A New Look at Copper Markets: A Regime-Switching Jump Model
AbstractGARCH-jump models of metal price returns, while allowing for sudden movements (jumps), apply the same specification of the jump component in both 'bear'and 'bull' markets. As a result, the more frequent but relatively small jumps that occur in both bear and bull markets dominate the characterization of the jump process. Given that large jumps, although less frequent, are still quite common in copper (and other metal) markets, this is a potential shortcoming of current models. More flexibility can be added to the modeling process by allowing for regime-switching. In this paper we specify a model that allows for switching across two separate regimes, with the possibility of different jump sizes and frequencies under each regime, along with a regime-specific GARCH process for the conditional variance. This model is applied to daily copper futures prices over the period of January 2 1980 through the end of July 2007. The model is estimated both with and without factors such as interest and exchange rate movements entering into the specification of the state-dependent mean of the conditional jump size. In some respects, a Regime Switching GARCH-Jump Model performs well when applied to the copper returns data. The results are mixed in terms of whether or not variations of the model that allow jump sizes to be a function of interest or exchange rates offer much of an advantage over a pure time series approach to the modeling of copper returns over the past three decades.
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Bibliographic InfoPaper provided by University of Alberta, Department of Economics in its series Working Papers with number 2009-13.
Length: 29 pages
Date of creation: 17 Mar 2009
Date of revision:
regime switching; Poisson jump; GARCH volatility; copper futures;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
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- Chew Lian Chua & Sandy Suardi, 2007. "Markov-Switching Mean Reversion in Short-Term Interest Rates: Evidence from East Asian Economies," The Economic Record, The Economic Society of Australia, The Economic Society of Australia, vol. 83(263), pages 383-397, December.
- John M. Maheu & Thomas H. McCurdy, 2001.
"Nonlinear Features of Realized FX Volatility,"
CIRANO Working Papers, CIRANO
- Chan, Wing H & Maheu, John M, 2002. "Conditional Jump Dynamics in Stock Market Returns," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 20(3), pages 377-89, July.
- Heal, Geoffrey & Barrow, Michael, 1980. "The Relationship between Interest Rates and Metal Price Movements," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 47(1), pages 161-81, January.
- Hamilton, James D. & Susmel, Raul, 1994.
"Autoregressive conditional heteroskedasticity and changes in regime,"
Journal of Econometrics, Elsevier,
Elsevier, vol. 64(1-2), pages 307-333.
- Tom Doan, . "RATS programs to estimate Hamilton-Susmel Markov Switching ARCH model," Statistical Software Components RTZ00083, Boston College Department of Economics.
- Bollerslev, Tim, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics, Elsevier,
Elsevier, vol. 31(3), pages 307-327, April.
- Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Slade, Margaret E., 1982. "Trends in natural-resource commodity prices: An analysis of the time domain," Journal of Environmental Economics and Management, Elsevier, vol. 9(2), pages 122-137, June.
- Maheu, John M & McCurdy, Thomas H, 2000. "Identifying Bull and Bear Markets in Stock Returns," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 18(1), pages 100-112, January.
- Chang, Kuang-Liang & Yu, Shih-Ti, 2013. "Does crude oil price play an important role in explaining stock return behavior?," Energy Economics, Elsevier, Elsevier, vol. 39(C), pages 159-168.
- Roque Montero & Javier García-Cicco, 2012. "Modelo y Pronóstico del Precio del Cobre: Un Enfoque de Cambio de Regímenes," Notas de Investigación Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, Central Bank of Chile, vol. 15(2), pages 099-116, August.
- Javier García - Cicco & Roque Montero, 2011. "Modeling Copper Price: A Regime-Switching Approach," Working Papers Central Bank of Chile, Central Bank of Chile 613, Central Bank of Chile.
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