This paper provides evidence that aggregate returns on commodity futures (without the returns on collateral) are predictable, both in-sample and out-of-sample, by various lagged variables from the stock market, bond market, macroeconomics, and the commodity market. Out of the 32 candidate predictors we consider, we find that investor sentiment is the best in-sample predictor of short-horizon returns, whereas the level and slope of the yield curve have much in-sample predictive power for long-horizon returns. We find that it is possible to forecast aggregate returns on commodity futures out-of-sample through several combination forecasts (the out-of-sample return forecasting R 2 is up to 1.65% at the monthly frequency).
This paper conducts a European investigation of eight multifactor models that have been previously tested using US data. Many results confirm the US evidence: Most of the eight multifactor models investigated do a good job explaining the cross‐section of our testing portfolios, but most models are not justifiable by the Intertemporal CAPM (ICAPM). Carhart's four‐factor model shows the best empirical performance and consistency with the ICAPM. Nevertheless, some results counter the US evidence: Fama and French's three‐factor model is inconsistent with the ICAPM and the models of Hahn and Lee (2006) and Koijen et al. (2010) show low explanatory power.
Metals are very important resources for industrial production, but recently they have attracted more and more attention from investors. While certainly industrial producers, consumers, and financial investors do have some influence on metal price development, the role of relevant price factors is not yet quite clear. Therefore, in this paper we examine the explanatory power of various fundamental factors and characteristics known from financial markets, specifically on the expected returns in a unique data sample of 30 metals. We applyto our knowledge for the first time in this contextthe widely accepted method of characteristicsorted portfolios, extended by the very recent method of two-way portfolio sorts as an alternative to classical multivariate regressions. This mostly non-parametric approach, combined with portfolio aggregation, provides very robust results. Our major finding is that the financial characteristics value and momentum have a very high predictive power for monthly returns of metal portfolios. Metal-specific fundamental factors like stocks, secondary production, apparent consumption, country concentration, mine production, or reserves perform depending on the interpretation moderately well or rather poorly, regarding some economically interpretable transformations and when using multivariate two-way sorts. Hence, from the perspective of expected returns, metals are predominantly assets, while fundamental metal-specific factors still play a non-negligible role. Thus, to a much lesser extent, metals can still be regarded as resources. Overall, the combination of financial characteristics and metal-specific fundamental factors yields the best results. With these robust results, we hope to contribute to a better understanding of metal prices and their underlying factors.
We estimate the costs of equity capital for 117 industries from 16 European countries employing the CAPM and 8 multifactor asset pricing models as well as a variety of different econometric techniques. In doing so, we extend previous research on cost of equity estimation in mainly two ways. First, our study involves European instead of US or UK industries, which are investigated in previous research, and we find that cost of equity estimates obtained from the CAPM or multifactor asset pricing models are as imprecise for European industries as for US and UK industries. Second, in addition to the CAPM, the Fama and French [1993. “Common Risk Factors in the Returns on Stocks and Bonds.” Journal of Financial Economics 33: 3–56] three-factor model, and the Carhart [1997. “On Persistence in Mutual Fund Performance.” The Journal of Finance 52 (1): 57–82] four-factor model, which are usually employed, our study includes six multifactor models that have not yet been examined on their models provide even more imprecise cost of equity estimates. One main reason for these inaccurate estimates is the large temporal variation of the risk loadings on the non-traded factors in these models
Purpose The purpose of this paper is to develop a new interdisciplinary methodology to estimate the life cycle cost (LCC) of complex business-to-business products in order to price different types of maintenance contracts and show the applicability of the method in a case study. LCC comprise of initial capital costs as well of operation costs including probabilistic costs (such as the costs of repairs and spare parts), which are directly linked to the maintenance characteristics of the product. Design/methodology/approach The paper proposes an integrated and practical methodology that applies different approaches from different disciplines. Therefore, exponential distributions for failure rates in subsystems, World Bank logistics factors for logistics costs of spare part handling, as well implied credit default probabilities for the counterpart risk in full service leasing contracts are applied. In order to validate the applicability of the proposed methodology to practical problems, the tool is applied in three case studies. Findings The results of the case studies show that this methodology can be applied to analyze LCC structures of engines operating in various regions with regard to different types of engine maintenance contracts. The results also highlight the interplay of technical as well as financial risks. Originality/value Because the literature in maintenance engineering so far either proposes general frameworks to calculate LCC or concentrates on specific aspects of LCC, the paper contributes to the literature in presenting a new interdisciplinary methodology to estimate the LCC.
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