nach oben

Empirical Economics

Erschienen in:

31.08.2017

Cointegration and price discovery in US corn cash and futures markets

verfasst von: Xiaojie Xu

Erschienen in: Empirical Economics | Ausgabe 4/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Using prices from 182 cash markets from seven states and the Chicago Board of Trade futures, we investigate cointegration and price discovery for corn. Analysis based on cash–futures pairs reveals that cointegration holds for 52 cash markets and failures tend to happen farther away from futures delivery locations. Cash generally are as important as futures prices as information sources in the long run and cash to futures information flow is most likely in the short run. Contributions to price discovery also are measured quantitatively for cointegrated cases. Analysis based on state-level cash prices indicates bidirectional information flow between cash and futures prices under a bivariate model, and futures to cash information flow under the octavariate model with all cash and the futures series. Comparisons of the two models show that including local cash markets in a price relationship model highlights cointegration and the futures’ price discovery role and could benefit cash price forecasting. Finally, evidence of nonlinear causality is found.

Vorheriger Artikel Revisiting income and price elasticity of gasoline demand in India: new evidence from cointegration tests

Nächster Artikel Mixture periodic GARCH models: theory and applications

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Approximately 39.5, 30.8, 9.2, 8.4, 4.3, 2.5, 2.1, 1.8, 1.2, and 0.2% of corn is used in the feed/residual, fuel/ethanol, DDGs (Distiller’s Dried Grains with Solubles), direct export, HFCS (High-Fructose Corn Syrup), sweeteners, starch, cereal, beverage/alcohol, and seed segment, respectively.

As shown in the seminal work of Engle et al. (1983), weakly exogeneity and Granger causality are conditions required for forecasting.

On days such as holidays where prices are missing in each market, we omit the observations and assume smooth data continuity (Goodwin and Piggott 2001).

A possible periodic jump in this futures series caused by contract rolling biases against identifying cointegration for each cash market equally and should not affect empirical results substantially.

Unless stated otherwise, we will refer to “log prices” as “prices” hereafter.

Johansen’s trace test has been shown to be more robust as compared to the maximum eigenvalue test (see, e.g., Cheung and Lai 1993). For the current study, these two tests arrive at the same results about cointegrating ranks.

As discussed in Sect. 3, unit root tests at the 5% significance level show that all of the price series are not stationary in levels but stationary in differences.

Before the nonlinear Granger causality test, the BDS test (Broock et al. 1996) is applied to investigate nonlinearities of the residuals and thus determines the appropriateness of the nonlinear test.

For our case, $p=2$ and $X_{t}=\left( \begin{array}{c} C_{\mathrm{{t}}} \\ F_{\mathrm{{t}}} \end{array} \right) $, where $C_\mathrm{{t}}$ and $F_\mathrm{{t}}$ stand for cash and futures prices, respectively. Equation (1) can be written as:

$$\begin{aligned} H_{0}{:} \left( \begin{array}{c} \Delta C_\mathrm{{t}} \\ \Delta F_{\mathrm{{t}}} \end{array} \right) =\mu +\left( \begin{array}{cc} \Pi _{11} &{} \Pi _{12} \\ \Pi _{21} &{} \Pi _{22} \end{array} \right) \left( \begin{array}{c} C_{\mathrm{{t}}-1} \\ F_{\mathrm{{t}}-1} \end{array} \right) +\sum _{i=1}^{k-1}\left( \begin{array}{cc} \Gamma _{i,11} &{} \Gamma _{i,12} \\ \Gamma _{i,21} &{} \Gamma _{i,22} \end{array} \right) \left( \begin{array}{c} \Delta C_{\mathrm{{t}}-i} \\ \Delta F_{\mathrm{{t}}-i} \end{array} \right) +e_{t}. \end{aligned}$$

For our $p=2$ case, (a) corresponds to:

$$\begin{aligned} \left( \begin{array}{c} \Delta C_\mathrm{{t}} \\ \Delta F_\mathrm{{t}} \end{array} \right) =\mu _{0}+\left( \begin{array}{c} \alpha _{1} \\ \alpha _{2} \end{array} \right) \left( \begin{array}{cc} \beta _{1}&\beta _{2} \end{array} \right) \left( \begin{array}{c} C_{\mathrm{{t}}-1} \\ F_{\mathrm{{t}}-1} \end{array} \right) +\sum _{i=1}^{k-1}\left( \begin{array}{cc} \Gamma _{i,11} &{} \Gamma _{i,12} \\ \Gamma _{i,21} &{} \Gamma _{i,22} \end{array} \right) \left( \begin{array}{c} \Delta C_{\mathrm{{t}}-i} \\ \Delta F_{\mathrm{{t}}-i} \end{array} \right) +e_{t}, \end{aligned}$$

