Causal structure among US corn futures and regional cash prices in the time and frequency domain

ABSTRACT

This study investigates causal structure among daily Chicago Board of Trade corn futures prices and seven regional cash series from Iowa, Illinois, Indiana, Ohio, Minnesota, Nebraska, and Kansas for January 2006–March 2011. Their wavelet transformed series are further analyzed for causal relationships at different time scales. Empirical results indicate no causality among states or between the futures and a cash series for time scales shorter than one month. As scales increase but do not exceed a year, bidirectional causal flows are determined among all prices. The information leadership role of the futures against a cash price is identified for the scale longer than one year and raw series, at which no interstate causality is found.

KEYWORDS:

• Corn
• cash
• futures
• causality
• wavelet

Acknowledgments

The author acknowledges Kevin McNew and Geograin, Inc of Bozeman, Montana for generously providing the data used in the analysis in this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Gençay et al. [27] and Ramsey [61] explained how wavelet analysis could be utilized in economic and finance. Several studies in these two areas that use wavelet analysis include but are not limited to: Ramsey and Zhang [64] – foreign exchanges, Davidson et al. [19] – commodity price behavior, Ramsey and Lampart [62,63] – decomposition of the relationship between expenditure and income, Pan and Wang [56] – stock market inefficiency, Lin and Stevenson [50] – causality between the equity spot and futures market, Gençay et al. [28,29] – systematic risk in an asset pricing model, Kim and In [43] – the relationship between financial variables and real economic activity, Almasri and Shukur [2] – causality between public expenditure and income, Dalkir [17] – causality between money supply and income, Lee [49] – causality among international stock markets, Kim and In [44] – the relationship between stock returns and inflation, Kim and In [45] – the Sharpe ratio, In and Kim [35] – hedge ratios and the relationship between the stock and futures market, Kim and In [46] – the relationship between changes in stocks prices and bond yields in the G7 countries, and Alzahrani et al. [3] – causality between oil spot and futures prices.

2. Investigations of contemporaneous causal orderings among price series (e.g. [84]) are not discussed because of this paper's focuses on lead–lag causality.

3. Wavelet analysis also has been utilized in the recent literature to explore the correlation/co-movement among economic and financial variables (see, e.g. [1,16,31,75–77]).

4. See, for example, [20].

5. Additional details of the data could be found from [85].

6. Unless stated otherwise, we will refer to ‘log prices’ as ‘prices’ hereafter.

7. Figure 10 shows the normalized trace test statistics calculated at each data point between 9 March 2006 (point 46) and 24 March 2011 (point 1316). The first 45 data points ranging from 3 January 2006 to 8 March 2006 are used as the base period. Recursive analysis is performed through adding a new observation each time to the base period to update the sample for calculating the trace test statistic. Eventually, this procedure will reach the end of the data, 24 March 2011 (point 1316). As shown in Figure 10, the test statistics are scaled by the 5% critical values. Therefore, we can reject the null hypothesis at a data point if its corresponding entry is greater than 1. For a few breaks found, that is, their associated data points have entries greater than 1 at the 5% significance level, they can be eliminated if the 1% significance level is used.