodity Market Finance

Edited by
October 2023
234 pages
  • ISBN978-3-0365-9029-5 (Hardback)
  • ISBN978-3-0365-9028-8 (PDF)

This book is a reprint of the Special Issue Commodity Market Finance that was published in

Business & Economics
Computer Science & Mathematics
Summary

Commodity markets have evolved substantially since the early 2000s and have become more financialized. The recent cold war between the U.S.A. and China, the outbreak of COVID-19, and Russia's invasion of Ukraine have caused resource prices to soar, leading to greater volatility in the commodity markets. The volatility of the commodity markets has increased, and at the same time, financial markets such as the stock market, bond market, and foreign exchange market have become unstable. This has increased the linkage between the commodity and financial markets and has led to a great deal of attention being paid to the commodity markets by governments, companies, and investors.This reprint delves into recent developments in the commodity markets and elucidates the multifaceted factors that have shaped their trajectory. It examines how the interwoven dynamics of supply and demand, geopolitics, technology, and financialization have brought about a new era in commodity trading. By providing a comprehensive survey of these developments, we aim to provide insights that will help stakeholders successfully navigate the challenges and opportunities presented by this evolving landscape.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
Russia and Ukraine conflict; commodities; G7 and BRIC markets; TVP-VAR; connectedness; oil price uncertainty shocks; international equity markets; global vector autoregressive model; arbitrage; efficiency; futures; liquidity; market integration; platinum; COVID-19; pandemic; agriculture; commodity; MF-DFA; high frequency; efficiency; asymmetric volatility spillover; bitcoin; altcoin; cryptocurrency; frequency connectedness; Bitcoin; machine learning; random forest regression; LSTM; energy market volatility; oil price dynamics; fear index; Markov-regime switching models; volatility risk premium (VRP); implied and realized volatility; oil and stock returns; financialization; Bermudan commodity options; multi-layer perceptron; multi-asset stochastic volatility model; hybrid forecasting approaches; two-step forecasting approaches; gold; euro; sentiment analysis; machine learning; ARIMA; wavelet transformation; seasonal decomposition; long short-term memory; random forest; eXtreme gradient boosting; commodity; stock; markets; cycles; investing; risk; returns