NONLINEAR STOCHASTIC MODELLING DYNAMIC OF THE AGRICULTURAL PRODUCTS EXCHANGE RATES
The aim of this paper is to research some of the most important fnancial-stochastic models which enable the description of the dynamics of agricultural exchange rates. This dynamics is usually characterized by the properties of nonlinearity, hence the so-called conditional heteroskedastic models are used as the basic models for precise description of its behavior. The basic stochastic properties of these models, as well as the procedures to estimate their parameters, are also studied here. Finally, the conditional heteroskedastic models are applied in ftting of the empirical data: the nominal average cereals exchange rate indexes between the U.S. and the other countries.
2. Balakrishnan, N., Brito, M. R., Quiroz, A. J. (2013): On the goodness-of-ft procedure for normality based on the empirical characteristic function for ranked set sampling data, Metrika, Vol. 76, pp. 161–177.
3. Bollerslev, T. (1986): Generalized Autoregressive Conditional Heteroskedasticity, Journal of Financial Economics, Vol. 31, pp. 307-327.
4. Chavas, J.-P., Cox, T. L. (1997): Production Analysis: A Non-Parametric Time Series Application to U.S. Agriculture, Journal of Agricultural Economics, Vol.48, pp. 330-348.
5. Engle, R. F. (1982): Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inﬂation, Econometrica, Vol. 50, No. 4, pp. 987-1007.
6. Durhan, G.B. (2007): SV mixture models with application to S&P 500 index. Journal of Financial Economics, Vol. 85, No. 3, pp. 822–856.
7. Fornar, Mele, A. (1997): Sign- And Volatility Switching- ARCH Models: Theory and Applications to International Stock Markets, Journal of Applied Econometrics, Vol. 12, pp. 49-65.
8. Francq, C., Roussignol, M., Zakoian, J. M. (2001): Conditional Heteroskedasticity Driven by Hidden Markov Chains, Journal of Time Series Analysis, Vol. 22, No. 2, pp. 197-220.
9. Franses, P. H., Dijk, V. D. (2000): Nonlinear Time Series Models in Empirical Finance, Cambridge University Press.
10. Huang, B.-N., Fok, R.C.W. (2001): Stock Market Integration-An Application of the Stochastic Permanent Breaks Model, Applied Economics Letters, Vol. 8, No. 11, pp. 725–729.
11. Hill, M. J., Donald, G. E. (2003): Estimating Spatio-Temporal Patterns of Agricultural Productivity in Fragmented Landscapes Using AVHRR NDVI Time Series, Remote Sensing in Environment, Vol.84, No. 3, pp. 367-384.
12. Kapetanios, G., Tzavalis, E. (2010): Modeling structural breaks in economic relationships using large shocks, Journal of Economic Dynamics and Control, Vol. 34, No. 3, pp. 417–436.
13. Mikosch, T. (2001): Modeling Dependence and Tails of Financial Time Series, Laboratory of Actuarial Mathematics, University of Copenhagen.
14. Pažun, B., Langović, Z., Langović-Milićević, A. (2016): Econometric Analysis of Exchange Rate in Serbia and its Inﬂuence on Agricultural Sector, Economics of Agriculture, Vol. 43, No. 1, pp. 47-60.
15. Popović, B., Stojanović, V. (2003): Stacionarnost volatilnosti cene u ARCH modelima (in Serbian), Proceedings of the Conference SYM-OP-IS, September 2003, Herceg-Novi, pp. 575-578.
16. Popović, B., Stojanović, V. (2005): Split-ARCH, Pliska Studia Mathematica Bulgarica, Vol. 17, pp. 201-220.
17. Sangjoon, K., Shephard, N., Siddhartha, C. (1998): Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models, Review of Economic Studies, Vol. 65, pp. 361-393.
18. Singleton, K. J. (2001): Estimation of affne asset pricing models using the empirical characteristic function, Journal of Econometrics, Vol. 102, No. 1, pp. 111–141.
19. Stojanović, V., Popović, B. (2004): Iterativni metodi ocene parametara u modelima uslovne heterogenosti (in Serbian), Proceedings of the Conference SYM-OP-IS, September 2004, Fruška Gora, pp. 513-516.
20. Zakoian, J. M. (1994): Threshold Heteroskedastic Models, Journal of Economic Dynamics and Control, Vol. 18, pp. 931-955.