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Author: Miguel Dorta
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Languages : en
Pages :
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Book Description
This study develops a multi-period log-return quantile forecasting procedure to evaluate the performance of eleven nearby commodity futures contracts (NCFC) using a sample of 897 daily price observations and at-the-money (ATM) put and call implied volatilities of the corresponding prices for the period from 1/16/2008 to 7/29/2011. The statistical approach employs dynamic log-returns quantile regression models to forecast price densities using implied volatilities (IVs) and factors estimated through principal component analysis (PCA) from the IVs, pooled IVs and lagged returns. Extensive in-sample and out-of-sample analyses are conducted, including assessment of excess trading returns, and evaluations of several combinations of quantiles, model specifications, and NCFC's. The results suggest that the IV-PCA-factors, particularly pooled return-IV-PCA-factors, improve quantile forecasting power relative to models using only individual IV information. The ratio of the put-IV to the call-IV is also found to improve quantile forecasting performance of log returns. Improvements in quantile forecasting performance are found to be better in the tails of the distribution than in the center. Trading performance based on quantile forecasts from the models above generated significant excess returns. Finally, the fact that the single IV forecasts were outperformed by their quantile regression (QR) counterparts suggests that the conditional distribution of the log-returns is not normal.
Author: Miguel Dorta
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
This study develops a multi-period log-return quantile forecasting procedure to evaluate the performance of eleven nearby commodity futures contracts (NCFC) using a sample of 897 daily price observations and at-the-money (ATM) put and call implied volatilities of the corresponding prices for the period from 1/16/2008 to 7/29/2011. The statistical approach employs dynamic log-returns quantile regression models to forecast price densities using implied volatilities (IVs) and factors estimated through principal component analysis (PCA) from the IVs, pooled IVs and lagged returns. Extensive in-sample and out-of-sample analyses are conducted, including assessment of excess trading returns, and evaluations of several combinations of quantiles, model specifications, and NCFC's. The results suggest that the IV-PCA-factors, particularly pooled return-IV-PCA-factors, improve quantile forecasting power relative to models using only individual IV information. The ratio of the put-IV to the call-IV is also found to improve quantile forecasting performance of log returns. Improvements in quantile forecasting performance are found to be better in the tails of the distribution than in the center. Trading performance based on quantile forecasts from the models above generated significant excess returns. Finally, the fact that the single IV forecasts were outperformed by their quantile regression (QR) counterparts suggests that the conditional distribution of the log-returns is not normal.
Author: Robert J. Bianchi
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Languages : en
Pages : 32
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Book Description
This study examines the relationship between commodity futures and global stocks. For the first time, we examine the financialization of commodity futures by employing a quantile regression approach. From 2004-2013, we confirm a strong degree of dependence in energy commodities with moderate effects in metals and lesser magnitudes in agriculturals. During the 2008-2009 global financial crisis, our findings show a strengthening in the financialization of energy commodities while there were weaker effects in agriculturals and a decoupling or de-financialization in metal markets. With the recent closure of commodity trading units in Wall Street in 2013, the findings reveal the de-financialization of metals and agricultural markets from 2014-2017. Overall, our findings cast doubt on the diversification benefits of energy-dominated commodity indices after 2013. We argue the impact of financialization on commodity futures markets is more permanent than previously thought.
Author: Trevor A. Reeve
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Languages : en
Pages :
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Author: Davidson Heath
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Category :
Languages : en
Pages : 72
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This paper investigates the forecastability of prices and returns in commodity futures markets. To examine the implications for models of commodity prices we derive a new canonical affine form that lends itself to model evaluation and comparison. Both regressions and model estimates imply that effectively all variation in the term structure of futures prices is due to time varying risk premiums and none to price forecasts. The model estimates further suggest that the economic quantity that links futures prices to storage -- the cost of carry -- is pinned down unambiguously by the data.
