Karapanagiotidis, Paul (2013): Empirical evidence for nonlinearity and irreversibility of commodity futures prices.
Preview |
PDF
MPRA_paper_56801.pdf Download (10MB) | Preview |
Abstract
Theory suggests that commodity futures price levels and returns data may exhibit both nonlinear and nonreversible features. This paper attempts to provide a thorough empiri- cally investigation of these claims. The data set is composed of 25 individual continuous contract commodity futures series which fall within a number of industry sectors including softs, precious metals, energy, and livestock. Employing both time-domain and frequency- domain tests examining the higher order cumulant properties of these series, it is shown that they exhibit both nonlinearities and irreversibility differing across industry sector. Furthermore, in modeling these series I estimate a number of parametric models able to capture irreversibility such as the linear mixed causal/noncausal autoregressive model and various purely causal nonlinear models, since there is a close connection between these two classes of models. It is shown that the linear causal ARMA model is unable to adequately account for the features of the data and while the mixed causal/noncausal model improves model fit significantly by capturing latent irreversibility, the vast majority of the nonlinearity these series exhibit is of the “nonlinear in variance” type. Finally, out of sample forecasts and an evaluation of the estimated unconditional distribution of the mixed causal/noncausal models suggest that there may still exist model misspecification.
Item Type: | MPRA Paper |
---|---|
Original Title: | Empirical evidence for nonlinearity and irreversibility of commodity futures prices |
Language: | English |
Keywords: | mixed causal/noncausal autoregressions, nonlinear models, commodity futures, speculative price bubbles. |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C50 - General C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics |
Item ID: | 56801 |
Depositing User: | Paul Karapanagiotidis |
Date Deposited: | 24 Jun 2014 22:51 |
Last Modified: | 30 Sep 2019 09:47 |
References: | Andrews B., David, R.A., Breidt, F.J. (2006) “Maximum likelihood estimation for all-pass time series models,” Journal of Multivariate Analysis, 97, 1638-1659. Barnett, W.A., Gallant, R.A., Hinich, M.J., Jungeilges, J.A., Kaplan, D.T, Jensen, M.J. (1996) “An experimental design to compare tests of nonlinearity and chaos,” in Nonlinear Dynamics and Economics: Proceedings of the Tenth International Symposium in Economic Theory and Econometrics, eds. W.A. Barnett, A.P. Kirman, M. Salmon, 163-190, Cambridge University Press. Black, F. (1976) “The pricing of commodity contracts,” The Journal of Financial Economics, 3, 167-179. Blanchard, O., Watson, M., (1982) “Bubbles, rational expectations and financial markets,” Working paper No. 945. National Bureau of Economic Research. Blank, S.C., (1991) “Chaos in futures markets? A nonlinear dynamical analysis,” The Journal of Futures Markets, 11, 6, 711-728. Bollerslev, T. (1986) “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, 31, 3, 307327. Breidt, J., Davis, R., Lii, K., Rosenblatt, M. (1991) “Maximum likelihood estimation for non- causal autoregressive processes,” Journal of Multivariate Analysis, 36, 175-198. Breidt, J., Davis, R., Trindade, A. (2001) “Least absolute deviation estimation for all-pass time series models,” The Annals of Statistics, 29, 4, 919-946. Brillinger, D.R., Rosenblatt, M. (1967) “Asymptotic theory of estimates of k-th order spectra,” in Spectral Analysis of Time Series, ed. B. Harris, 153-188, Wiley, New York. Brock, W., Dechert, W.D., Scheinkman, J. (1987) “A test for independence based on the corre- lation dimension,” working paper, Department of Economics, University of Wisconsin, Madi- son. Burns, A.F., Mitchell, W.C. (1946) Measuring Business Cycles, Columbia University Press, New York. Campbell, J.Y., Lo, A.W., MacKinlay, A.C. (1996) The Econometrics of Financial Markets, Princeton University Press, NJ. Deaton, A., Laroque, G. (1996) “Competitive storage and commodity price dynamics,” Journal of political economy, 104, 5, 896-923. DeCoster, G.P., Labys, W.C., Mitchell, D.W., (1992) “Evidence of chaos in commodity futures prices,” The Journal of Futures Markets, 12, 3, 291-305. Dempster, A.P., Laird, N.M., Rubin, D.B. (1977) ”Maximum likelihood from incomplete data via the EM algorithm,” Journal of the Royal Statistical Society. Series B (Methodological), 39, 1, 1-38. Dusak, K. (1973) “Futures trading and investor returns: An investigation of commodity market risk premiums,” Journal of Political Economy, 81, 1387-1406. Eichenwald, K. (1989-12-21). “2 Hunts Fined And Banned From Trades,” New York Times on- line, http://www.nytimes.com/1989/12/21/business/2-hunts-fined-and-banned-from-trades.html, Retrieved 2013-05-26. Engle, R.F. (1982) “Autoregressive Conditional Heteroscedasticity with Estimates of Variance of United Kingdom Inflation”, Econometrica, 50, 987-1008. Evans, G. (1991) “Pitfalls in testing for explosive bubbles in asset prices,” The American Eco- nomic Review, 81, 4, 922-930. Fama, E.F., French, K.R. (1987) “Commodity futures prices: Some evidence on forecast power, premiums, and the theory of storage,” The Journal of Business, 60, 1, 55-73. Findley, D.F. (1986) “The uniqueness of moving