and (b) corresponds to:

$$\begin{aligned} \left( \begin{array}{c} \Delta C_\mathrm{{t}} \\ \Delta F_\mathrm{{t}} \end{array} \right) =\left( \begin{array}{c} \alpha _{1} \\ \alpha _{2} \end{array} \right) \left( \left( \begin{array}{cc} \beta _{1}&\beta _{2} \end{array} \right) \left( \begin{array}{c} C_{\mathrm{{t}}-1} \\ F_{\mathrm{{t}}-1} \end{array} \right) +\delta ^{^{\prime }}\right) +\sum _{i=1}^{k-1}\left( \begin{array}{cc} \Gamma _{i,11} &{} \Gamma _{i,12} \\ \Gamma _{i,21} &{} \Gamma _{i,22} \end{array} \right) \left( \begin{array}{c} \Delta C_{\mathrm{{t}}-i} \\ \Delta F_{\mathrm{{t}}-i} \end{array} \right) +e_{t}. \end{aligned}$$

Shu and Zhang (2012), Francis et al. (2010), Dergiades et al. (2013), Fujihara and Mougoué (1997), Silvapulle and Moosa (1999), Ajayi and Serletis (2009), and Dergiades (2012) gave a formal description of the nonlinear Granger causality test.

Before the nonlinear Granger causality test, data nonlinearities are examined (Francis et al. 2010; Dergiades et al. 2013) by applying the BDS test suggested by Broock et al. (1996) to the residuals from an ECM or a VAR in differences (Dergiades et al. 2013; Fujihara and Mougoué 1997). The BDS test essentially inspects the validity of the identically and independently distributed (i.i.d) assumption on time series.

All tests in Table 1, 2 are performed at the 5% significance level.

We follow Bekiros and Diks (2008a, b) and discuss results for one lag. However, it is possible to perform the test for longer lags (see, e.g., Diks and Panchenko (2006).

Johansen’s trace statistic tests the nested hypotheses: $null:r=r_{0}$ versus $ alternative:r>r_{0}$ for $r_{0}=0$, 1, 2, ..., $p-1$, where p is the number of variables. Johansen (1992) proposed a sequential testing procedure to determine the number of cointegrating vectors. Hypotheses are tested in the following order: $H_{1}^{*}(0)$, $H_{1}(0)$, $H_{1}^{*}(1)$, $ H_{1}(1)$, ..., $H_{1}^{*}(p)$, $H_{1}(p)$. For example, $H_{1}^{*}(1)$ can only be rejected if also $H_{1}^{*}(0)$ and $H_{1}(0)$ are rejected, and $H_{1}(1)$ can only be rejected if also $H_{1}^{*}(0)$, $ H_{1}(0)$, and $H_{1}^{*}(1)$ are rejected. Testing is terminated and the corresponding hypothesis is not rejected at the first failure to reject the null in the testing sequence. Johansen’s maximum eigenvalue statistic tests the hypotheses: $null:r=r_{0}$ versus $alternative:r=r_{0}+1$ for $ r_{0}=0 $, 1, 2, ..., $p-1$.

Readers may be interested in cointegration results based on unlogged prices. For 171 (94%) of the 182 cash markets, the results are qualitatively the same as those based on logged prices.

For example, the null hypothesis of the ADF test without (with) trend at the 5% significance level is rejected for 85% (77%) of the basis of the 52 cash markets cointegrated with the futures and only 10% (3%) of that of the 130 cash markets not cointegrated with the futures.

Weekly Midwest No 2 diesel retail prices are obtained from the US Energy Information Administration.

Empirical results based on state-level cash price series in Sect. 6 reveal that adding more cash markets to a model could help identify cointegration.

Indiana is an exception because it 8 cointegrating and 7 non-cointegrating cash markets of the futures.

One exception exists for Iowa and Nebraska (see Table 3) because the ratio of cash markets cointegrated with the futures in Iowa is smaller than that in Nebraska.

