Chaos and Finance

Content

Objective

The course aims to develop students’ conceptual understanding of time series, chaos, risk and uncertainty, behavioural economics, and financial markets, and to examine the relationships among these concepts through modelling methods.

It focuses on modelling and testing stochastic processes in economic and financial markets and relating these processes to economic agents’ decision-making within the framework of behavioural economics.

Linear stochastic processes are analysed using Stata 10.0, while chaotic processes are examined using R. The course also covers estimation and forecasting methods.

The course schedule is available at the following link: Course Schedule.

Course Content

Analysis and critical assessment of the Efficient Market Hypothesis.

Risk, uncertainty, and time.

Information, Bayesian decision-making, and rationality.

Applications of behavioural economics.

The concept of equilibrium.

Time series analysis:

Linear modelling of stochastic processes, with applications using Stata 10.0.

Nonlinear modelling, with applications using R.

Chaotic processes.

Application of time series methods to financial variables in markets with high and low trading volumes, including equity, foreign exchange, and bond markets.

Course Learning Outcomes

Upon successful completion of the course, students will be able to:

Explain the relationship between behavioural economics and finance.

Analyse the effects of decision-making processes on markets.

Model stochastic processes.

Use Stata 10.0 and EViews to model stochastic processes.

Explain phase spaces and nonlinear processes.

Explain the concept of chaos and test for chaotic dynamics in financial processes.

Use R to model and test nonlinear processes.

Teaching and Learning Methods

Theory and Practice

The course will draw on contemporary examples of central bank practices worldwide, monetary and fiscal policies, and current developments in financial markets.

Developments in global financial markets, particularly equity markets, will also be used to illustrate the application of time series analysis methods.

References

In addition to the studies of 4 scientists who received the award in the fields of Nobel Economics and Physics, the following sources will be followed.


Johansen, S. (1991) Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive models. Econometrica, 59, p 1151-1181

Johansen, S ., Juselius, K. (1991) Testing structural hypotheses in a multivariate cointegration analysis of the PPP and the UIP for UK. Journal of Econometrics, 53, p 211-244

Kahneman, D., Slovic, P., Tversky, A. (1982). Judgment Under Uncertainty: Heuristics and Biases . New York: Cambridge University Press.

Kantz, H. (1994) A robust method to estimate the maximal Lyapunov exponent of a time series, Physics Letters A 185, 77-87.

Kantz, H., Schreiber T.(1997) Nonlinear time series analysis. Cambridge, UK: Cambridge University Press.

Karanfil, F., Ozkaya, A. (2007). Estimation of real GDP and unrecorded economy in Turkey based on environmental data. Energy Policy 35 (10), 4902-4908.


Grassberger, P., Procaccia, I. (1983a). Characterization of strange attractors. Phy. Rev. Let., 50: 346-349

Grassberger, P., Procaccia, I. (1983b.) Estimation of the Kolmogorov entropy from a chaotic signal. Phys Rev [A] 29:2591-3.

Rosenstein, M.T., Collins, J.J., De Luca, C.J. (1993) A practical method for calculating largest Lyapunov exponents from small data sets, Physica D 65, 117-34.

Sachs, J.D. (1984) Theoretical issues in international borrowing. Princeton Studies in International Finance, vol. 54. Department of Economics, Princeton University, Princeton.

Said, S.E., Dickey, D.A. (1984) Testing for unit roots in autoregressive moving average models of unknown order. Biometrica, 71, p 599-608

Takens, F. (1981) Detecting strange attractors in turbulence. In: Dynamical systems and turbulence. Berlin: Springer; p. 366.

Wolf, A., Swift, J.B., Swinney, H.L., Vastano, J.A. (1985) Determining Lyapunov Exponents from a time series, Physica D 16, 285-317.

World Bank (2000, September). Turkey—Country economic memorandum— Structural reforms for sustainable Growth (Vols. I and II) (Report No.20657TU), Washington, DC.

Johansen, S. (1991) Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive models. Econometrica, 59, p 1151-1181

Johansen, S ., Juselius, K. (1991) Testing structural hypotheses in a multivariate cointegration analysis of the PPP and the UIP for UK. Journal of Econometrics, 53, p 211-244

Weekly Contents

Assessment System

ECTS