Author: Massimo Guidolin
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Languages : en
Pages : 37
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We test whether three well-known commodity-specific variables (basis, hedgingpressure, and momentum) may improve the predictive power for commodity futuresreturns of models otherwise based on macroeconomic factors. We compute recursive,out-of-sample forecasts for fifteen monthly commodity futures return series, whenestimation is based on a stepwise model selection approach under a probability-weightedregime-switching regression that identifies different volatility regimes.Comparisons with an AR(1) benchmark show that the inclusion of commodity-specificfactors does not improve the forecasting power. We perform a back-testing exercise of amean-variance investment strategy that exploits any predictability of the conditionalrisk premium of commodities, stocks, and bond returns, also taking into accounttransaction costs caused by portfolio rebalancing. The risk-adjusted performance of thisstrategy does not allow us to conclude that any forecasting approach outperforms theothers. However, there is evidence that investment strategies based on commodity-specificpredictors outperform the remaining strategies in the high-volatility state.
Author: Chakriya Bowman
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Languages : en
Pages : 28
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This paper assesses the performance of three types of commodity price forecasts--those based on judgment, those relying exclusively on historical price data, and those incorporating prices implied by commodity futures. For most of the 15 commodities in the sample, spot and futures prices appear to be nonstationary and to form a cointegrating relation. Spot prices tend to move toward futures prices over the long run, and error-correction models exploiting this feature produce more accurate forecasts. The analysis indicates that on the basis of statistical- and directional-accuracy measures, futures-based models yield better forecasts than historical-data-based models or judgment, especially at longer horizons.
Author: Daehwan Kim
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Languages : en
Pages : 40
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Book Description
A growing body of literature confirms the significance of the commodity futures basis factor: It has a significantly positive premium and it explains the cross-section of commodity-futures excess returns. We extend the literature by documenting predictive relation between this factor and the inter-quartile spread in the basis. Using commodity futures market data between 1972 and 2011, we show that the basis spread is a strong predictor of the basis factor return. Our finding supports the insight from recent theoretical models that economy-wide production shock affects the commodity market risk premium through the basis.
Author: Charles Schut Rockwell
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Category :
Languages : en
Pages : 114
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Author: Lin Gao
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Languages : en
Pages : 0
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The detrended implied volatility of commodity options (VOL) forecasts the cross section of the commodity futures returns significantly. A zero-cost strategy that is long in low VOL and short in high VOL commodities yields an annualized return of 12.66% and a Sharpe ratio of 0.69. Notably, the excess returns based on the volatility strategy emanate mainly from its forecasting power for the future spot component, different from the other commodity strategies examined so far in the literature which are all driven by roll returns. This strategy demonstrates low correlations (below 10%) with the other strategies such as momentum or basis and performs especially well in recessions. Our results are robust after controlling for illiquidity, other commodity pricing factors, and exposure to the aggregate commodity market volatility. The VOL measure is associated with hedging pressure on the futures and especially on the options market. News media also helps amplify the uncertainty impact. Variables related to investors' lottery preferences and market frictions are able to explain part of the predictive relationship.
Author: Abdelkader Mohamed Sghaier Derbali
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Languages : en
Pages : 0
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This paper aims to provide an important perspective to the predictive capacity of Organization of the Petroleum Exporting Countries (OPEC) meeting dates and production announcements for energy futures (crude oil West Texas Intermediate (WTI), gasoline reformulated gasoline blendstock for oxygen blending (RBOB), Brent oil, London gas oil, natural gas and heating oil) market returns and volatilities.To examine the impact of OPEC news on energy futures market returns and volatilities, the authors use a conditional quantile regression methodology during the period from April 01, 2013 to June 30, 2017.From the empirical findings, the authors show a conditional dependence between energy futures returns and OPEC-based predictors; hence, the authors can find clear the significance of relationship in the process of financialization of the OPEC announcements and energy futures in the case of this paper. From the quantile-causality test, the authors find that the effect of OPEC news is important to energy futures. Specifically, OPEC announcements dates predict the quantiles of the conditional distribution of energy futures market returns.The authors confirm the presence of unidirectional nexus between OPEC news and energy commodities futures in the long term.
