1

Information content of commodity futures prices

for monetary policy

MSc student: Laura Alina GheorgheSupervisor: Professor Moisă Altăr, PhD

Bucharest, July 2008

The Academy of Economic StudiesDoctoral School of Finance and Banking

Agenda of the presentation

2

Purpose of the research

Methodology

VAR: Vector Autoregresssive Technique

CCF: Cross Correlation Function

Conclusions

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1. Purpose of the research

►This paper empirically examines the information content of commodity

futures prices for monetary policy (e.g., consumer prices and industrial

production).

► Commodity prices and the general price level tend to be closely

related, with movements in the former leading movements in the latter

► Empirical results show that commodity prices can serve as

information variables for monetary policy not only in mean, but also in

variance.

Perspective

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Pro

Primary goods are inputs for manufactured goods, hence changes in commodity prices directly influence production costs and the general price level.

Most commodity prices are determined in auction markets, hence they reflect demand or supply shocks more rapidly than do the prices of manufactured goods

A rise in commodity prices may indicate to policymakers that the economy is growing too rapidly and hence inflation is inclined to rise.

Cons

Commodity prices are subject to large, market-specific shocks, which may not have macroeconomic implications

Commodity price movements are the result of macroeconomic/monetary factors

Earlier studies

5

Pro

Commodity price indices can be appropriately used as information variables, but are in no way suited for use as intermediate targets - Garner (1989), Sephton (1991)

The use of commodity prices as information variables in monetary policy management can boost economic performance - Cody and Mills (1991)

Commodity price indices impact both the consumer price index and industrial production index, but neither the consumer price index nor industrial production index impacts commodity price indices - Awokuse and Yang (2003)

Cons

Commodity price indices change partly in response to macroeconomic factors - Hua (1998)

The increases in commodity prices during the 1970s were the result of monetary policy - Barsky and Kilian (2001)

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2. Methodology

VAR - I study the impact of an increase (decrease) in commodity futures prices on the consumer prices and industrial production using vector autoregressive technique

→ Impulse response functions will show how the conditional forecast of one variable would change in response to the shock in another variable of the VAR system.

CCF - The Cross Correlation Function developed by Cheung and Ng (1996) is an outstanding approach permitting analysis of not only causality-in-mean but also causality-in-variance.

Data

The empirical research in this paper is performed using monthly U.S. data covering the period from January 1957 through May 2008:

• Reuters-CRB index (a futures index) – CRB• The consumer price index – CPI• The industrial production index - IP

VAR

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Results

Approach

Impulse function

VAR

VAR Results

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CPI_GR

CRB_GR

The coefficient 0.019418 of the CRB_GR in the CPI_GR equation at lag 1 is statistically significant at the 5% level. Also, the coefficient 0.025217of the CRB_GR in the IP_GR equation at lag 1 is statistically significant at the 5% level.

The R-squared of the CPI_GR equation is 0.425997 and the R-squared of the IP_GR is 0.181746. These give a relatively high precision to the equation’s estimation.

IP_GR

= 0.6198891073*CPI_GR(-1) + 0.01941835531*CRB_GR(-1) – 0.01463222361*IP_GR(-1) + 0.001248143717

= - 0.2952419548*CPI_GR(-1) + 0.02521714764*CRB_GR(-1) + 0.3838829559*IP_GR(-1) + 0.002425350615

= - 0.05852934065*CPI_GR(-1) – 0.001476201864*CRB_GR(-1) +

0.1437975626*IP_GR(-1) + 0.002904131689

Approach

There are no causal relationships from CPI and industrial production to the CRB futures prices. In contrast, there are clear causal relationships from CRB futures prices to CPI and industrial production. To check the robustness of empirical results, we have reported the test statistic and corresponding p-value.

Awokuse and Yang (2003) found causal relationships from CRB to CPI and industrial production in the United States over the period between 1975 and 2001. Our results support theirs for a longer sample period, i.e., between 1957 and 2008. These empirical findings provide additional support for the notion that commodity prices can play an informational role in the formulation of monetary policy.

Impulse function

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.0000

.0001

.0002

.0003

.0004

.0005

.0006

.0007

.0008

1 2 3 4 5 6 7 8 9 10

Response of CPI_GR to NonfactorizedOne S.D. CRB_GR Innovation

-.0004

.0000

.0004

.0008

.0012

.0016

1 2 3 4 5 6 7 8 9 10

Response of IP_GR to NonfactorizedOne S.D. CRB_GR Innovation

CCF – Cross Correlation Function Approach

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The Model

Causal relationships

Results

CCF

CCF Model

The procedure is based on the residual cross correlation function and is robust to distributional assumptions. In the first of the two steps, we estimate a set of univariate time-series models that allow for time variation in both conditional means and conditional variances. The second step is conducted by constructing the residuals standardized by conditional variances and the squared residuals standardized by conditional variances. The CCF of the standardized residuals is used to test the null hypothesis of no causality-in-mean, and the CCF of squared-standardized residuals is used to test the null hypothesis of no causality-in-variance.

the sample cross-correlation coefficient at lag k, ,from the consistent estimates of the conditional mean and

variance of Xt and Yt.

