scholarly journals Learning in the Oil Futures Markets: Evidence and Macroeconomic Implications

2016 ◽  
Vol 2016 (1179) ◽  
pp. 1-47 ◽  
Author(s):  
Sylvain Leduc ◽  
◽  
Kevin Moran ◽  
Robert J. Vigfusson
Keyword(s):  
Futures Markets ◽  
Oil Futures ◽  
Macroeconomic Implications

scholarly journals Learning in the Oil Futures Markets: Evidence and Macroeconomic Implications

2021 ◽  
pp. 1-45
Author(s):  
Sylvain Leduc ◽  
Kevin Moran ◽  
Robert J. Vigfusson
Keyword(s):  
Futures Markets ◽  
Oil Price ◽  
Oil Futures ◽  
Learning Mechanism ◽  
Expectation Formation ◽  
Macroeconomic Impact ◽  
Oil Demand ◽  
Price Movements ◽  
Macroeconomic Implications ◽  
Empirical Framework

Abstract Using oil futures, we examine expectation formation and how it alters the macroeconomic transmission of shocks. Our empirical framework, where investors learn about the persistence of oil-price movements, successfully replicates the fluctuations in oil-price futures since the late 1990s. By embedding this learning mechanism in an estimated model, we document that an increase in the persistence of TFP-driven fluctuations in oil demand largely accounts for investors’ perceptions that oil-price movements became increasingly permanent during the 2000s. Learning alters the macroeconomic impact of shocks, making the responses time-dependent and conditional on perceptions of shocks’ likely persistence.

scholarly journals Learning in the Oil Futures Markets: Evidence and Macroeconomic Implications

2020 ◽  
pp. 1.000-67.00
Author(s):  
Kevin Moran ◽  
◽  
Robert J. Vigfusson ◽  
Sylvain Leduc ◽  
◽  
...  
Keyword(s):  
Futures Markets ◽  
Oil Price ◽  
Oil Futures ◽  
Learning Mechanism ◽  
Economic Agents ◽  
Macroeconomic Impact ◽  
Oil Demand ◽  
Price Movements ◽  
Macroeconomic Implications ◽  
And Storage

Working Papers 2020-33 October 2020 Learning in the Oil Futures Markets: Evidence and Macroeconomic Implications Sylvain Leduc, Kevin Moran, Robert J. Vigfusson Using expectations embodied in oil futures prices, we examine how expectations are formed and how they affect the macroeconomic transmission of shocks. We show that an empirical framework in which investors form expectations by learning about the persistence of oil-price movements successfully replicates the fluctuations in oil-price futures since the late 1990s. We then embed this learning mechanism in a model with oil usage and storage. Estimating the model, we document that an increase in the persistence of TFP-driven fluctuations in oil demand largely account for investors' perceptions that oil-price movements became increasingly permanent during the 2000s before declining thereafter. We show that the presence of learning alters the macroeconomic impact of shocks, making the responses time-dependent and conditional on the views of economic agents about the shocks' likely persistence.

Macroeconomic Factors in Oil Futures Markets

2019 ◽  
Vol 65 (9) ◽  
pp. 4407-4421 ◽  
Author(s):  
Davidson Heath
Keyword(s):  
Economic Activity ◽  
Futures Markets ◽  
Price Shocks ◽  
Productivity Shocks ◽  
Macroeconomic Factors ◽  
Oil Futures ◽  
Risk Premiums ◽  
Crude Oil Futures ◽  
Future Consumption ◽  
New Evidence

This paper documents new evidence against perfect risk spanning in crude oil futures, and develops an affine futures pricing model that allows for unspanned macroeconomic factors. Compared to previous estimates, the oil spot premium is more volatile and strongly procyclical, which suggests that previous models miss the majority of variation in oil risk premiums. The estimates reveal a dynamic two-way relationship between oil futures and economic activity: productivity shocks are associated with higher oil prices, while oil price shocks affect economic activity by lowering future consumption spending. Unspanned macro factors also affect the valuation of real options. This paper was accepted by Karl Diether, finance.

Covariance breakdowns and connectedness of crude oil futures markets with non-synchronous data

2018 ◽  
Vol 51 (5) ◽  
pp. 422-443 ◽  
Author(s):  
Jiawen Luo ◽  
Langnan Chen ◽  
Weiguo Zhang
Keyword(s):  
Crude Oil ◽  
Futures Markets ◽  
Oil Futures ◽  
Crude Oil Futures

Gold and oil futures markets: Are markets efficient?

2010 ◽  
Vol 87 (10) ◽  
pp. 3299-3303 ◽  
Author(s):  
Paresh Kumar Narayan ◽  
Seema Narayan ◽  
Xinwei Zheng
Keyword(s):  
Futures Markets ◽  
Oil Futures

Adaptive Bands Z-Test-Statistics in Futures Markets: A New Technical Analysis Indicator

2013 ◽  
Vol 12 (3) ◽  
pp. 19-47
Author(s):  
Mohd Rizal Palil
Keyword(s):  
Soybean Oil ◽  
Futures Markets ◽  
Abnormal Returns ◽  
Technical Analysis ◽  
Test Statistics ◽  
Futures Prices ◽  
Oil Futures ◽  
Trading Rules

This paper presents a study of technical analysis trading rules that generate abnormal returns for futures prices.  It reports abnormal returns above that of the passive buy-and-hold policy for FKLI, FCPO, Soybean Oil Futures, Soybean Futures and Corn Futures for year 2008 tested. 

The Impact of Structural Breaks on Volatility Linkages between Asian Stock and Oil Futures Markets

2016 ◽  
Vol 24 (1) ◽  
pp. 31-64
Author(s):  
Sang Hoon Kang ◽  
Seong-Min Yoon
Keyword(s):  
Futures Markets ◽  
Structural Breaks ◽  
Global Financial Crisis ◽  
Garch Model ◽  
Time Varying ◽  
Oil Futures ◽  
Volatility Spillovers ◽  
Icss Algorithm ◽  
The Global Financial Crisis ◽  
The Impact

This paper investigates the impact of structural breaks on volatility spillovers between Asian stock markets (China, Hong Kong, India, Indonesia, Japan, Korea, Singapore, and Taiwan) and the oil futures market. To this end, we apply the bivariate DCC-GARCH model to weekly spot indices during the period 1998-2015. The results reveal significant volatility transmission for the pairs between the Asian stock and oil futures markets. Moreover, we find a significant variability in the time-varying conditional correlations between the considered markets during both bullish and bearish markets, particularly from early 2007 to the summer of 2008. Using the modified ICSS algorithm, we find several sudden changes in these markets with a common break date centred on September 15, 2008. This date corresponds to the collapse of Lehman Brothers which is considered as our breakpoint to define the global financial crisis. Also, we analyse the optimal portfolio weights and time-varying hedge ratios based on the estimates of the multivariate DCC-GARCH model. The results emphasize the importance of overweighting optimal portfolios between Asian stock and the oil futures markets.

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