scholarly journals Commodity Price Forecasts, Futures Prices and Pricing Models

2016 ◽  
Author(s):  
Gonzalo Cortazar ◽  
Cristobal Millard ◽  
Hector Ortega ◽  
Eduardo Schwartz
Keyword(s):  
Commodity Price ◽  
Futures Prices ◽  
Pricing Models

scholarly journals Commodity Price Forecasts, Futures Prices, and Pricing Models

2019 ◽  
Vol 65 (9) ◽  
pp. 4141-4155 ◽  
Author(s):  
Gonzalo Cortazar ◽  
Cristobal Millard ◽  
Hector Ortega ◽  
Eduardo S. Schwartz
Keyword(s):  
Term Structure ◽  
Commodity Price ◽  
Futures Prices ◽  
Pricing Models ◽  
Medium Term ◽  
Oil Futures ◽  
New Approach ◽  
Commodity Pricing ◽  
Spot Prices

Even though commodity-pricing models have been successful in fitting the term structure of futures prices and its dynamics, they do not generate accurate true distributions of spot prices. This paper develops a new approach to calibrate these models using not only observations of oil futures prices, but also analysts’ forecasts of oil spot prices. We conclude that to obtain reasonable expected spot curves, analysts’ forecasts should be used, either alone or jointly with futures data. The use of both futures and forecasts, instead of using only forecasts, generates expected spot curves that do not differ considerably in the short/medium term, but long term estimations are significantly different. The inclusion of analysts’ forecasts in addition to futures, instead of only futures prices, does not alter significantly the short/medium part of the futures curve but does have a significant effect on long-term futures estimations. This paper was accepted by Gustavo Manso, finance.

Financialization of Commodity Markets

2018 ◽  
Author(s):  
Kyle J. Putnam
Keyword(s):  
Business Cycle ◽  
Futures Markets ◽  
Commodity Price ◽  
Commodity Markets ◽  
Commodity Futures ◽  
Futures Prices ◽  
Price Behavior ◽  
Risk And Return ◽  
Commodity Futures Markets ◽  
Asset Class

In the early 2000s, financial investors began pouring billions of dollars into the commodity futures markets seeking the unique investment benefits of this distinct asset class. This “financialization” process has called into question the fundamental risk and return properties of commodity futures as evidence has emerged favoring the idea that the massive increase in investor flows caused a rise in futures prices, volatility, and intra- and intermarket return correlations. However, a contrarian line of research contends that the effects of the new “speculative” capital on the futures markets are unsubstantiated and the increased participation of financial investors poses little consequence to the economics of the marketplace. This latter line of literature maintains that the investment benefits of commodity futures have not been diminished and that fundamental factors and business cycle variations can explain the observed changes in commodity price behavior.

scholarly journals Determinants of Commodity Futures Prices: Decomposition Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Emmanuel Antwi ◽  
Emmanuel N. Gyamfi ◽  
Kwabena Kyei ◽  
Ryan Gill ◽  
Anokye M. Adam
Keyword(s):  
Correlation Coefficient ◽  
Market Price ◽  
Low Frequency ◽  
Rank Correlation ◽  
Commodity Price ◽  
Commodity Futures ◽  
Futures Prices ◽  
Special Events ◽  
Statistical Measures ◽  
Mode Decomposition

Developing models to analyze time series is a very sophisticated, time-consuming, but interesting experience for researchers. Commodity price component determination is challenging due to remarkable price volatility, uncertainty, and complexity in the futures market. This study aims to determine the components that drive the market price of commodity futures. This study utilized the decomposition methods, empirical mode decomposition (EMD), and variational mode decomposition (VMD), to analyze three commodity futures prices data: corn from agricultural products, crude oil from energy, and gold from industrial metal. We applied these techniques to decompose the daily data of each commodity price from different periods and frequencies into individual intrinsic mode functions for EMD and modes for VMD. We used the hierarchical clustering method and Euclidean distance approach to classify the IMFs and modes into high-frequency, low-frequency, and trend. Next, applying statistical measures, particularly, the Pearson product-moment correlation coefficient, Kendall rank correlation, and Spearman rank correlation coefficient, we observed that the trend and low-frequency parts of the market price are the main drivers of commodity futures markets’ price fluctuations. The low-frequencies are caused by special events. In a nutshell, commodity futures prices are affected by economic development rather than short-lived market variations caused by ordinary disequilibrium of supply-demand.

