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A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Financial Markets".

Deadline for manuscript submissions: closed (1 June 2023) | Viewed by 76007

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Prof. Dr. Kentaro Iwatsubo

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Guest Editor

Graduate School of Economics, Kobe University, 2-1, Rokkodai, Nada-Ku, Kobe 657-8501, Japan
Interests: market microstructure; empirical finance; international finance

Special Issue Information

Dear Colleagues,

As an active researcher in the field of commodity market finance, we would like to draw your attention to the Special Issue of the Journal of Risk and Financial Management (JRFM) on "Commodity Market Finance” that we are guest editing.

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 markets and the financial markets and has led to a great deal of attention being paid to the commodity markets by governments, companies, and investors.

This Special Issue aims to cover recent developments in research on commodity market finance. We seek contributions in empirical research on commodity markets including, but not limited to, derivative pricing and valuation, risk and volatility, microstructure and efficiency, forecasting, market interrelationships, financialization, and interactions between commodity markets and the real economy. The markets of interest include those for industrial and precious metals, energy, agricultural products and livestock, and other commodities such as rubber.

Please check the following website for more information:

If you would like to contribute, please submit your manuscript prior to the deadline of 31 December 2022. Please contact us if you would like to submit a manuscript but are unable to meet the deadline. Instructions for authors are available on the journal website (https://www.mdpi.com/journal/jrfm/instructions). Submitted papers should not have been published previously, nor be under consideration for publication elsewhere.

Prof. Dr. Kentaro Iwatsubo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

commodities
financialization
volatility
COVID-19
energy
metals

Published Papers (11 papers)

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Research

13 pages, 1456 KiB

Open AccessArticle

A Cyclical Phenomenon among Stock & Commodity Markets

by Hector O. Zapata, Junior E. Betanco, Maria Bampasidou and Michael Deliberto

J. Risk Financial Manag. 2023, 16(7), 320; https://doi.org/10.3390/jrfm16070320 - 04 Jul 2023

Cited by 2 | Viewed by 3496

Abstract

Considerable studies have examined the relationship between commodity markets and stock markets. This paper studies the cyclical relationship between commodity markets and stock markets with implications for investing based on index relationships. Stock markets are represented by the U.S. S&P 500 index and aggregate commodity markets by the U.S. producer price index (PPI). Tradeable market indexes readily available to investors, namely the S&P GSCI Index and the Bloomberg Commodity Index (BCOM), are also studied. An optimal bandpass filter is used to estimate the cyclical component using a pricing-performance measure of the S&P 500 relative to the PPI, based on annual data from 1871 to 2022. The S&P GSCI and the BCOM indexes are also used to test the robustness of the findings. The impacts of the financial crisis of 2008 and the coronavirus pandemic are also assessed. The overriding conclusion of the study is that a cyclical relationship exists between stock markets and commodity markets for both aggregate and tradeable indexes which can last, from peak to peak, approximately 31 years. Measuring returns and risks in a manner consistent with these cycles can shed new light on the usefulness of commodity investing via tradeable indexes in seeking efficient portfolios. Full article

(This article belongs to the Special Issue Commodity Market Finance)

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25 pages, 4561 KiB

Open AccessArticle

Do Large Datasets or Hybrid Integrated Models Outperform Simple Ones in Predicting Commodity Prices and Foreign Exchange Rates?

by Jin Shang and Shigeyuki Hamori

J. Risk Financial Manag. 2023, 16(6), 298; https://doi.org/10.3390/jrfm16060298 - 09 Jun 2023

Viewed by 1690

Abstract

With the continuous advancement of machine learning and the increasing availability of internet-based information, there is a belief that these approaches and datasets enhance the accuracy of price prediction. However, this study aims to investigate the validity of this claim. The study examines the effectiveness of a large dataset and sophisticated methodologies in forecasting foreign exchange rates (FX) and commodity prices. Specifically, we employ sentiment analysis to construct a robust sentiment index and explore whether combining sentiment analysis with machine learning surpasses the performance of a large dataset when predicting FX and commodity prices. Additionally, we apply machine learning methodologies such as random forest (RF), eXtreme gradient boosting (XGB), and long short-term memory (LSTM), alongside the classical statistical model autoregressive integrated moving average (ARIMA), to forecast these prices and compare the models’ performance. Based on the results, we propose novel methodologies that integrate wavelet transformation with classical ARIMA and machine learning techniques (seasonal-decomposition-ARIMA-LSTM, wavelet-ARIMA-LSTM, wavelet-ARIMA-RF, wavelet-ARIMA-XGB). We apply this analysis procedure to the commodity gold futures prices and the euro foreign exchange rates against the US dollar. Full article

