Volatility spillovers between agricultural commodity and financial asset markets ZEF Volatility Workshop, 1 February 2013

Transcription

1 Volatility spillovers between agricultural commodity and financial asset markets ZEF Volatility Workshop, Stephanie Grosche

2 Growing importance of commodities as portfolio assets More investment vehicles available Global financial crisis intensify Growth in Commodity ETP assets , bn USD global financial crisis Subprime mortgage crisis (2007/2008) Sovereign debt crisis (from ~2010) Use of agricultural commodities as portfolio diversifiers facilitated Higher importance of agricultural commodities refuge assets Source: BlackRock (2011); Conover et al. 2010, Chong and Miffre 2010, Gorton and Rouwenhorst 2006, Ankrim and Hensel

3 Jun-98 Jun-99 Jun-00 Jun-01 Jun-02 Jun-03 Jun-04 Jun-05 Jun-06 Jun-07 Jun-08 Jun-09 Jun-10 Jun-11 Jun-98 Jun-99 Jun-00 Jun-01 Jun-02 Jun-03 Jun-04 Jun-05 Jun-06 Jun-07 Jun-08 Jun-09 Jun-10 Jun-11 Jun-98 Jun-99 Jun-00 Jun-01 Jun-02 Jun-03 Jun-04 Jun-05 Jun-06 Jun-07 Jun-08 Jun-09 Jun-10 Jun-11 Jun-98 Jun-99 Jun-00 Jun-01 Jun-02 Jun-03 Jun-04 Jun-05 Jun-06 Jun-07 Jun-08 Jun-09 Jun-10 Jun-11 Development of trading volume in asset markets CBOT C, S, W, 1 st gen NYMEX WTI Crude oil, 1 st gen DJ Equity REIT Index Sharp decrease, not zero S&P 500 Index Significant increase in commodity trading volume after 2006 Source: Bloomberg 3

4 Research objective Investigate whether market interdependence and volatility transmission between agricultural commodity markets and financial asset markets increases A B In normal markets: As a result of portfolio rebalancing and asset weight adjustments. In crisis markets: As s a result of real asset substitution and use of agricultural commodities as refuge assets. 4

5 Methodology Selection criteria Methodology Application examples Multivariate (~8 variables) Link to economic theory Account for potential regimeswitches Structural VAR (rolling estimation) Generalized Forecast Error Variance Decompositions (Pesaran and Shin, 1998) Volatility spillover indices Diebold and Yilmaz (2009, 2012) Dimpfl and Jung (2012) 5

6 Modeling steps I Selection of included financial and commodity assets Data gathering II Computation of volatility proxies III IV Estimation of rolling VAR models Generalized forecast error variance decompositions (FEVDs) Calculation of volatility spillover indices 6

7 I Assets included in the analysis Commodity Financial Agricultural Corn, CBOT (C1)* Wheat, CBOT (W1)* Soybeans, CBOT (S1)* Energy WTI Crude oil, NYMEX (CL1)* Equity S&P 500 Index (SPX) Real estate DJ Equity All REIT Index (REIT) Fixed income 10-y-U.S. Treasury, CBOT (TY1)* 3 June Mar 2012/ Daily data Foreign exchange ICE Futures U.S. Dollar Index (DXY) * Future contracts, 1 st generic (Bloomberg), rolling relative to expiration, contracts rolled after last trading day of front month 7

8 Volatility proxies used in the models Range-based volatility* ˆ II Range, it High 1 P it ln Low 4ln 2 Pit Return-based volatility 2 Focus Pro: Captures intraday movements Con: May show high volatility in times of a persistent trend in returns May be inflated due to intraday periods of low trading volume m 1 ˆ ( m) ( R R ( m)) Re turn, it it n i m 1 n 1 with R it Close P it ln Close P it 1 and m = 5, 30, 90, Pro: Captures trends Con: Neglects intraday movements Sensitive to included no. of observations/ time period of investigation * based on Parkinson (1980) 8

9 II Asset volatility profiles(range-based, Annualized*) * Multiplied by Source: Own calculations 9

10 III Estimation of VARs Rolling regression Model Specification* Included observations (No. per variable = T) No. of windows w = 252 (T-w + 1) log ˆ VAR(4) 06/03/98 03/30/12 (3,488) 3,237 Range log (5) ˆReturn VAR(1) 06/10/98 03/30/12 (3,483) 3,232 log (90) ˆReturn VAR(1) 10/09/98 03/30/12 (3,398) 3,147 * Lag length selected with SBC, VAR models for 30 and 180 day return-based volatilities estimated, results not reported 10

11 IV Generalized vs. Orthogonalized impulse responses MA representation: IR function: y u t h t h h 0 j () h Restriction required Focus Orthogonalized Generalized (Pesaran and Shin 1998) Response to specific shock in one variable (equation j, c.p.) Via Cholesky decomposition of covariance matrix = PP : () h Pe o j h j Response to typical composite shock (equation j and others) Via information on history contained in estimated : 1 g 2 j ( h) jj h ej Sensitive to ordering Theory required jj = variance of errror term for j th equation e j = selection vector (1 as jth element, 0 otherwise) Not sensitive to ordering Sensitive to past behavior h = time period for forecast 11