scholarly journals Economic Criteria for Evaluating Commodity Price Forecasts

1997 ◽  
Vol 29 (2) ◽  
pp. 337-345 ◽  
Author(s):  
Jeffrey H. Dorfman ◽  
Christopher S. Mcintosh
Keyword(s):  
Asset Price ◽  
Asset Returns ◽  
Commodity Price ◽  
Forecast Evaluation ◽  
Futures Prices ◽  
Economic Time Series ◽  
Trading System ◽  
Monte Carlo Techniques ◽  
Economic Time ◽  
Economic Criteria

AbstractForecasts of economic time series are often evaluated according to their accuracy as measured by either quantitative precision or qualitative reliability. We argue that consumers purchase forecasts for the potential utility gains from utilizing them, not for their accuracy. Using Monte Carlo techniques to incorporate the temporal heteroskedasticity inherent in asset returns, the expected utility of a set of qualitative forecasts is simulated for corn and soybean futures prices. Monetary values for forecasts of various reliability levels are derived. The method goes beyond statistical forecast evaluation, allowing individuals to incorporate their own utility function and trading system into valuing a set of asset price forecasts.

COMMODITY PRICE DYNAMICS AND DERIVATIVE VALUATION: A REVIEW

2013 ◽  
Vol 16 (06) ◽  
pp. 1350032 ◽  
Author(s):  
JANIS BACK ◽  
MARCEL PROKOPCZUK
Keyword(s):  
Commodity Price ◽  
Commodity Markets ◽  
Derivative Pricing ◽  
Empirical Literature ◽  
Price Dynamics ◽  
Important Research ◽  
Price Behavior ◽  
Pricing Models ◽  
Key Characteristics ◽  
Research Questions

This paper reviews extant research on commodity price dynamics and commodity derivative pricing models. In the first half, we provide an overview of key characteristics of commodity price behavior that have been explored and documented in the theoretical and empirical literature. In the second half, we review existing derivative pricing models and discuss how the peculiarities of commodity markets have been integrated in these models. We conclude the paper with a brief outlook on various important research questions that need to be addressed in the future.

scholarly journals Volatility Markets Underreacted to the Early Stages of the COVID-19 Pandemic

2020 ◽  
Vol 10 (4) ◽  
pp. 635-668 ◽  
Author(s):  
Ing-Haw Cheng
Keyword(s):  
Equity Market ◽  
Trading Strategies ◽  
Futures Prices ◽  
Pricing Models ◽  
Subsequent Increase ◽  
Oxford University ◽  
Price Premiums ◽  
Asset Pricing Models ◽  
Vix Futures ◽  
Published Paper

Abstract VIX futures prices rose slowly in late February and early March 2020 as the COVID-19 pandemic took hold. Futures price premiums, defined as futures prices minus real-time statistical forecasts of future VIX values, turned sharply negative and remained negative until mid-April. Trading strategies based on estimated premiums profited from the subsequent increase in market volatility and equity market crash. The underreaction of futures prices to growing pandemic risks poses a puzzle for standard asset pricing models. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Pricing models of shares of Russian companies and their practical application

2019 ◽  
pp. 48-76 ◽  
Author(s):  
Alexander E. Abramov ◽  
Alexander D. Radygin ◽  
Maria I. Chernova
Keyword(s):  
Stock Market ◽  
Institutional Investors ◽  
Factor Model ◽  
Investment Strategies ◽  
Pricing Models ◽  
Foreign Portfolio Investment ◽  
Market Portfolio ◽  
Stock Market Performance ◽  
Long Time ◽  
Russian Stock Market

The article analyzes the problems of applying stock pricing models in the Russian stock market. The novelty of the study lies in the peculiarities of the methodology used and the substantive conclusions on the specifics of the influence of fundamental factors on the pricing of shares of Russian companies. The study was conducted using its own 5-factor basic pricing model based on a sample of the most complete number of issues of shares of Russian issuers and a long time horizon, from 1997 to 2017. The market portfolio was the widest for a set of issuers. We consider the factor model as a kind of universal indicator of the efficiency of the stock market performance of its functions. The article confirms the significance of factors of a broad market portfolio, size, liquidity and, in part, momentum (inertia). However, starting from 2011, the significance of factors began to decrease as the qualitative characteristics of the stock market deteriorated due to the outflow of foreign portfolio investment, combined with the low level of development of domestic institutional investors. Also identified is the cyclical nature of the actions of company size and liquidity factors. Their ability to generate additional income on shares rises mainly at the stage of the fall of the stock market. The results of the study suggest that as domestic institutional investors develop on the Russian stock market, factor investment strategies can be used as a tool to increase the return on investor portfolios.

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