(This article belongs to the Special Issue Commodity Market Finance)

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23 pages, 3027 KiB

Open AccessArticle

Pricing Multi-Asset Bermudan Commodity Options with Stochastic Volatility Using Neural Networks

by Kentaro Hoshisashi and Yuji Yamada

J. Risk Financial Manag. 2023, 16(3), 192; https://doi.org/10.3390/jrfm16030192 - 12 Mar 2023

Viewed by 1286

Abstract

It has been recognized that volatility in commodity markets fluctuates significantly depending on the demand–supply relationship and geopolitical risk, and that risk and financial management using multivariate derivatives are becoming more important. This study illustrates an application of multi-layered neural networks for multi-dimensional Bermudan option pricing problems assuming a multi-asset stochastic volatility model in commodity markets. In addition, we aim to identify continuation value functions for these option pricing problems by implementing smooth activation functions in the neural networks and evaluating their accuracy compared with other activation functions or regression techniques. First, we express the underlying asset dynamics using the multi-asset stochastic volatility model with mean reversion properties in the commodity market and formulate the multivariate Bermudan commodity option pricing problem. Subsequently, we apply multi-layer perceptrons in the neural network to represent the continuation value functions of Bermudan commodity options, wherein the entire neural network is trained using the least-squares Monte Carlo simulation method. Finally, we perform numerical experiments and demonstrate that applications of neural networks for Bermudan options in a multi-dimensional commodity market achieve sufficient accuracy with regard to various aspects, including changing the exercise dates, the number of layers/neurons, and the dimension of the problem. Full article

(This article belongs to the Special Issue Commodity Market Finance)

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20 pages, 2623 KiB

Open AccessArticle

Dynamic Relationship between Volatility Risk Premia of Stock and Oil Returns

by Nobuhiro Nakamura, Kazuhiko Ohashi and Daisuke Yokouchi

J. Risk Financial Manag. 2023, 16(3), 173; https://doi.org/10.3390/jrfm16030173 - 05 Mar 2023

Viewed by 1577

Abstract

This study investigates the relationship between the volatility risk premia (VRP) of stock and oil returns. Using daily data on VRP from 10 May 2007 to 16 May 2017, VAR analyses on the stock and oil VRP are conducted, and it is found that the effects of the stock VRP on the oil VRP are limited and, if any, short-lived. In contrast, the VRP of oil has significantly positive and long-lasting effects on the stock VRP after the financial crisis. These results suggest that investors’ sentiments (measured by VRP) are transmitted from the oil to the stock market over time, but not vice versa. This is unexpected because the financialization of commodities means a massive increase in investment in commodities by investors in the traditional stock and bond markets; hence, the direction of effects is thought to be from the stock to the commodity market. Full article

(This article belongs to the Special Issue Commodity Market Finance)

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20 pages, 1352 KiB

Open AccessArticle

Markov-Regime Switches in Oil Markets: The Fear Factor Dynamics

by Hiroyuki Okawa

J. Risk Financial Manag. 2023, 16(2), 67; https://doi.org/10.3390/jrfm16020067 - 23 Jan 2023

Cited by 2 | Viewed by 2490

Abstract

This paper is an attempt to examine regime switches in the empirical relation between return dynamics and implied volatility in energy markets. The time-varying properties of the return-generating process are defined as a function of several risk factors, including oil market volatility and changes in stock prices and currency rates. The empirical evidence is based on Markov-regime switching models, which have the capacity to capture, in particular, the stochastic behavior of the OVX oil volatility index as a benchmark for investors’ fear. The results suggest that the dynamics of oil market returns are governed by two distinct regimes, a state driven by a negative relationship between returns and implied volatility and another state characterized by a more pronounced negative correlation. It is the latter regime with a stronger correlation that tends to prevail over the sample period from 2008 to 2021, but the frequency of regime shifts also seems to increase under more volatile oil price dynamics in association with significant events such as the COVID-19 pandemic. Thus, the evidence of a negative correlation structure is found to be robust to changes in the estimation period, which suggests that the oil volatility index remains a reliable gauge of market sentiment in the energy markets. Full article

(This article belongs to the Special Issue Commodity Market Finance)