The logit model estimated is $logit(cointegration)=2.5978-0.7259\times \textit{distance}$. The marginal effect of distance on cointegration is $\frac{{\partial p(cointegration)}}{{\partial {\textit{distance}}}} = \frac{{ - 0.7259{e^{ - 2.5978 + 0.7259 \times {\textit{distance}}}}}}{{{{(1 + {e^{ - 2.5978 + 0.7259 \times {\textit{distance}}}})}^2}}}$, revealing that distance has a negative effect on the probability toward cointegration.

Empirical results based on state-level cash price series in Sect. 6 reveal that adding more cash markets to a model could highlight the price discovery role of the futures market. Future research comparing cointegration and price discovery results for a cash and the futures market with different numbers of other cash markets incorporated into a model is thus of interest.

Coefficients $\Gamma _{i,12}$’s and $\Gamma _{i,21}$’s associated with cash markets 48, 50, 56, 103, 129, and 130 are not plotted in Fig. 6 . The optimal numbers of lags corresponding to these markets are 3, 4, 3, 4, 4, and 2, respectively, under the VAR in levels representation. $\Gamma _{1,12}=0.467$, $\Gamma _{2,12}=0.154$, $\Gamma _{1,21}=0.0595$, and $\Gamma _{2,21}=0.0201$ for market 48; $\Gamma _{1,12}=0.544$, $\Gamma _{2,12}=0.305$ , $\Gamma _{3,12}=0.148$, $\Gamma _{1,21}=0.0941$, $\Gamma _{2,21}=0.0346$, and $\Gamma _{3,21}=0.0253$ for market 50; $\Gamma _{1,12}=-0.00555$, $ \Gamma _{2,12}=0.0426$, $\Gamma _{1,21}=0.192$, and $\Gamma _{2,21}=0.0813$ for market 56; $\Gamma _{1,12}=-0.0175$, $\Gamma _{2,12}=0.0327$, $\Gamma _{3,12}=-0.0973$, $\Gamma _{1,21}=0.337$, $\Gamma _{2,21}=0.0738$, and $ \Gamma _{3,21}=0.192$ for market 103; $\Gamma _{1,12}=-0.0992$, $\Gamma _{2,12}=0.0729$, $\Gamma _{3,12}=0.211$, $\Gamma _{1,21}=0.0544$, $\Gamma _{2,21}=-0.0109$, and $\Gamma _{3,21}=-0.000210$ for market 129; $\Gamma _{1,12}=0.165$, and $\Gamma _{1,21}=0.0937$ for market 130.

See Footnote 10.

Coefficients $\Gamma _{i,12}$’s and $\Gamma _{i,21}$’s associated with cash markets 133, 144, 145, 147, 148, 158, 168, 176, 179, and 182 are not plotted in Fig. 6. The optimal numbers of lags corresponding to these markets are 4, 2, 2, 3, 3, 2, 2, 3, 2, and 2, respectively, under the VAR in levels representation. $\Gamma _{1,12}=-0.0637$, $\Gamma _{2,12}=0.0310$, $ \Gamma _{3,12}=0.0916$, $\Gamma _{1,21}=0.00901$, $\Gamma _{2,21}=-0.0665$, and $\Gamma _{3,21}=0.0794$ for market 133; $\Gamma _{1,12}=-0.120$, and $ \Gamma _{1,21}=0.0889$ for market 144; $\Gamma _{1,12}=0.0972$, and $\Gamma _{1,21}=0.0895$ for market 145; $\Gamma _{1,12}=0.321$, $\Gamma _{2,12}=0.149 $, $\Gamma _{1,21}=0.169$, and $\Gamma _{2,21}=0.0109$ for market 147; $\Gamma _{1,12}=0.350$, $\Gamma _{2,12}=0.145$, $\Gamma _{1,21}=0.121$, and $\Gamma _{2,21}=-0.0212$ for market 148; $\Gamma _{1,12}=0.0313$, and $\Gamma _{1,21}=0.121$ for market 158; $\Gamma _{1,12}=-0.247$, and $\Gamma _{1,21}=0.306$ for market 168; $\Gamma _{1,12}=0.507$, $\Gamma _{2,12}=0.209$, $\Gamma _{1,21}=0.0637$, and $\Gamma _{2,21}=0.00820$ for market 176; $\Gamma _{1,12}=-0.103$, and $\Gamma _{1,21}=0.128$ for market 179; $\Gamma _{1,12}=-0.0571$, and $\Gamma _{1,21}=0.167$ for market 182.

Two exceptions are cash markets 148 and 173 for which the information share average finds that the cash market contributes more to price discovery while the common factor weight reveals an opposite result. However, the numerical results provided by the two approaches are almost identical for each of these two markets (see Table 2).

