1 Information content of commodity futures prices for monetary policy MSc student: Laura Alina Gheorghe Supervisor: Professor Moisă Altăr, PhD Bucharest, July 2008 The Academy of Economic Studies Doctoral School of Finance and Banking

Agenda of the presentation 2 Purpose of the research Methodology VAR: Vector Autoregresssive Technique CCF: Cross Correlation Function Conclusions

3 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 4 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)

6 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 7 Results Approach Impulse function VAR

VAR Results 8 CPI_GR CRB_GR The coefficient of the CRB_GR in the CPI_GR equation at lag 1 is statistically significant at the 5% level. Also, the coefficient of 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 and the R- squared of the IP_GR is These give a relatively high precision to the equation’s estimation. IP_GR = *CPI_GR(-1) *CRB_GR(-1) – *IP_GR(-1) = *CPI_GR(-1) *CRB_GR(-1) *IP_GR(-1) = *CPI_GR(-1) – *CRB_GR(-1) *IP_GR(-1)

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 Our results support theirs for a longer sample period, i.e., between 1957 and 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 10

CCF – Cross Correlation Function Approach 11 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. 12

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

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. 14

Empirical results of AR-EGARCH model 15

Cross correlation analysis for the levels and squares of the standardized residuals: CRB and CPI 16 CRB & CPI MeanVariance

Cross correlation analysis for the levels and squares of the standardized residuals: CRB and CPI 17 CRB & CPI MeanVariance 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 18 MeanVariance CRB & IP

Cross correlation analysis for the levels and squares of the standardized residuals: CRB and IP 19 MeanVariance 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 20 MeanVariance CPI & IP

Cross correlation analysis for the levels and squares of the standardized residuals: CPI and IP 21 MeanVariance 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 22 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 23 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.

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