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25 pages, 6964 KiB

Open AccessArticle

Analysis of Bitcoin Price Prediction Using Machine Learning

by Junwei Chen

J. Risk Financial Manag. 2023, 16(1), 51; https://doi.org/10.3390/jrfm16010051 - 13 Jan 2023

Cited by 16 | Viewed by 41123

Abstract

The research purpose of this paper is to obtain an algorithm model with high prediction accuracy for the price of Bitcoin on the next day through random forest regression and LSTM, and to explain which variables have influence on the price of Bitcoin. There is much prior literature on Bitcoin price prediction research, and the research methods mainly revolve around the ARMA model of time series and the LSTM algorithm of deep learning. Although it cannot be proved by the Diebold–Mariano test that the prediction accuracy of random forest regression is significantly better than that of LSTM, the prediction errors RMSE and MAPE of random forest regression are better than those of LSTM. The changes in the variables that determine the price of Bitcoin in each period are also obtained through random forest regression. From 2015 to 2018, three US stock market indexes, NASDAQ, DJI, and S&P500 and oil price, and ETH price have impact on Bitcoin prices. Since 2018, the important variables have become ETH price and Japanese stock market index JP225. The relationship between accuracy and the number of periods of explanatory variables brought into the model shows that for predicting the price of Bitcoin for the next day, the model with only one lag of the explanatory variables has the best prediction accuracy. Full article

(This article belongs to the Special Issue Commodity Market Finance)

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15 pages, 2880 KiB

Open AccessArticle

On the Risk Spillover from Bitcoin to Altcoins: The Fear of Missing Out and Pump-and-Dump Scheme Effects

by Mehmet Balcilar and Huseyin Ozdemir

J. Risk Financial Manag. 2023, 16(1), 41; https://doi.org/10.3390/jrfm16010041 - 09 Jan 2023

Cited by 4 | Viewed by 3154

Abstract

This article examines the asymmetric volatility spillover effects between Bitcoin and alternative coin markets at the disaggregate level. We apply a frequency connectedness approach to the daily data of 11 major cryptocurrencies for the period from 1 September 2017 to 2 March 2022. We try to uncover the existence of the “fear of missing out” psychological effect and “pump-and-dump schemes” in the crypto markets. To do that, we estimate the volatility spillovers from Bitcoin to altcoin and the cryptos’ own risk spillovers during bull and bear markets. The spillover results from Bitcoin to altcoin provide mixed results regarding the presence of this theory for major cryptocurrencies. However, the empirical findings carried out by the cryptos’ own spillover effects fully confirm the existence of a fear-of-missing-out effect and pump-and-dump schemes in all cryptocurrencies except for USDT. Full article

(This article belongs to the Special Issue Commodity Market Finance)

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18 pages, 4094 KiB

Open AccessArticle

Analysis of the Impact of COVID-19 Pandemic on the Intraday Efficiency of Agricultural Futures Markets

by Faheem Aslam, Paulo Ferreira and Haider Ali

J. Risk Financial Manag. 2022, 15(12), 607; https://doi.org/10.3390/jrfm15120607 - 15 Dec 2022

Cited by 3 | Viewed by 1764

Abstract

The investigation of the fractal nature of financial data has been growing in the literature. The purpose of this paper is to investigate the impact of the COVID-19 pandemic on the efficiency of agricultural futures markets by using multifractal detrended fluctuation analysis (MF-DFA). To better understand the relative changes in the efficiency of agriculture commodities due to the pandemic, we split the dataset into two equal periods of seven months, i.e., 1 August 2019 to 10 March 2020 and 11 March 2020 to 25 September 2020. We used the high-frequency data at 15 min intervals of cocoa, cotton, coffee, orange juice, soybean, and sugar. The findings reveal that the COVID-19 pandemic has great but varying impacts on the intraday multifractal properties of the selected agricultural future markets. In particular, the London sugar witnessed the lowest multifractality while orange juice exhibited the highest multifractality before the pandemic declaration. Cocoa became the most efficient while the cotton exhibited the minimum efficient pattern after the pandemic. Our findings show that the highest improvement is found in the market efficiency of orange juice. Furthermore, the behavior of these agriculture commodities shifted from a persistent to an antipersistent behavior after the pandemic. The information given by the detection of multifractality can be used to support investment and policy-making decisions. Full article

(This article belongs to the Special Issue Commodity Market Finance)

