Steven Landgraf WPPI Energy and Marquette University Abdur Chowdhury Marquette University USAEE/IAEE North American Conference Tuesday Oct 11 th, 2011

 Commodity price bubble (2003 – 2008) Record high oil and natural gas prices Ultra low interest rates, Accelerated EM economic growth “Financialization” of commodity markets

 Commodity price recovery post financial crisis (2009- ) Near-zero interest rate policies in advanced countries Massive injections of liquidity during the financial crisis (Quantitative Easing).  QE2 (late 2010 to mid 2011)

Backlash in the media against QE2 – Coincided with run-ups in prices of oil, gold, food, etc. – Roubini: “Wall of liquidity” chasing assets in EMs Continued strong performance of EMs after 2008 fueled commodity demand Not much “global” research incorporates BRIC influence

Frankel (1986, 2008) – Overshooting model – Monetary variables and commodity prices related Sousa and Zaghini (2004, 2006) – Global monetary shocks have long-run impacts on domestic prices Rüffer and Stracca (2006) – “Excess liquidity” impacts prices in advanced countries Belke et. al. (2010) – Expansionary shocks increase relative prices

 Does excess liquidity positively impact commodity prices?  Which effect is more prominent: Demand channel? Excess liquidity?  Do the results change if emerging market data is included in the global aggregate?

 ADV – aggregates 10 advanced economies and the euro zone economies  ALL – aggregates the BRIC countries in addition to the countries in ADV

 Output (demand channel) impacts commodity prices – robust result  Excess liquidity, interest rate – mixed results Interest rate – little influence Shocks to excess liquidity more prominent than shocks to output in ALL – opposite of ADV

Vector Error-Correction Granger causality Impulse Response Function (IRF) Variance Decomposition (VDC) GDP Sum of GDP (“demand channel”) MON Sum of broad money supply divided by GDP sum (“excess liquidity”) INT GDP-weighted average of S-T (3 mo) interest rate CPI GDP-weighted average of headline CPI COM Commodity price index

 Log-difference (except interest rate)  Sample: 1995Q2 to 2010Q3  Sourced mostly from IMF Supplemented with World Bank data for some BRIC countries Aggregation methodology follows Sousa and Zaghini (2004)  PPP exchange rates  Commodity Index: S&P GSCI 66% weighted with energy commodities

 Lag length (Info criterion and LM test) ADV: 2 ALL: 3  Unit root tests: stationary in 1 st diff.  Cointegration: tests suggest its presence Appropriate to use a VEC vs. a VAR Long-run equilibrium exists between variables

 Demand channel (GDP) robustly impacts commodity prices whether or not BRICs are included. Granger, IRF, VDCs support Structural relationship between output and commodity prices Prices also respond positively to positive shocks

 Neither interest rates nor excess liquidity Granger cause commodity prices  Positive shocks (1 std. dev.) ADV: increase commodity prices 2 quarters out ALL: increase commodity prices 6 and 7 quarters out

 VDCs show discrepancies between ADV and ALL  Excluding BRIC data overestimates impact of demand channel and underestimates excess liquidity

Shock to GDP QuarterADVALL 112%23% 230%32% 327% 434%24% 536%24% 634%21% 737%17% 837%18% 936%16% 1037%17% Shock to MON QuarterADVALL 11% 29%7% 38%9% 412%25% 511%25% 611%34% 711%42% 811%42% 911%46% 1011%46%

ADV: ALL:

ADV: ALL:

 BRIC country economies impact commodity prices in a way not captured by using advanced country data  Global liquidity shocks have a great impact on prices when country sample is expanded beyond advanced countries  Suggests a diminished role of advanced countries in impacting prices

 Central Banks should continue to closely monitor emerging market monetary policy when considering effects on commodity and energy markets  Research coming from a global standpoint should not exclude emerging markets from analyses Subject to data availability

 Interest rates not shown to have a measurable impact on commodity prices Contrasts with the literature Average a good measure? Some use LIBOR  Relatively low degrees of freedom  Data quality  Evidence of monetary impacts not overwhelming