Timing Techniques for Commodity Futures Markets
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Return predictability ADS index. This figure plots the in- and out-of-sample performances predicting the next year's excess return based on the ADS index. For robustness, to assess the predictive power of the ADS index in the case of multiple predictive regressions, we use Equation 3 , however, we now define X t as the vector of predictors. In doing so, we are able to analyze whether the ADS index still has significant predictive power for future excess returns or whether other predictive variables are capturing its predictive power.
Thus, we proceed as follows: we run the multiple regressions once with and once without the ADS index. First, we use stock and macroeconomic variables jointly. Afterwards, we consider both kinds of predictive variables separately. Table 10 reports the results of multiple regressions using stock and macroeconomic variables jointly. The results confirm the strong predictive power of the ADS index. The multiple regressions yield significant R 2 s for all metal futures ranging from 9.
Read Timing Techniques for Commodity Futures Markets: Effective Strategy and Tactics for Short-Term
In the out-of-sample analysis, we detect significant predictive power for gold and silver excess returns, indicated by R o o s 2 s of Moreover, the strong predictive power of the ADS index is also supported by its statistically significant t -statistics for all metals, except silver. Also dfy reveals strong predictive power for future excess returns, indicated by statistically significant t -statistics for all metals, which is supporting our previous findings of the univariate regressions see Table 5. Multiple regressions: stock variables, macroeconomic variables, and ADS index.
This table reports the regression results of monthly excess returns [name in column] on a constant and the lagged predictive variables [name in rows]. The results in Table 11 confirm the findings, when using stock variables only. The R 2 s are statistically significant for all metals. The out-of-sample results show significant predictive power for gold, silver, and platinum excess returns, indicated by R o o s 2 s of Table 12 provides somewhat weaker evidence, when using macroeconomic variables.
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Overall, the analysis reveals significant predictive power in-sample for gold, palladium, and silver excess returns, whereas out-of-sample for gold and silver excess returns. Multiple regressions: stock variables and ADS index. Multiple regressions: macroeconomic variables and ADS index. In total, the multiple regressions provide evidence for a strong predictive power of the ADS index for future metal excess returns, both in-sample and out-of-sample.
To emphasize is the predictive power in particular for gold and silver excess returns. The results support our main findings. This paper performs a comprehensive study of metal futures excess return predictability using 12 variables that are supposed to predict stock returns. We also focus on the identification of years of high and low predictability. We find a substantial degree of predictability both in- and out-of-sample. Mean forecast combinations provide evidence for an improved out-of-sample predictability.
Gold returns appear to be best predictable. Moreover, we analyze the ADS index, which captures business conditions in real-time, to examine the potential effects on metal returns and on the behavior over business cycles. We provide evidence that the ADS index incorporates relevant information for metal returns. It turns out to be a strong predictor for gold returns. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Further variables known to predict stock returns are the dividend—price ratio and the 3-month U. Thus, we also take care about potential structural breaks in the time series. Due to the apparent sensitivity of the forecasting accuracy of the R o o s 2 s , it is necessary to additionally assess the degree of significance rather than relying on the absolute amount of the R o o s 2 s only. We obtain similar results, when using these approaches rather than the mean forecast combination approach.
Accordingly, we use the log-return and variance to express the simple return. National Center for Biotechnology Information , U. Journal List Heliyon v. Published online Jun Author information Article notes Copyright and License information Disclaimer. Published by Elsevier Ltd. Abstract This paper studies the predictability of metal futures returns.
Keywords: Economics, Commodities, Metal futures, Return predictability. Introduction Are returns predictable? Methodology 2. Data We obtain our data from several sources. Table 1 Summary statistics metal futures excess returns. Nobs Copper 0. Open in a separate window. Variables Metal futures excess returns.
In- and out-of-sample return predictability In-sample analysis.
Timing Techniques for Commodity Futures Markets - Investment / Trading - Finance
Bootstrap procedure To implement the bootstrap algorithm, we follow Rapach and Wohar MSFE-adjusted test statistic The R o o s 2 is a point estimate, thus, the forecast accuracy is sensitive, among others, to the sample size Zhu and Zhu , Results 3. Summary statistics Before discussing our main empirical results, it is instructive to look at some summary statistics and correlations. Table 2 Correlation matrix metal futures excess returns. Table 3 Summary statistics predictive variables. Nobs de Table 4 Correlation matrix predictive variables. Return predictability We start by analyzing the performance of variables predicting the next year's excess return on the basis of univariate regressions.
Table 5 Return predictability: univariate regressions.
Model selection approach Next, we examine the return predictability based on a model selection approach. Figure 1. Economic value analysis Next, we examine whether return predictability also translates to economic gains. Table 7 Economic value.
Table 8 Economic value and transaction costs. Results and discussion In this section, we analyze the relationship between metal futures excess returns and the ADS index.
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Informative value of the ADS index for metal futures returns Fig. Figure 2. Table 9 Correlations and return predictability of ADS index. Figure 3. Multiple regressions For robustness, to assess the predictive power of the ADS index in the case of multiple predictive regressions, we use Equation 3 , however, we now define X t as the vector of predictors.
Table 10 Multiple regressions: stock variables, macroeconomic variables, and ADS index. Table 11 Multiple regressions: stock variables and ADS index. Table 12 Multiple regressions: macroeconomic variables and ADS index. Conclusion This paper performs a comprehensive study of metal futures excess return predictability using 12 variables that are supposed to predict stock returns. Funding statement This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Competing interest statement The authors declare no conflict of interest. Additional information No additional information is available for this paper. References Ang A. Stock return predictability: is it there? Stock return predictability: Is it there?
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