Advertisement

Empirical Economics

, Volume 33, Issue 2, pp 231–244 | Cite as

Commodity price cycles and heterogeneous speculators: a STAR–GARCH model

  • Stefan Reitz
  • Frank Westerhoff
Original Paper

Abstract

We propose an empirical commodity market model with heterogeneous speculators. While the power of trend-extrapolating chartists is constant over time, the symmetric impact of stabilizing fundamentalists adjusts endogenously according to market circumstances: Using monthly data for various commodities such as cotton, sugar or zinc, our STAR–GARCH model indicates that their influence positively depends on the distance between the commodity price and its long-run equilibrium value. Fundamentalists seem to become more and more convinced that mean reversion will set in as the mispricing enlarges. Commodity price cycles may thus emerge due to the nonlinear interplay between different trader types.

Keywords

Commodity markets Chartists and fundamentalists Nonlinearities STAR-GARCH model 

JEL classification

C51 D84 Q11 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bollerslev T, Wooldridge J (1992) Quasi maximum likelihood estimation and inference in dynamic models with time varying covariances. Econometric Rev 11:143–172CrossRefGoogle Scholar
  2. Borenzstein E, Khan M, Reinhart C, Wickham P (1994) The behavior of non-oil commodity prices. IMF Occasional Paper 112, IMF, WashingtonGoogle Scholar
  3. Brock W, Hommes C (1997) A rational route to randomness. Econometrica 65:1059–1095CrossRefGoogle Scholar
  4. Brock W, Hommes C (1998) Heterogeneous beliefs and routes to chaos in a simple asset pricing model. J Econ Dyn Control 22:1235–1274CrossRefGoogle Scholar
  5. Canoles B, Thompson S, Irwin S, France V (1998) An analysis of the profiles and motivations of habitual commodity speculators. J Futures Mark 18:765–801CrossRefGoogle Scholar
  6. Cashin P, McDermott J, Scott A (2002) Booms and slumps in world commodity prices. J Dev Econ 69:277–296CrossRefGoogle Scholar
  7. Chiarella C, Iori G (2002) A simulation analysis of the of the microstructure of double auction markets. Quant Finance 2:1–8CrossRefGoogle Scholar
  8. Chiarella C, Dieci R, Gardini, L (2002) Speculative behavior and complex asset price dynamics. J Econ Behav Orgn 49:173–197CrossRefGoogle Scholar
  9. Day R (1994) Complex economic dynamics: An introduction to dynamical systems and market mechanisms. MIT Press, CambridgeGoogle Scholar
  10. Day R, Huang W (1990) Bulls, bears and market sheep. J Econ Behav Org 14:299–329CrossRefGoogle Scholar
  11. Deaton A (1999) Commodity prices and growth in Africa. J Econ Perspect 13:23–40CrossRefGoogle Scholar
  12. Deaton A, Laroque G (1992) On the behavior of commodity prices. Rev Econ Stud 59:1–24CrossRefGoogle Scholar
  13. De Grauwe P, Dewachter H, Embrechts M (1993) Exchange rate theory: chaotic models of foreign exchange markets. Blackwell, OxfordGoogle Scholar
  14. Draper D (1985) The small public trader in futures markets. In: Peck A (ed) Futures markets: regulatory issues. American Enterprise Institute for Public Policy Research, Washington 211–269Google Scholar
  15. Eitrheim O, Teräsvirta T (1996) Testing the adequacy of smooth transition autoregressive models. J Econ 74:59–75Google Scholar
  16. Farmer D, Joshi S (2002) The price dynamics of common trading strategies. J Econ Behav Org 49:149–171CrossRefGoogle Scholar
  17. Granger C, Teräsvirta T (1993) Modelling nonlinear economic relationships. Oxford University Press, OxfordGoogle Scholar
  18. Hommes C (1998) On the consistency of backward-looking expectations: The case of the cobweb. J Econ Behav Org 33:333–362CrossRefGoogle Scholar
  19. Hommes C (2001) Financial markets as nonlinear adaptive evolutionary systems. Quant Finance 1:149–167Google Scholar