As pointed out by Baillie et al. (2002), this does not indicate that one method is better than another because they differ in the focuses of price discovery measurements and have their own merits (de Jong 2002).

Detailed numerical results are available upon request.

The ADF, PP, and KPSS test with or without trend reveal that the basis is not stationary for Iowa, Indiana, Ohio, Minnesota, Nebraska, and Kansas. For Illinois, the KPSS test without trend shows that its basis is not stationary. The plot of the basis is available upon request.

In the long run, the null hypotheses that $\alpha _{Futures}=0$, $\alpha _{IA}=0$, $\alpha _{IL}=0$, $\alpha _{IN}=0$, $\alpha _{OH}=0$, $\alpha _{MN}=0$, $\alpha _{NE}=0$, and $\alpha _{KS}=0$, where $\alpha _{Futures}$, $\alpha _{IA}$, $\alpha _{IL}$, $\alpha _{IN}$, $\alpha _{OH}$, $\alpha _{MN} $, $\alpha _{NE}$, and $\alpha _{KS}$ are elements of the loading matrix of the octavariate ECM, are rejected at the 5% significance level, indicating that futures and seven state-level cash series all respond to disturbances in the long-run equilibrium. We also test the null hypotheses that ${\left| \alpha _{F}\right| =\left| \alpha _{IA}\right| }$, ${\left| \alpha _{F}\right| =\left| \alpha _{IL}\right| }$, ${\left| \alpha _{F}\right| =\left| \alpha _{IN}\right| }$, ${\left| \alpha _{F}\right| =\left| \alpha _{OH}\right| }$, $ {\left| \alpha _{F}\right| =\left| \alpha _{MN}\right| }$, ${\left| \alpha _{F}\right| =\left| \alpha _{NE}\right| }$, and ${\left| \alpha _{F}\right| =\left| \alpha _{KS}\right| }$, and they are all rejected at the 5% significance level, indicating that futures and each state-level cash series respond to disturbances at different magnitudes.

As we can see in Table 4, Nebraska and Kansas never have nonlinear causal relationships with the futures market because their observed individual markets are far away from futures delivery locations. However, while observed markets in Iowa and Minnesota also are farther away from futures delivery points than those in Illinois, Indiana, and Ohio, they are much closer to these points as compared to markets in Nebraska and Kansas. Thus, nonlinear relationships can still be detected. This again reveals that the price connection between a cash and the futures market tends to weaken as the distance between the cash and futures delivery point increases.

Log prices are used in the models. For our case, the log transformation stabilizes the variance of the underlying unlogged series and could be beneficial for forecasting (Lükepohl and Xu 2012). Readers are referred to Lükepohl and Xu (2012) for a detailed investigation into conditions under which taking logs is beneficial for forecasting. It is also worth comparing the bivariate with a trivariate, quadvariate, quinvariate, hexavariate, and heptavariate model. However, we need to consider 6, 15, 20, 15, and 6 versions of the trivariate, quadvariate, quinvariate, hexavariate, and heptavariate model, respectively, for each state. For simplicity, we focus on the comparison between the bivariate and octavariate model.

Xu and Thurman (2015) provided evidence that including more cash series in a VAR model can improve price forecasts of individual cash markets.

10-, 20-, 40-, 60-, and 90-day correspond to 2-, 4-, 8-, 12- and 18-week ahead forecasts, respectively.

Ohio is not considered because the bivariate model forecasts better for it at h3 and h5. h1 is not considered because the bivariate model forecasts better for all states at this horizon.

Separate from one-quarter ahead forecasts, Colino and Irwin (2010) also provided results for two- and three-quarter ahead forecasts. One-quarter ahead forecasts are selected to illustrate the average RMSE reduction because they represent a horizon that is closest to those considered in the current study.