Under the condition of regularity, it holds that:

This test statistic can be used to test the null hypothesis of no causality-in-mean. To test for a causal relationship at a specified lag k, we compare with the standard normal distribution. If the test statistic is larger than the critical value of normal distribution, then we reject the null hypothesis.

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( )( )

(0) (0)

c kr k

c c

1( ) ( )( )t t kc k

T

( ) (0,1)LiT r k N

( )r k

( )r k

CCF Model

For the causality-in-variance test:

Causality in the variance of Xt and Yt can be tested by examining the squared standardized residual CCF, .

Under the condition of regularity, it holds that:

This test statistic can be used to test the null hypothesis of no causality-in-variance. To test for a causal relationship at a specified lag k, we compare with the standard normal distribution. If the test statistic is larger than the critical value of normal distribution, then we reject the null hypothesis.

13

( )( )

(0) (0)uv

uv

uu vv

c kr k

c c

1( ) ( )( )uv t t kc k u u v v

T

( )uvr k

( ) (0,1)Luv iT r k N

( )uvr k

Causal relationships using CCF approach

We estimate a series of univariate time-series models to allow for time variation in both the conditional mean and conditional variance. The AR(k)-EGARCH(1,1) model is used to model the dynamics of each variable. The conditional mean and conditional variance are respectively expressed as follows:

These models are applied to the growth rates of the futures price index, consumer price index, and industrial production index. Each model is estimated by the method of maximum likelihood.

The SBIC and residual diagnostics are used to check the specification of the models. Using ADF test, by the Schwartz Info Criterion, we define the number of lags used for the specification of the models.

The following models are thus selected: the AR(1)-EGARCH(1,1) model for the CRB commodity prices, the AR(12)-EGARCH(1,1) model for CPI, and the AR(3)-EGARCH(1,1) model for industrial production.

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0 11

p

t i t tix a a x

2 21( ) 0, ( )t t t t tE E

2 21 11

1 1

log( ) log( )t tt t

t t

Empirical results of AR-EGARCH model

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CRB CPI IP Mean equation p value CRB p value CPI p value IP a0 0.003063 0.0151 0.003411 0.0001 0.00252 0.0001 a1 0.001619 0.968 0.332235 0 0.352659 0 a2 0.070586 0.0962 0.074538 0.0812 a3 0.007872 0.8524 0.080355 0.0464 a4 0.080214 0.0562 a5 0.044727 0.288 a6 0.098188 0.0199 a7 0.051296 0.226 a8 0.014386 0.7342 a9 0.13687 0.0013 a10 0.091226 0.0331 a11 0.147115 0.0006 a12 -0.171894 0 Variance equation ω -0.29367 0.0002 -3.335973 0 -1.07644 0 α 0.178972 0 0.504193 0 0.242601 0 β 0.978695 0 0.754566 0 0.908621 0 γ 0.085588 0 0.020268 0.6818 -0.05507 0.0044

■ shows the statistically significance at the 5% level.

Cross correlation analysis for the levels and squares of the standardized residuals: CRB and CPI

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CRB &

CPI

Mean Variance

Test statistics

k

Levels

CRB and CPI (-k) CRB and CPI (+k) 0 -1.49417511 1 0.26513555 6.645734072 2 0.329561011 2.123562304 3 1.665150372 1.194348927 4 0.297348281 3.374902985 5 0.61451978 -0.104071898 6 1.712230516 2.443211706 7 1.437183356 1.122489759 8 -0.716113776 1.058064299 9 0.079292875 0.57239544

10 1.184437318 0.4237213 11 1.3777137 -0.723547483 12 -1.207478171 2.137698341

Test statistics

k

Squares

CRB and CPI (-k) CRB and CPI (+k) 0 -0.027256926 1 -0.428677105 5.619882504 2 -0.822663576 -1.712230516 3 1.030807373 -1.045674787 4 -0.106549801 1.575945887 5 -0.569917538 -1.848515145 6 0.686378948 1.320721947 7 1.305854532 -0.173453164 8 -0.792928748 -0.007433707 9 -0.473279347 -0.579829147

10 1.333111458 0.379119058 11 3.122156947 -0.460889835 12 -1.727837662 2.423388487

Cross correlation analysis for the levels and squares of the standardized residuals: CRB and CPI

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CRB &

CPI

Mean Variance

CRB prices uni-directionally cause CPI in mean.

