Finance Discipline Group
UTS Business School
University of Technology, Sydney

Working Paper Series

Title:
Learning Dynamics in a Nonlinear Stochastic Model of Exchange Rates
Author(s): Carl Chiarella & Alexander Khomin
Date of publication: May 1996
Working paper number: 64
Abstract:
This paper considers a version of the Dornbusch model of exchange rate dynamics which allows a nonlinear domestic demand for foreign assets function and imperfect substitutability between domestic and foreign interest bearing assets. Expectations of exchange rate changes are modelled as adaptive with perfect foresight being obtained as a limiting case. For sufficiently rapid speed of adjustment of expectations the model is able to generate cyclical behaviour of the exchange rate and expectations of its change. In the perfect foresight limit the cycles become relaxation cycles. To this underlying model of the fundamentals a white noise "news" process is added. Agents are assumed to attempt to learn about the system dynamics and the link between such learning and exchange rate volatility is studied. Two learning scenarios are considered. In the first scenario economic agents are regarded as a uniformly well-informed group of sophisticated traders. In the second scenario a group of "naive" traders coexist with the sophisticated traders. We find that both learning scenarios lead to increased volatility. However this effect increases in proportion to the weight of the "naive" traders.
Paper: Download (Format: PDF, Size: 553 Kb)
Known citations:

Ezepue, P. O. and Solarin, A. R. T., 2009, "The Meta-Heuristics of Global Financial Risk Management in the Eyes of the Credit Squeeze: Any Lessons for Modelling Emerging Financial Markets?", The Proceedings of NMC-COMSATS Conference on Mathematics Modeling of Global Challenging Problems 2008, Nigeria.

Long, K. S., 2011, "Integrated Optimal Control and Parameter Estimation Algorithms for Discrete-Time Nonlinear Stochastic Dynamical Systems", PhD Thesis, Faculty of Science, Universiti Teknologi Malaysia.