Abhyankar A (1996) Does the stock Index futures market tend to lead the cash? New evidence from the FT-SE 100 stock Index futures market. Working Paper No 96-01, Department of Accounting and Finance, University of Stirling

Ahti V (2009) Testing the rational expectations competitive storage hypothesis. Discussion Paper No 267, Helsinki Center of Economic Research

Ahumada H, Cornejo M (2016) Out-of-sample testing price discovery in commodity markets: the case of soybeans. Agric Econ. doi:10.1111/agec.12267. (forthcoming) CrossRef

Ajayi RA, Serletis A (2009) Testing for causality in the transmission of Eurodollar and US interest rates. Appl Financ Econ 19:439–443. doi:10.1080/09603100801964420 CrossRef

Amato JD, Swanson NR (2001) The real-time predictive content of money for output. J Monet Econ 48:3–24. doi:10.1016/S0304-3932(01)00070-8 CrossRef

Ashley R, Granger CWJ, Schmalensee R (1980) Advertising and aggregate consumption: an analysis of causality. Econometrica 48:1149–1167. doi:10.2307/1912176 CrossRef

Ashley RA, Tsang KP (2014) Credible Granger-causality inference with modest sample lengths: a cross-sample validation approach. Econometrics 2:72–91. doi:10.3390/econometrics2010072 CrossRef

Baek E G, Brock W A (1992) A general test for nonlinear Granger causality: bivariate model. Working Paper, Iowa State University and University of Wisconsin-Madison

Baillie RT, Booth GG, Tse Y, Zabotina T (2002) Price discovery and common factor models. J Financ Mark 5:309–321. doi:10.1016/S1386-4181(02)00027-7 CrossRef

Bekiros SD, Diks CGH (2008) The relationship between crude oil spot and futures prices: cointegration, linear and nonlinear causality. Energy Econ 30:2673–2685. doi:10.1016/j.eneco.2008.03.006 CrossRef

Bekiros SD, Diks CGH (2008) The nonlinear dynamic relationship of exchange rates: parametric and nonparametric causality testing. J Macroecon 30:1641–1650. doi:10.1016/j.jmacro.2008.04.001 CrossRef

Bessler DA, Yang J, Wongcharupan M (2003) Price dynamics in the international wheat market: modeling with error correction and directed acyclic graphs. J Reg Sci 43:1–33. doi:10.1111/1467-9787.00287 CrossRef

Bohl MT, Salm CA, Schuppli M (2011) Price discovery and investor structure in stock Index futures. J Futures Mark 31:282–306. doi:10.1002/fut.20469 CrossRef

Brock WA, LeBaron B (1996) A dynamic structural mode for stock return volatility and trading volume. Rev Econ Stat 78:94–110. doi:10.2307/2109850 CrossRef

Broock WA, Scheinkman JA, Dechert WD, LeBaron B (1996) A test for the independence based on the correlation dimension. Econom Rev 15:197–235. doi:10.1080/07474939608800353 CrossRef

Cabrera J, Wang T, Yang J (2009) Do futures lead price discovery in electronic foreign exchange markets? J Futures Mark 29:137–156. doi:10.1002/fut.20352 CrossRef

Carter CA, Mohapatra S (2008) How reliable are hog futures as forecasts? Am J Agric Econ 90:367–378. doi:10.1111/j.1467-8276.2007.01122.x CrossRef

Chao J, Corradi V, Swanson NR (2001) Out-of-sample tests for Granger causality. Macroecon Dyn 5:598–620

Chen Y, Gau Y (2009) Tick sizes and relative rates of price discovery in stock, futures, and options markets: evidence from the Taiwan stock exchange. J Futures Mark 29:74–93. doi:10.1002/fut.20319 CrossRef

Cheung Y, Lai KS (1993) Finite-sample sizes of Johansen’s likelihood ratio tests for cointegration. Oxf Bull Econ Stat 55:313–328. doi:10.1111/j.1468-0084.1993.mp55003003.x CrossRef

Colino EV, Irwin SH (2010) Outlook vs. futures: three decades of evidence in hog and cattle markets. Am J Agric Econ 92:1–15. doi:10.1093/ajae/aap013 CrossRef

de Jong F (2002) Measures of contributions to price discovery: a comparison. J Financ Mark 5:323–327. doi:10.1016/S1386-4181(02)00028-9 CrossRef

Dergiades T (2012) Do investors’ sentiment dynamics affect stock returns? Evidence from the US economy. Econ Lett 116:404–407. doi:10.1016/j.econlet.2012.04.018 CrossRef

Dergiades T, Martinopoulos G, Tsoulfidis L (2013) Energy consumption and economic growth: parametric and non-parametric causality testing for the case of Greece. Energy Econ 36:686–697. doi:10.1016/j.eneco.2012.11.017 CrossRef

Diamandis PF, Georgoutsos DA, Kouretas GP (2000) The monetary model in the presence of I(2) components: long-run relationships, short-run dynamics and forecasting of the Greek Drachma. J Int Money Finance 19:917–941. doi:10.1016/S0261-5606(00)00040-1 CrossRef