The causation pattern in mean is of lags 1, 2, 4, 6 and 12 from CRB prices to CPI.

This is consistent with the results of the VAR analyze.

As shown earlier in other studies, our findings indicate that commodity prices are useful in predicting inflation.

CRB prices cause CPI in variance at lags 1 and 12.

It is interested to note that CRB prices cause CPI in variance up to lag 12.

This evidence reinforces support for the notion that commodity prices can play an informational role in formulating monetary policy.

Cross correlation analysis for the levels and squares of the standardized residuals: CRB and IP

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Mean Variance

CRB &

IP

Test statistics

k

Levels

CRB and IP (-k) CRB and IP (+k) 0 1.647805055 1 0.577351245 2.398609464 2 1.541255255 0.77310553 3 -0.807796162 0.594696561 4 0.230444917 0.317171499 5 0.123895117 -0.535226905 6 0.156107847 -0.054513851 7 -0.081770777 -1.429749649 8 0.408853886 -1.129923466 9 -1.015939959 0.797884553

10 1.184437318 -0.411331788 11 0.733459092 -2.113650695 12 0.33947262 0.245312332

Test statistics

k

Squares

CRB and IP (-k) CRB and IP (+k) 0 2.321794491 1 1.325677751 3.550834051 2 1.466918184 2.800029643 3 -0.240356527 2.450645413 4 0.681423143 2.15081923 5 0.636820901 1.137357173 6 0.488146761 1.013462057 7 0.475757249 -0.322127304 8 0.854876307 -0.517881589 9 0.012389512 0.973815619

10 1.417360138 0.396464374 11 0.787972944 -1.263730193 12 0.364251644 0.257701843

Cross correlation analysis for the levels and squares of the standardized residuals: CRB and IP

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Mean Variance

CRB prices unidirectionally cause industrial production in mean.

Two different causality-in-mean lag patterns are found between these

two variables, at lag 1 and 11. These findings are consistent with

the results of VAR analyze.

There is a causality-in-variance pattern from CRB to IP at lag 1, 2, 3 and 4. CRB

& IP

Cross correlation analysis for the levels and squares of the standardized residuals: CPI and IP

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Mean Variance

CPI &

IP

Test statistics

k

Levels

CPI and IP (-k) CPI and IP (+k) 0 0.743370702 1 0.324605206 -0.547616417 2 0.874699526 -1.558600571 3 2.138429718 -0.713635874 4 1.846037242 -1.278597607 5 1.268685997 -1.167092002 6 1.174525709 -0.753282311 7 -0.57239544 -0.364251644 8 -0.004955805 0.864787916 9 -0.136284629 -1.449572868

10 0.594696561 -2.143385523 11 1.630459739 -1.776655977 12 0.552572222 -1.043196885

Test statistics

k

Squares

CPI and IP (-k) CPI and IP (+k) 0 0.934169182 1 0.537704807 1.19682683 2 0.775583432 0.58230705 3 1.075409615 1.033285275 4 -0.086726582 -0.019823219 5 -0.250268136 -0.569917538 6 0.069381265 -0.277525062 7 -0.612041878 0.26513555 8 0.745848604 1.744443246 9 0.393986472 1.253818583

10 1.162136197 0.564961733 11 0.750804409 0.195754285 12 -0.270091355 0.795406651

Cross correlation analysis for the levels and squares of the standardized residuals: CPI and IP

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Mean Variance

There is a causality relationship between CPI and industrial production in mean.

We find a feedback of these two

variables in mean. Industrial production causes CPI in mean at lag 3, and CPI causes industrial production in mean at lag 10.

We note with interest that no causality-in-variance is found between these two variables.CPI

& IP

Final conclusions

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We sought, in this paper, to analyze whether commodity prices (CRB prices) have causal relationships with CPI and industrial production, and vice versa.

Our VAR analysis results reveal that CRB prices influence CPI and industrial production, whereas CPI and industrial production do not influence CRB prices. This confirms that the empirical results from Awokuse and Yang (2003) and hold for a longer sample period.

Our results using the CCF approach indicate that CRB prices cause CPI and industrial production in mean, whereas CPI and industrial production do not cause CRB prices in mean.

We thus find that the results from the CCF approach are consistent with the results from the VAR analyze, and that commodity prices are useful as a leading indicator of inflation and industrial production.