12 IV Variance decompositions and volatility spillovers Generalized FEVDs Spillover indices (Diebold and Yilmaz 2009, 2012) Own variance shares: fraction of H-step ahead FEVs for one asset class (i) that are due to shocks to this asset class (i). Spillovers (cross variance shares): fraction of H-step ahead FEVs for one asset class (i) that are due to shocks to another asset class (j). Total spillovers (H) = sum of spillovers across all asset classes in relation to the total forecast error variance. Directional spillovers FROM (H) = spillovers received by asset i from all other assets j = 1,,N, j i in relation to the total forecast error variance. Directional spillovers TO (H) = spillovers transmitted by asset i to all other assets j = 1,,N, j i in relation to the total forecast error variance. Net (pair wise) spillovers (H) = spillovers transmitted by asset i to all other assets j = 1,,N, j i (one asset j) spillovers received by asset i from all other assets j = 1,,N, j i (one asset j) in relation to the total forecast error variance. 12

13 IV Index calculations based on FEVDs Own variance share Cross variance shares (spillovers) C1 S1 W1 CL1 SPX REI TY1 DXY (j) * C1 S1 W1 CL1 SPX REI TY1 DXY (i) N Matrix with FEVDs for a given forecast horizon H * Entries have been normalized with row sum A B C D Total spillover index = Sum of cross-variance shares rows 1:N / Sum of all variance shares rows 1:N (=N) * 100 Spillover index FROM all j to i = Sum of cross variance shares in row (i)/ sum of all variance shares in rows 1:N (= N) *100 Spillover index from i TO all j = Sum of cross variance shares in column (i)/ sum of all variance shares in columns 1:N (= N) * 100 Net (pairwise) spillover index i= Spillover index from i TO all j (one j) spillover index FROM all j (one j) to i Source: Diebold and Yilmaz (2012, 2009) 13

14 Percent Percent IV Results Total volatility spillover index, H = 10 Nasdaq crash, end of dot.com bubble (03/03) Stock market downturn of 2002 Low real GDP growth in EU 27 and US September 11 Beginning of war in Afghanistan, Invasion in Iraq Continued reduction of EU buffer stocks Growth in imports from China (soybeans) and India Early 2000s recession (03/00 12/03) log ˆ Range, it Time (end of regression window) log (5) Late 2000s recession (01/07 ) log ˆ (90) ˆRe turn, it Re turn, it Subprime crisis/ Sovereign Bond Crisis Low /negative real GDP growth in in EU27 and US Aftermath of Afghanistan/ Iraq wars 12 successive decreases of interest rates by Fed b/w Aug 07 and Dec 08 Biofuel mandates in EU and US Further growth in imports from China and India Low stock levels Commodity index fund trading volume growth 14

15 IV Net directional spillovers* (Range-based) * > 0 = net transmitter < 0 = net receiver 15

16 IV Pairwise analysis* (Range-based): Corn * > 0 = net transmitter < 0 = net receiver 16

17 IV Pairwise analysis* (Range-based): Wheat * > 0 = net transmitter < 0 = net receiver 17

18 IV Pairwise analysis* (Range-based): Soybeans * > 0 = net transmitter < 0 = net receiver 18

19 IV Pairwise analysis* (Range-based): Crude oil * > 0 = net transmitter < 0 = net receiver 19

20 First insights and preliminary conclusions Total volatility spillovers generally increase during times of financial crises Net volatility spillovers from equity and real estate markets reached high levels during and after subprime crisis Commodities (except soybeans) mostly net receivers of volatility spillovers during and after subprime crisis crude oil net transmitter of volatility during early 2000 crisis Most effects more pronounced in the short-term (range-based / 5D return-based) No general evidence on effects of financial crises on intra-commodity market spillovers Some evidence for closer integration of commodity and financial asset markets during times of crises Some evidence for a structural change in volatility spillovers in soybean-corn and soybean-wheat market pairs, soybean market net volatility transmitter 20

21 Robustness checks and possible extensions Robustness checks Sensitivity analysis (e.g. different lag lengths (HQ, AIC criteria), different forecast horizons, different window size) Check for whiteness of residuals for each window (Ljung Box Test, Breusch-Godfrey LM Test) Check for structural breaks within the windows Planned extensions Use of index composed of wheat, corn, soybeans (weight e.g. trading volume?) Check for structural breaks within volatility spillover indices Complementary structural analysis (e.g. Impulse responses, Granger Causality Analysis) Inclusion of metal markets Introduction of seasonality (e.g. harvest dummies) Comparison with conditional volatility model (M-GARCH) Use of implied volatility 21

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