Dickey DA, Fuller WA (1981) Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49:1057–1072. doi:10.2307/1912517 CrossRef

Diks C, Panchenko V (2006) A new statistic and practical guidelines for nonparametric Granger causality testing. J Econ Dyn Control 30:1647–1669. doi:10.1016/j.jedc.2005.08.008 CrossRef

Engle RF, Granger CWJ (1987) Co-integration, and error correction: representation, estimation and testing. Econometrica 55:251–276. doi:10.2307/1913236 CrossRef

Engle RF, Hendry DF, Richard J-F (1983) Exogeneity. Econometrica 51:277–304. doi:10.2307/1911990 CrossRef

Francis BB, Mougoué M, Panchenko V (2010) Is there a symmetric nonlinear causal relationship between large and small firms? J Empir Finance 17:23–38. doi:10.1016/j.jempfin.2009.08.003 CrossRef

Fujihara RA, Mougoué M (1997) An examination of linear and nonlinear causal relationships between price variability and volume in petroleum futures markets. J Futures Mark 17:385–416. doi:10.1002/(SICI)1096-9934(199706)17:4<385::AID-FUT2>3.0.CO;2-D CrossRef

Garbade KD, Silber WL (1983) Price movements and price discovery in futures and cash markets. Rev Econ Stat 65:289–297. doi:10.2307/1924495 CrossRef

Gonzalo J (1994) Five alternative methods of estimating long-run equilibrium relationships. J Econom 60:203–233. doi:10.1016/0304-4076(94)90044-2 CrossRef

Gonzalo J, Granger C (1995) Estimation of common long-memory components in cointegrated systems. J Bus Econ Stat 13:27–35. doi:10.2307/1392518 CrossRef

Goodwin BK, Piggott NE (2001) Spatial market integration in the presence of threshold effects. Am J Agric Econ 82:302–317. doi:10.1111/0002-9092.00157 CrossRef

Goodwin BK, Schroeder TC (1991) Cointegration tests and spatial price linkages in regional cattle markets. Am J Agric Econ 73:452–464. doi:10.2307/1242730 CrossRef

Granger CWJ (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37:424–438. doi:10.1017/ccol052179207x.002 CrossRef

Hansen H, Johansen S (1999) Some tests for parameter constancy in cointegrated VAR-models. Econom J 2:306–333. doi:10.1111/1368-423X.00035 CrossRef

Harvey D, Leybourne S, Newbold P (1997) Testing the equality of prediction mean squared errors. Int J Forecast 13:281–291. doi:10.1016/S0169-2070(96)00719-4 CrossRef

Hasbrouck J (1995) One security, many markets: determining the contributions to price discovery. J Finance 50:1175–1199. doi:10.1111/j.1540-6261.1995.tb04054.x CrossRef

Hernandez M, Torero M (2010) Examining the dynamic relationship between spot and future prices of agricultural commodities. International Food Policy Research Institute (IFPRI) Discussion Paper 00988

Hiemstra C, Jones JD (1994) Testing for linear and non-linear granger causality in the stock price–volume relationship. J Finance 49:1639–1664. doi:10.1111/j.1540-6261.1994.tb04776.x CrossRef

Hoffman L A, Aulerich N (2013) Recent convergence performance of futures and cash prices for corn, soybeans, and wheat. Feed Outlook No. (FDS-13L-01), U.S. Department of Agriculture, Economic Research Service. http://www.ers.usda.gov/publications/fds-feed-outlook/fds13l-01.aspx#.Uxv0tfmsim4

Irwin SH, Garcia P, Good DL, Kunda EL (2011) Spreads and non-convergence in chicago board of trade corn, soybean, and wheat futures: are Index funds to blame? Appl Econ Perspect Policy 33:116–142. doi:10.1093/aepp/ppr001 CrossRef

Johansen S (1988) Statistical analysis of cointegration vectors. J Econ Dyn Control 12:231–254. doi:10.1016/0165-1889(88)90041-3 CrossRef

Johansen S (1991) Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive methods. Econometrica 59:1551–1580. doi:10.2307/2938278 CrossRef

Johansen S (1992) Determination of cointegration rank in the presence of a linear trend. Oxf Bull Econ Stat 54:383–397. doi:10.1111/j.1468-0084.1992.tb00008.x CrossRef