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17 pages, 6525 KiB

Open AccessArticle

Causality between Arbitrage and Liquidity in Platinum Futures

by Kentaro Iwatsubo and Clinton Watkins

J. Risk Financial Manag. 2022, 15(12), 593; https://doi.org/10.3390/jrfm15120593 - 09 Dec 2022

Viewed by 1525

Abstract

Arbitrage and liquidity are interrelated. Liquidity facilitates arbitrageurs’ trading on deviations from the law of one price. However, whether arbitrage opportunity leads to an increase or decrease in liquidity depends on the cause of the deviation. A demand shock leads to greater liquidity, while asymmetric information is toxic to liquidity. We examine how arbitrage and liquidity influence each other in the world’s largest platinum futures markets on exchanges in New York and Tokyo. The markets provide an interesting institutional setting because the futures are based on an identical underlying commodity but exhibit different liquidity characteristics both intraday and over their lifespans. Using intraday data, we find that deviation in currency-adjusted futures prices leads, on average, to an immediate increase in liquidity, suggesting that demand shocks are the dominant driver of arbitrage opportunities. Less actively traded futures experience a greater liquidity effect. Arbitrageurs improve liquidity in both New York and Tokyo by acting as discretionary liquidity traders and cross-sectional market-makers. Full article

(This article belongs to the Special Issue Commodity Market Finance)

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26 pages, 2984 KiB

Open AccessArticle

Oil Price Uncertainty Shocks and Global Equity Markets: Evidence from a GVAR Model

by Afees A. Salisu, Rangan Gupta and Riza Demirer

J. Risk Financial Manag. 2022, 15(8), 355; https://doi.org/10.3390/jrfm15080355 - 09 Aug 2022

Cited by 3 | Viewed by 2158

Abstract

This paper examines the propagation of oil price uncertainty shocks to real equity prices using a large-scale Global Vector Autoregressive (GVAR) model of 26 advanced and emerging stock markets. The GVAR framework allows us to capture the transmission of local and global shocks, while simultaneously accounting for individual-country peculiarities. Utilising a recently developed model-free, robust estimate of oil price uncertainty, we document a statistically significant and negative effect of uncertainty shocks emanating from oil prices on the large majority of global stock markets, with the adverse effect of oil price uncertainty shocks found to be stronger for emerging economies as well as net oil-exporting nations. Interestingly, however, global stock markets exhibit a great deal of heterogeneity in their recovery following oil uncertainty shocks as some experience rapid corrections in stock valuations while others suffer from extended slumps. While the results are sensitive to the oil uncertainty measure utilised, they suggest that country diversification in the face of rising oil market uncertainty can still be beneficial for global investors as global stock markets exhibit a rather heterogeneous pattern in their recovery rates against oil market shocks. Full article

(This article belongs to the Special Issue Commodity Market Finance)

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20 pages, 2892 KiB

Open AccessArticle

The Impacts of the Russia–Ukraine Invasion on Global Markets and Commodities: A Dynamic Connectedness among G7 and BRIC Markets

by Md. Kausar Alam, Mosab I. Tabash, Mabruk Billah, Sanjeev Kumar and Suhaib Anagreh

J. Risk Financial Manag. 2022, 15(8), 352; https://doi.org/10.3390/jrfm15080352 - 08 Aug 2022

Cited by 58 | Viewed by 14091

Abstract

The conflict between Russia and Ukraine has been causing knock-on effects worldwide. The supply and price of major commodity markets (oil, gas, platinum, gold, and silver) have been greatly impacted. Due to the ongoing conflict, financial markets across the world have experienced a strong dynamic regarding commodities prices. This effect can be considered the biggest change since the occurrence of the financial crisis in the year 2008, which explicitly influenced the oil and gold markets. This study attempts to investigate the impacts of the Russian invasion crisis on the dynamic connectedness among five commodities and the G7 and BRIC (leading stock) markets. We have applied the time-varying parameter vector autoregressive (TVP-VAR) method, which reflects the way spillovers are shaped by various crises periods, and we found extreme connectedness among all commodities and markets (G7 and BRIC). The findings show that gold and silver (commodities) and the United States, Canada, China, and Brazil (stock markets) are the receivers from the rest of the commodities/market’s transmitters of shocks during this invasion crisis. This research has policy implications that could be beneficial to commodity and stock investors, and these implications could guide them to make many decisions about investment in such tumultuous situations. Policymakers, institutional investors, bankers, and international organizations are the possible beneficiaries of these policy decisions. Full article

(This article belongs to the Special Issue Commodity Market Finance)

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