When we used the results of the CCF approach to check for causality-in-variance, the CRB prices caused CPI in variance. Commodity price index uncertainty, therefore, is a signal of future consumer price index uncertainty as well. This new evidence provides additional support for the notion that commodity prices can play an informational role in formulating monetary policy.

Final conclusions

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In summary, the analyses performed clearly demonstrate that commodity price indices provide information on future changes in prices and production, and are valuable information variables for monetary policy management. Commodity price indices serve as important information variables for monetary policy management as signals of future movements in macroeconomic variables.

Uncertainty in the consumer price index signals uncertainty in future prices, and therefore includes much more information than was previously suspected. Sims (1998) and Sims and Zha (1998) emphasize the importance of introducing the commodity price variable in designing monetary policy rules. Our results empirically support their discussion and indicate that researchers should include commodity prices as information variables when they construct monetary econometric models.

Our findings suggest that commodity prices can help monetary authorities in formulating monetary policy as they may provide signals about the future direction of the economy, including inflation and other macroeconomic activities such as industrial production.

References

Andrews, D.W.K., 1993. Tests for parameter instability and structural change with unknown change point. Econometrica 61, 821–856.

Awokuse, T.O., Yang, J., 2003. The information role of commodity prices in formulating monetary policy: a re-examination. Economics Letters 79,219–224.

Barsky, R.B., Kilian, L., 2001. Do We Really Know That Oil Caused the Great Stagflation? A monetary view. NBER Working Paper, vol. 8389.

Bernanke, B.S., 2004. Inflation targeting. Federal Reserve Bank of St. Louis Review 86, 165–168 (July/August). Bernanke, B.S., Gertler, M., Watson, M., Sims, C.A., Friedman, B.M., 1997. Symmetric monetary policy and the

effects of oil price shocks. Brookings Papers on Economic Activity 1, 91–174. Cheung, Y.-W., Ng, L.K., 1996. A causality-in-variance test and its application to financial market prices. Journal

of Econometrics 72, 33–48. Cody, B.J., Mills, L.O., 1991. The role of commodity prices in formulating monetary policy. Review of Economics

and Statistics 73, 358–365. Engle, R.F., Granger, C.W.J., 1987. Co-integration and error correction: representation, estimation, and testing.

Econometrica 55, 251–276. Garner, A.C., 1989. Commodity prices: policy target or information variables? Journal of Money, Credit and

Banking 21, 508–514. Gorton, G., Rouwenhorst, K.G., 2006. Facts and fantasies about commodity futures. Financial Analysts Journal

62, 47–68. Hamori, S., 2003. An Empirical Investigation of Stock Markets: The CCF Approach. Kluwer Academic Publishers,

Boston. Hansen, B.E., 1997. Approximate asymptotic p-values for structural change tests. Journal of Business and

Economic Statistics 15, 60–67. Hua, P., 1998. On primary commodity prices: the impact of macroeconomic/monetary shocks. Journal of Policy

Modeling 20, 767–790.

References

Johansen, S., 1988. Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control 12, 231–254.

Johansen, S., Juselius, K., 1990. Maximum likelihood estimation and inference on cointegration with application to the demand for money. Oxford

Bulletin of Economics and Statistics 52, 169–209. Marquis, M.H., Cunningham, S.R., 1990. Is there a role of commodity prices in the design of monetary policy?

Some empirical evidence. Southern Economic Journal 57, 394–412. Nelson, D.B., 1991. Conditional heteroskedasticity in asset returns: a new approach. Econometrica 59, 347–370. Poon, S.-H., Granger, C.W.J., 2003. Forecasting volatility in financial markets: a review. Journal of Economic

Literature 41, 478–539. Ross, S.A., 1989. Information and volatility: the non-arbitrage Martingale approach to timing and resolution

irrelevancy. Journal of Finance 42, 541–566. Sephton, P.S., 1991. Commodity prices: policy target or information variables? A comment. Journal of Money,

Credit and Banking 23, 260–266. Sims, C., 1998. The role of interest rate policy in the generation and propagation of business cycles: what has

changed since the 30's? In: Fuhrer, J.C., Schuh, S. (Eds.), Beyond Shocks: What Causes Business Cycles? Conference Series, vol. 42. Federal Reserve

Bank of Boston. Sims, C., Zha, T., 1998. Does Monetary Policy Generate Recessions? Working Paper, vol. 98-12. Federal

Reserve Bank of Atlanta. Toda, H.Y., Yamamoto, T., 1995. Statistical inference in vector autoregression with possibly integrated processes.

Journal of Econometrics 66, 225–250. R. Bhar, S. Hamori / Economic Modelling 25 (2008) 274–283 283

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