Kawaller IG, Koch PD, Koch TW (1988) The relationship between the S&P 500 Index and the S&P 500 Index futures prices. Fed Reserve Bank Atlanta Econ Rev 73:2–10

Koop G, Pesaran MH, Potter SM (1996) Impulse response analysis in nonlinear multivariate models. J Econom 74:119–147. doi:10.1016/0304-4076(95)01753-4 CrossRef

Kroner KF, Sultan J (1993) Time-varying distributions and dynamic hedging with foreign currency futures. J Financ Quant Anal 28:535–551. doi:10.2307/2331164 CrossRef

Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y (1992) Testing the null hypothesis of stationarity against the alternative of a unit root. J Econom 54:159–178. doi:10.1016/0304-4076(92)90104-Y CrossRef

Lee T, Tse Y (1996) Cointegration tests with conditional heteroscedasticity. J Econom 73:401–410. doi:10.1016/S0304-4076(95)01745-3 CrossRef

Lee C, Zeng J (2011) Revisiting the relationship between spot and futures oil prices: evidence from quantile cointegrating regression. Energy Econ 33:924–935. doi:10.1016/j.eneco.2011.02.012 CrossRef

Lükepohl H, Xu F (2012) The role of the log transformation in forecasting economic variables. Empir Econ 42:619–638. doi:10.1007/s00181-010-0440-1 CrossRef

Lux T (1995) Herd behavior, bubbles and crashes. Econ J 105:881–896. doi:10.2307/2235156 CrossRef

Lyons RK (2001) The microstructure approach to exchange rates. MIT Press, Cambridge

Mason RO, Lind DA, Marchal WG (1983) Statistics: an introduction. Harcourt Brace Jovanovich Inc, New York

Mattos F, Garcia P (2004) Price discovery in thinly traded markets: cash and futures relationships in Brazilian agricultural futures markets. Paper presented at the NCR-134 conference on applied commodity price analysis, forecasting, and market risk management, 19–20 April, St. Louis, Missouri

Moosa IA (1996) An econometric model of price determination in the crude oil futures markets. In: Proceedings of the econometric society Australasian meeting vol 3, pp 373–402

National Agricultural Statistics Service (2010) Field crops usual planting and harvesting dates. http://usda.mannlib.cornell.edu/usda/current/planting/planting-10-29-2010.pdf

Nazlioglu S (2011) World oil and agricultural commodity prices: evidence from nonlinear causality. Energy Policy 39:2935–2943. doi:10.1016/j.enpol.2011.03.001 CrossRef

Oellermann CM, Brorsen BW, Farris PL (1989) Price discovery for feeder cattle. J Futures Mark 9:113–121. doi:10.1002/fut.3990090204 CrossRef

Pesaran HH, Shin Y (1998) Generalized impulse response analysis in linear multivariate models. Econ Lett 58:17–29. doi:10.1016/S0165-1765(97)00214-0 CrossRef

Peters EE (1994) Fractal market analysis: applying chaos theory to investment and economics. Wiley, New York

Phillips PCB, Perron P (1988) Testing for a unit root in time series regression. Biometrica 75:335–346. doi:10.1093/biomet/75.2.335 CrossRef

Poskitt R (2009) Price discovery in electronic foreign exchange markets: the sterling/dollar market. J Futures Mark 30:590–606. doi:10.1002/fut.20433 CrossRef

Purcell WD, Hudson MA (1985) The economic roles and implications of trade in livestock futures. In: Peck AE (ed) Futures markets: regulatory issue. American Enterprise Institute for Public Policy Research, Washington

Qiao Z, Li Y, Wong W (2008) Policy change and lead-lag relations among China’s segmented stock markets. J Multinatl Financ Manag 18:276–289. doi:10.1016/j.mulfin.2007.11.001 CrossRef

Rosenberg J V, Traub L G (2006) Price discovery in the foreign currency futures and spot market. Federal Reserve Bank of New York Staff Reports, No. 262

Rossi B (2013) Advances in forecasting under instability. In: Graham E, Allan T (eds) Handbook of economic forecasting. Elsevier, Amsterdam. doi:10.1016/b978-0-444-62731-5.00021-x CrossRef

Schroeder TC (1997) Fed cattle spatial transactions price relationships. J Agric Appl Econ 29:347–362CrossRef

Schroeder TC, Goodwin BK (1990) Regional fed cattle price dynamics. West J Agric Econ 15:111–122

Schroeder TC, Goodwin BK (1991) Price discovery and cointegration for live hogs. J Futures Mark 11:685–696. doi:10.1002/fut.3990110604 CrossRef