  20. Kahneman D, Slovic P, Tversky A (1986) Judgment under uncertainty: heuristics and biases. Cambridge University Press, CambridgeGoogle Scholar
  21. Kilian L, Taylor M (2003) Why is it so difficult to beat the random walk forecast of exchange rates? J Int Econ 60:85–107CrossRefGoogle Scholar
  22. Kirman A (1991) Epidemics of opinion and speculative bubbles in financial markets. In: Taylor M (ed) Money and financial markets. Blackwell, Oxford:354–368Google Scholar
  23. LeBaron B, Arthur B, Palmer R (1999) Time series properties of an artificial stock market. J Econ Dyn Control 23:1487–1516CrossRefGoogle Scholar
  24. Ljung G, Box G (1978) On a Measure of Lack of Fit in Time Series Models. Biometrika 67:297–303CrossRefGoogle Scholar
  25. Lundbergh S, Teräsvirta T (1998) Modeling economic high frequency time series with STAR–STGARCH models. Stockholm School of Economics WP No 291Google Scholar
  26. Lux T (2004) Financial power laws: empirical evidence, models, and mechanisms. University of Kiel, mimeoGoogle Scholar
  27. Lux T, Marchesi M (2000) Volatility clustering in financial markets: A micro-simulation of interacting agents. International J Theor Appl Finance 3:675–702CrossRefGoogle Scholar
  28. Menkhoff L (1997) Examining the use of technical currency analysis. Int J Finance Econ 2:307–318CrossRefGoogle Scholar
  29. Murphy J (1999) Technical analysis of financial markets. New York Institute of Finance, New YorkGoogle Scholar
  30. Nam K, Pyun C, Avard S (2001) Asymmetric reverting behavior of short-horizon stock returns: an evidence of stock market overreaction. J Bank Finance 25:807–824CrossRefGoogle Scholar
  31. Nam K, Pyun C, Arize A (2002) Asymmetric mean-reversion and contrarian profits: ANST-GARCH approach. J Empirical Finance 9:563–588CrossRefGoogle Scholar
  32. Rosser J, Ahmed E, Hartmann G (2003) Volatility via social flaring. J Econ Behav Org 50:77–87CrossRefGoogle Scholar
  33. Sanders D, Irwin S, Leuthold R (2000) Noise trader sentiment in futures markets. In: Goss B. (eds). Models of futures markets. Routledge, pp 86–116Google Scholar
  34. Sarantis N (1999) Modeling non-linearities in real effective exchange rates. J Int Money Finance 18:27–45CrossRefGoogle Scholar
  35. Smidt S (1965) Amateur speculators: A survey of trading strategies, information sources and patterns of entry and exit from commodity futures markets by non-professional speculators. Cornell Studies in Policy and Administration, Cornell UniversityGoogle Scholar
  36. Smith V (1991) Papers in experimental economics. Cambridge University Press, CambridgeGoogle Scholar
  37. Sonnemans J, Hommes C, Tuinstra J, van de Velden H (2004) The instability of a heterogeneous cobweb economy: a strategy experiment on expectation formation. J Econ Behav Org 54:453–481CrossRefGoogle Scholar
  38. Taylor M, Allen H (1992) The use of technical analysis in the foreign exchange market. J Int Money Finance 11:304–314CrossRefGoogle Scholar
  39. Teräsvirta T (1994) Specification, estimation, and evaluation of smooth transition autoregressive models. J Am Stat Assoc 89:208–218CrossRefGoogle Scholar
  40. Teräsvirta T, Anderson H (1992) Characterizing nonlinearities in business cycles using smooth transition autoregressive models. J Appl Econom 7:119–139CrossRefGoogle Scholar
  41. Weiner R (2002) Sheep in wolves’ clothing? Speculators and price volatility in petroleum futures. Q Rev Econ Finance 42:391–400CrossRefGoogle Scholar

Copyright information

© Springer Verlag 2006

Authors and Affiliations

  1. 1.Department of EconomicsDeutsche BundesbankFrankfurtGermany
  2. 2.Department of EconomicsUniversity of OsnabrückOsnabrückGermany

Personalised recommendations