RDP 2017-03: Financialisation and the Term Structure of Commodity Risk Premiums 4. The Term Structure of Risk Premiums and Net Hedging Positions

As discussed above, empirical identification of a relationship between NHP and commodity risk premiums has been mixed. An explanation that has not been explored in the literature to date is that the relationship between NHP and commodity risk premiums could be duration dependent and more apparent for longer-maturity contracts. In part, the fact that this has not been explored likely reflects the lack of available data on commercial positions by maturity.

There are a number of reasons why a relationship between NHP and commodity risk premiums could be more apparent for longer-maturity contracts. Consumers and producers may prefer to hedge for relatively long periods. For example, if producers of a given commodity have a strong preference for hedging their expected exposure to prices twelve months out due to the nature of their production schedule, a measured large negative NHP may be associated with a large positive risk premium on futures contracts with a twelve-month maturity, but not on futures contracts with a one-month maturity. Another reason that the relationship may be more apparent when longer-maturity futures contracts are considered is that these markets may have larger barriers to entry, limiting the number of speculators in the market and therefore preventing the ‘residual’ portion of the risk premiums from being competed away. This could reflect a preference by investors to take on short-term investments, as well as the potentially greater costs in compiling information on the long-term prospects of supply and demand in commodities markets.

In light of this, the following empirical analysis aims to investigate two questions which, to our knowledge, have not been investigated in the literature:

  1. Is there evidence of a significant negative relationship between commodity risk premiums and NHP if premiums on longer-dated contracts are incorporated into the analysis?
  2. Is there evidence of a more significant negative relationship between NHP and commodity risk premiums on longer-dated futures contracts?

We use panel regressions to examine the relationship between NHP and commodity risk premiums for commodity futures contracts with different maturities. The cross-section is made up of around 500 different contracts, with each one representing a generic commodity contract with a particular maturity (e.g. oil with a one-month maturity, oil with a two-month maturity). Specifically, we estimate:

where Rc,m,t is the annualised return on commodity c, with horizon m, entered into at time t. The γc,m are contract fixed effects that will account for omitted time-invariant factors, such as whether the commodity is storable. The θt are time fixed effects, which should help to capture omitted factors such as the global growth cycle.[24] The main coefficient of interest is β.

We estimate the model using cluster-robust standard errors as outlined in Thompson (2011). These errors are robust to heteroskedasticity, serial correlation among errors for a single cross-sectional contract, cross-sectional correlation between contracts at time t and common serially correlated disturbances.[25]

Table 2 shows the results from the model. If only risk premiums on the nearest to maturity contracts are included in the model, as is done in most of the literature, there is little evidence of a significant relationship between risk premiums and the ex ante level of the NHP.[26] However, if returns on longer-dated futures contracts are included, we find strong evidence of a negative relationship, consistent with the net hedging pressure theory.

Table 2: Regression Results – β Constant across Maturities
  Nearest to maturity contract All contracts
All sectors −0.13 −0.12***
  (0.08) (0.04)
By sub-sector
Agriculture −0.11 −0.12**
  (0.09) (0.05)
Energy −0.58 −0.60**
  (0.47) (0.30)
Metals −0.12 0.03
  (0.18) (0.12)

Notes: *, ** and *** indicate significance at the 10, 5 and 1 per cent level, respectively; Thompson (2011) standard errors are in parentheses and are robust to serial correlation, cross-sectional correlation, common serially correlated shocks and heteroskedasticity

The conclusions are similar if the β coefficient is allowed to differ for different commodity sub-sectors. If only the nearest to maturity contract is included, there is no evidence of a significant relationship. However, when longer-dated contracts are included, there is evidence of a statistically significant negative relationship for the agriculture and energy sub-sectors, though not for the metals sub-sector. This result is consistent with other papers that have failed to find a significant relationship between metals risk premiums and the NHP (e.g. Rouwenhorst and Tang 2012).

The results show that including longer-dated futures contracts allows us to identify NHP as a determinant of commodity risk premiums. To some extent, this may reflect the increased number of observations, which should lead to more precisely estimated coefficients, rather than actually indicating a stronger relationship between NHP and risk premiums for longer-dated contracts. It should also not be surprising that we find a relationship between NHP and commodity risk premiums using all contracts, rather than just short-term contracts, given NHP is an aggregate of hedging positions across all maturities.

To estimate precisely whether there is a stronger relationship between NHP and commodity risk premiums at specific maturities, we would need NHP to vary by maturity. As already noted, NHP data by maturity are not available. Instead, we can try and infer something about the relationship across the curve by allowing the β coefficient to differ across maturity buckets. Overall, the results suggest that the relationship between NHP and risk premiums is negative (as theory suggests) and of a similar magnitude across different maturity buckets for commodities in both the agricultural sub-sector and in the energy sub-sector. However, the coefficients are only statistically significant for longer-dated futures contracts (Table 3).[27] In contrast, for the metals sub-sector there is evidence of a significant negative relationship for the one-month maturity, but not for the longer-maturity futures contracts.[28]

Overall, these results provide empirical support for the net hedging pressure theory, which in turn indicates that commodities markets are somewhat segmented. As such, the net hedging pressure theory represents an appropriate lens through which to examine the effects of financialisation.

Table 3: Regression Results – β Varying across Maturities
  1–month 2–month 3–month 4–6 months 7–12 months 13–18 months 18–24 months
All sub-sectors −0.21** −0.15 −0.11 0.12** −0.11*** −0.14** −0.02
  (0.11) (0.10) (0.08) (0.06) (0.04) (0.05) (0.09)
By sub-sector
Agriculture −0.16 −0.14 −0.11 −0.11* −0.12** −0.12** 0.04
  (0.13) (0.09) (0.08) (0.06) (0.05) (0.05) (0.11)
Energy −0.72 −0.45 −0.50 −0.62 −0.60 −0.68*** −0.46***
  (0.46) (0.44) (0.44) (0.43) (0.36) (0.23) (0.10)
Metals −0.26** −0.09 0.00 0.03 0.12 0.02 0.06
  (0.11) (0.08) (0.09) (0.12) (0.14) (0.12) (0.14)

Notes: *, ** and *** indicate significance at the 10, 5 and 1 per cent level, respectively; Thompson (2011) standard errors are in parentheses and are robust to serial correlation, cross-sectional correlation, common serially correlated shocks and heteroskedasticity

Footnotes

The contract and time fixed effects should also help to capture any portion of the risk premium that is related to ‘systematic’ risk, rather than ‘residual’ risk, with the latter being the portion that is more directly related to hedging pressure (Hirshleifer 1988). The contract fixed effects will also capture the fact that the true Et (ec,m,t) will differ from zero by a small amount due to Jensen's inequality. [24]

A number of other less general error specifications were considered. However, given the nature of the data, and in particular the fact that the returns are estimated using overlapping horizons, we favoured a more general approach. [25]

We use the nearest to maturity contract, rather than the one-month maturity contract, to be more consistent with the literature. [26]

Maturity buckets are used, rather than individual maturities, for two reasons. First, it significantly reduces the number of coefficients to be estimated. Second, for a sizeable proportion of the commodities there are relatively few observations for longer maturities, which may make it difficult to estimate separate coefficients for each maturity. Pooling the maturities is likely to ameliorate this issue somewhat. [27]

This is surprising given the earlier finding when using nearest to maturity contracts. However, it is not inconsistent because the nearest to maturity contract can be the two- or three-month contract (as there are not contracts expiring in all calendar months). [28]