average representations with independent and identically distributed random variables for non-Gaussian stationary time series,” Biometrika, 73, 2, 520-521. Frank, M., Stengos, T. (1989) “Measuring the strangeness of gold and silver rates of return,” The Review of Economic Studies, 56, 4, 553-567. Gettler, L. (2008-02-02). “Wake-up calls on rogue traders keep ringing, but who’s answer- ing the phone?” The Age (Melbourne), http://www.theage.com.au/business/wakeup-calls-on- rogue-traders-keep-ringing-but-whos-answering-the-phone-20080201-1plq.html, Retrieved 2013-5-26. Gordon, N. J., Salmond, D. J., Smith, A. F. M. (1993) ”Novel approach to nonlinear/non- Gaussian Bayesian state estimation,” IEEE Proceedings F on Radar and Signal Processing, 140, 2, 107-113. Gourieroux, C., Jasiak, J. (2003) “Nonlinear innovations and impulse responses with applica- tion to VaR sensitivity,” Working paper, CREF 03-08. Gourieroux, C., Zakoian, J.M. (2012) “Explosive bubble modelling by noncausal Cauchy au- toregressive process,” Working paper, CREST. Granger, C.W., Andersen, A.P. (1978) An Introduction to Bilinear Time Series Models, Van- denhoeck and Ruprecht, Gottingen. Grassberger, P., Procaccia, I. (1983) “Measuring the strangeness of strange attractors,” Physica, 9D, 189-208. Hallin, M., Lefevre, C., Puri, M. (1988) “On time-reversibility and the uniqueness of moving average representations for non-Gaussian stationary time series,” Biometrika, 71, 1, 170-171. Hansen, L.P, Sargent, T.J. (1991) “Two difficulties in interpreting vector autogressions,” in Ra- tional Expectations Econometrics, eds. Hansen, L.P., and Sargent, T.J., Westview Press Inc., Boulder, CO, 77-119. Hinich, M.J. (1982) “Testing for Gaussianity and linearity of a stationary time series,” Journal of Time Series Analysis, 3, 3. Hinich, M.J. (1996) “Testing for dependence in the input to a linear time series model,” Journal of Nonparametric Statistics, 6, 2-3. Hinich, M.J. (2009) “Falsifying ARCH/GARCH models using bispectral based tests,” Commu- nications in Statistics: Theory and Methods, 38, 4, 529-541. Hinich, M.J., Rothman, P. (1998) “Frequency-domain test of time reversibility,” Macroeco- nomic Dynamics, 2, 72-88. Hsieh, D.A., (1989) “Testing for nonlinear dependence in daily foreign exchange rates,” The Journal of Business, 62, 3, 339-368. Jones, M.C. (2010) “A skew-t distribution,” in Probability and Statistical Models with Appli- cations, eds. Charalambides, A., Koutras, M.V., and Balakrishnan, N., Chapman & Hall/CRC Press. Lanne, M., Saikkonen, P. (2008) “Modeling expectations with noncausal autoregressions,” HECER Discussion paper 212. Lanne, M., Luoto, J., Saikkonen, P. (2010) “Optimal forecasting of noncausal autoregressive time series,” MPRA Paper 23648, University Library of Munich, Germany. Lanne, M., Nyberg, H., and Saarinen, E. (2011) “Forecasting U.S. macroeconomic and finan- cial time series with noncausal and causal AR models: a comparison,” HECER Discussion paper 319. Ling, S., Li, D. (2008) “Asymptotic inference for a nonstationary double AR(1) model,” Biometrika, 95, 257-263. Lof, M. (2011) “Noncausality and asset pricing,” HECER Discussion paper 323. Marcellino, M., Stock, J.H., Watson, M.W. (2006) “A comparison of direct and iterated AR methods for forecasting macroeconomic time series,” Journal of Econometrics, 135, 499-526. McLeod, A.I., Li, W.K. (1983) “Diagnostic checking ARMA time series models using squared residuals autocorrelations,” Journal of Time Series Analysis, 4, 269-273. Neftci, S.N. (1984) “Are economic time series asymmetric over the business cycle,” Journal of Political Economy, 92, 307-328. Nelson, C.R., Plosser, C.I. (1982) “Trends and random walks in macroeconomic time series: some evidence and implications,” Journal of Monetary Economics, 10, 139-162. Pemberton, J., Tong, H. (1981) “A note on the distributions of nonlinear autoregressive stochas- tic models, Journal of Time Series Analysis 2, 1, 49-52. Priestley, M.B. (1989) Non-linear and Non-stationary Time Series Analysis, Academic Press Ltd., London. Ramsey J., Rothman P. (1996) “Time irreversibility and business cycle asymmetry,” Journal of Money and Banking, 28, 1-21. Subba Rao, T., Gabr, M.M, (1984) “An introduction to bispectral analysis and bilinear time series models,” in Lecture Notes in Statistics, eds. D. Brillinger et. al., Springer-Verlag. Rosenblatt, M. (2000) Gaussian and Non-Gaussian Linear Time Series and Random Fields, Springer Verlag, New York. Sharpe, W.F. (1964) “Capital asset prices: A theory of market equilibrium under conditions of risk,” Journal of Fiance, 19, 3, 425-442. Terasvirta, T. (1994) “Specification, estimation, and evaluation of smooth transition autoregres- sive models,” Journal of the American Statistical Association, 89, 425, 208-218. Tong, H., Lim, K.S. (1980) “Threshold autoregression, limit cycles, and cyclical data,” Journal of the Royal Statistical Society, Series B, 42, 3, 245-292. Weiss, G. (1975) “Time reversibility of linear stochastic processes,” Journal of Applied Proba- bility, 12, 831-836. Working, H. (1949) “The theory of the price of storage,” American Economic Review, 39, 1254- 1262. Yang, S.R., Brorsen, W. (1993) “Nonlinear dynamics of daily futures prices: conditional het- eroskedasticity or chaos?,” The Journal of Futures Markets, 13, 2, 175-191. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/56801 |