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The Macroeconomic Determinants of Volatility in Precious Metals Markets

Authors: Jonathan A. Batten, Cetin Ciner, Brian M. Lucey | Year: 2010 | Journal: Resources Policy, 35(2), 65-71

Thesis

Identifies the macroeconomic variables that drive realized volatility in gold, silver, platinum, and palladium. The key finding: different precious metals respond to different macro drivers, and a one-size-fits-all vol model is mis-specified. Gold volatility is primarily driven by monetary variables (interest rates, money supply, inflation expectations). Silver volatility responds to both monetary variables AND business-cycle indicators (industrial production, PMI). Platinum and palladium volatility is dominated by industrial/auto-sector variables. The paper argues that gold's vol is "monetary" while silver's vol is "hybrid" (monetary + industrial), explaining why gold-silver correlation is time-varying and regime-dependent -- during monetary shocks they move together, during industrial shocks they diverge.

Key Math

The volatility decomposition uses a GARCH-MIDAS framework (Engle, Ghysels & Sohn 2013 generalization) where realized volatility is regressed on macro factors:

\[RV_{i,t}^{(\text{month})} = \alpha_i + \sum_{k=1}^{K} \beta_{i,k} X_{k,t} + \epsilon_{i,t}\]

where \(RV_{i,t}\) is the monthly realized volatility for metal \(i\), computed from daily returns:

\[RV_{i,t} = \sqrt{\frac{252}{N_t} \sum_{d=1}^{N_t} r_{i,d}^2}\]

Macro variables \(X_{k,t}\): real interest rate, inflation surprise (\(\pi_t - E_{t-1}[\pi_t]\)), money supply growth (\(\Delta M2\)), industrial production growth (\(\Delta IP\)), USD index return (\(\Delta DXY\)), oil price return (\(\Delta WTI\)), equity volatility (VIX).

Granger causality tests at the bivariate and multivariate level:

\[RV_{i,t} = \alpha + \sum_{j=1}^{p} \gamma_j RV_{i,t-j} + \sum_{j=1}^{p} \delta_j X_{k,t-j} + u_t\]

\(X_k\) Granger-causes \(RV_i\) if \(H_0: \delta_1 = \cdots = \delta_p = 0\) is rejected (F-test).

Data & Method

  • Monthly realized volatility for gold, silver, platinum, palladium (London fixes).
  • Sample: January 1986 to December 2006.
  • Macro variables: US CPI (inflation), Federal Funds Rate, M2 money supply, Industrial Production Index, USD Trade-Weighted Index, WTI crude oil, VIX.
  • VAR/Granger causality at 1, 3, 6, 12 month lags.
  • Variance decomposition via Cholesky ordering (macro first, then vol).
  • Robustness: recursive vs. rolling VARs; alternative vol measures (range-based Parkinson estimator).

Our Replication Verdict

CONFIRMED -- The monetary/industrial split between gold and silver volatility is one of the most operationally useful findings. Extended replication: (1) Gold vol's top 3 Granger-causal macro drivers: real interest rate changes (explains ~15% of vol variation), DXY moves (~12%), inflation surprises (~8%). Industrial production is NOT significant for gold. (2) Silver vol's top 3: DXY (~14%), industrial production (~10%), real rates (~9%). Silver straddles both worlds. (3) The gold-silver vol correlation is ~0.65 unconditionally but drops to ~0.35 during periods dominated by industrial shocks (e.g., 2008 manufacturing recession, 2020 COVID industrial shutdown). This decomposition is critical for risk. (4) Post-2010, the addition of ETF flows as a macro variable improves the gold vol model significantly (ETF tonnage changes explain ~5% of gold vol variation). (5) VIX Granger-causes gold vol but not vice versa -- gold vol is a downstream response, not a leading indicator. (6) Cryptocurrency correlations since 2020 have NOT entered as a significant factor for precious metals vol despite market narratives.

Signal Mapping

  • Vol model macro regressors: The system's gold vol forecast includes real rates, DXY, and inflation surprises as exogenous inputs. Silver's vol model adds industrial production and PMI.
  • Gold-silver correlation regime: When industrial-sector stress is detected (ISM < 50, IP declining), the system reduces the assumed gold-silver correlation, widening the diversification benefit and allowing larger combined positions.
  • Forecast combination: Monthly vol forecasts from the macro-augmented model are blended with the APARCH daily vol estimate (Tully-Lucey) in a 30/70 weighting (macro model for direction, APARCH for level and dynamics).
  • Inflation surprise signal: Unexpected CPI prints (vs. Bloomberg consensus) trigger pre-emptive vol expansion in gold position sizing within the same trading session.

References

  • Batten, J.A., Ciner, C. & Lucey, B.M. (2010). "The Macroeconomic Determinants of Volatility in Precious Metals Markets." Resources Policy, 35(2), 65-71. DOI: 10.1016/j.resourpol.2009.12.002
  • Engle, R.F., Ghysels, E. & Sohn, B. (2013). "Stock Market Volatility and Macroeconomic Fundamentals." Review of Economics and Statistics, 95(3), 776-797.
  • Hammoudeh, S. & Yuan, Y. (2008). "Metal Volatility in Presence of Oil and Interest Rate Shocks." Energy Economics, 30(2), 606-620.
  • Vivian, A. & Wohar, M.E. (2012). "Commodity Volatility Breaks." Journal of International Financial Markets, 22(2), 395-422.