Schwarz TV, Szakmary AC (1994) Price discovery in petroleum markets: arbitrage, cointegration, and the time interval of analysis. J Futures Mark 14:147–167. doi:10.1002/fut.3990140204 CrossRef

Shu J, Zhang JE (2012) Causality in the VIX futures market. J Futures Mark 32:24–46. doi:10.1002/fut.20506 CrossRef

Silvapulle P, Moosa IA (1999) The relationship between spot and futures prices: evidence from the crude oil market. J Futures Mark 19:175–193. doi:10.1002/(SICI)1096-9934(199904)19:2<175::AID-FUT3>3.0.CO;2-H CrossRef

Silvapulle PS, Podivinsky JM (2000) The effect of non-normal disturbances and conditional heteroskedasticity on multiple cointegration tests. J Stat Comput Simul 65:173–189. doi:10.1080/00949650008811997 CrossRef

Stock JH, Watson JW (1988) Testing for common trends. J Am Stat Assoc 83:1097–1107. doi:10.2307/2290142 CrossRef

Tang YN, Mak SC, Choi DFS (1992) The causal relationship between stock Index futures and cash Index prices in Hong Kong. Appl Financ Econ 2:187–190. doi:10.1080/758527099 CrossRef

Tao L, Song FM (2010) Do small traders contribute to price discovery? Evidence from the Hong Kong Hang Seng Index markets. J Futures Mark 30:156–174. doi:10.1002/fut.20410 CrossRef

Theissen E (2002) Price discovery in floor and screen trading systems. J Empir Finance 9:455–474. doi:10.1016/S0927-5398(02)00005-1 CrossRef

Wahab M, Lashgari M (1993) Price dynamics and error correction in stock Index and stock Index futures markets: a cointegration approach. J Futures Mark 13:711–742. doi:10.1002/fut.3990130702 CrossRef

Wang Z, Yang J, Li Q (2007) Interest rate linkages in the Eurocurrency market: contemporaneous and out-of-sample Granger causality tests. J Int Money Finance 26:86–103. doi:10.1016/j.jimonfin.2006.10.005 CrossRef

Weber JC, Lamb DR (1970) Statistics and research in physical education. CV Mosby Co., St. Louis

Xu X (2015) Cointegration among regional corn cash prices. Econ Bull 35:2581–2594

Xu X (2017) Linear and nonlinear causality between corn cash and futures prices. J Agric Food Ind Organ. doi:10.1515/jao-2016-0006

Xu X, Thurman W (2015) Using local information to improve short-run corn cash price forecasts. In: Proceedings of the NCCC-134 conference on applied commodity price analysis, forecasting, and market risk management. St. Louis, MO

Yang J (2003) Market segmentation and information asymmetry in Chinese stock markets: a var analysis. Financ Rev 38:591–609. doi:10.1111/1540-6288.00062 CrossRef

Yang J, Bessler DA, Leatham DJ (2001) Asset storability and price discovery in commodity futures markets: a new look. J Futures Mark 21:279–300. doi:10.1002/1096-9934(200103)21:3<279::AID-FUT5>3.0.CO;2-L CrossRef

Yang J, Yang Z, Zhou Y (2012) Intraday price discovery and volatility transmission in stock Index and stock Index futures markets: evidence from China. J Futures Mark 32:99–121. doi:10.1002/fut.20514 CrossRef

Zapata HO, Rambaldi AN (1997) Monte Carlo evidence on cointegration and causation. Oxf Bull Econ Stat 59:285–298. doi:10.1111/1468-0084.00065 CrossRef

Zhong M, Darrat AF, Otero R (2004) Price discovery and volatility spillovers in Index futures markets: some evidence from Mexico. J Bank Finance 28:3037–3054. doi:10.1016/j.jbankfin.2004.05.001 CrossRef

Titel: Cointegration and price discovery in US corn cash and futures markets
verfasst von: Xiaojie Xu
Publikationsdatum: 31.08.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 4/2018
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-017-1322-6

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 4/2018

Speculative price bubbles in urban housing markets

The real effect of currency misalignment on productivity growth: evidence from middle-income economies

An attempt to restore Wagner’s law of increasing state activity

Revisiting income and price elasticity of gasoline demand in India: new evidence from cointegration tests

Detecting which firm-specific characteristics impact market-oriented R&D

Analysis of electricity prices for Central American countries using dynamic conditional score models

Premium Partner