Aydin A. Cecen

Distinguishing between stochastic and deterministic behavior in high frequency foreign exchange rate returns: Can non-linear dynamics help forecasting?

Abstract This paper investigates the dynamic properties of high frequency foreign exchange rate returns. Using hourly data for four exchanges rates, the British Pound, the Deutschemark, the Japanese Yen and Swiss Franc, we attempt to differentiate between stochastic and deterministic behavior in hourly rates of returns. While the autocorrelation coefficients and the Brock-Dechert-Scheinkman test point to the presence of some non-linear dependence, correlation dimension estimates reveal little evidence in favor of low-dimensional chaos. The analysis appears to support the view that although it is not possible to exploit deterministic non-linear dependence in exchange rate time series in order to improve short-term forecasting, non-linear stochastic models can be used for conditional volatility forecasts.

Normal heartbeat series are nonchaotic, nonlinear, and multifractal: New evidence from semiparametric and parametric tests

We present new evidence that normal heartbeat series are nonchaotic, nonlinear, and multifractal. In addition to considering the largest Lyapunov exponent and the correlation dimension, the results of the parametric and semiparametric estimation of the long memory parameter (long-range dependence) unambiguously reveal that the underlying process is nonstationary, multifractal, and has strong nonlinearity.

Distinguishing between stochastic and deterministic behavior in foreign exchange rate returns: Further evidence

Abstract The nature of non-linear dependence in hourly exchange rate returns is scrutinized. While the Brock, Dechert and Scheinkman (BDS) test results reject i.i.d. behavior, various correlation dimension estimates reveal little evidence in favor of a low-dimensional attractor in the time series.

Economic Growth and Structural Change in Turkey 1960–88

After almost five decades of industrialization—characterized, on the one hand, by considerable state intervention and, on the other, by protectionist import-substituting policies in domestic capital formation—in the early 1980s Turkey ostensibly entered a new era of export-led economic growth. Since 1960, the Turkish democracy has experienced a series of crises with astonishingly regular ten-year cycles of recurrence. The 1980 military intervention, however, brought about a radical attempt to restructure the economy, hardly comparable with the rather gradual changes in its recent economic history. Its ten years of experimentation with economic liberalization and structural adjustment provide us today with an adequate record to identify and discuss at least the salient features of this period by comparing the performance of the economy in different years.

EFFECTS OF TREND AND PERIODICITY ON THE CORRELATION DIMENSION AND THE LYAPUNOV EXPONENTS

We present a critical remark on the pitfalls of calculating the correlation dimension and the largest Lyapunov exponent from time series data when trend and periodicity exist. We consider a special case where a time series Zi can be expressed as the sum of two subsystems so that Zi = Xi + Yi and at least one of the subsystems is deterministic. We show that if the trend and periodicity are not properly removed, correlation dimension and Lyapunov exponent estimations yield misleading results, which can severely compromise the results of diagnostic tests and model identification. We also establish an analytic relationship between the largest Lyapunov exponents of the subsystems and that of the whole system. In addition, the impact of a periodic parameter perturbation on the Lyapunov exponent for the logistic map and the Lorenz system is discussed.

On testing for nonlinear dependence and chaos in financial time series data

The paper is aimed at highlighting some of the pitfalls of empirical analysis in complex dynamics. Two examples of high frequency financial time series data analysis are provided in order to investigate the characteristics of the data generating processes involved and to illustrate the difficulties encountered in numerical analyses.

Machines, Memory and the Markov Property in Stock Returns: Deus Ex Machina?

This study seeks to understand whether and to what extent High Frequency Trading (HFT) affects the probabilistic properties of the stock returns in five markets. More specifically, it focuses on the impact of HFT/machine trading on five major stock indices, DAX, Nikkei 225, S&P 500, Russell 2000, and TOPIX. The empirical analysis demonstrates that while the introduction of machine trading and/or HFT appears to make the return series more “predictable” by reducing their Multiscale Entropy, it does not affect the Markov property, which does not hold for the return series under study.

Predictability in heartbeat data

Predicting the behavior of chaotic dynamical systems is difficult in general. It is important to study such systems since the existence of chaos implies potential short term predictability. Several methods exist to analyze time series, including correlation dimension and the Brock-Dechert-Scheinkman-LeBaron (BDSL) test. Recently, a new tool, sample entropy (SampEn), has gained importance for data differentiation. We have applied these methods to cardiovascular time series data. Our findings suggest that correlation dimension is useful in analyzing such data, but not of sufficient power to discriminate between various data generating processes while sample entropy can be used as a supplementary tool.

Resource extraction, international trade and production with the resource

Abstract The paper analyses the optimal growth of a small economy that extracts an exhaustible resource both for domestic production and export purposes. It is shown that when the exogenously given resource price is constant, extraction for export will stop in finite time whereas extraction for domestic production will continue. Yet, if the resource price increases exponentially, extraction will continue to support both sectors.

Growth, resource extraction and production in a small open economy

Summary and ConclusionsThis paper analyzed the optimal growth of a resource exporting economy in the framework of a Ramsey-type model. Two versions of the same model are used. In the first version (where the aggregate production function uses the conventional inputs, namely labor and capital) it was shown that along the optimal paths the resource would be exhausted in finite time and that the economy approaches asymptotically the modified golden rule capital intensity, well known from one-sector growth theory. Subsequently the impact of the changes in resource prices on the rate of extraction are investigated by considering an exponentially rising price.In the second version of the model, the resource extracted is divided between domestic production (the aggregate output of the economy is produced by means of labor, capital and the resource input) and export. Under this assumption, it is demonstrated that when the relative price of the resource is constant and given exogenously, the opening of trade (i.e., resource exports) depends on the relative magnitudes of the marginal product of the resource and its price. Furthermore the paper showed that even if trade opens, resource extraction for export will come to an end in finite time. After the economy stops exporting the resource, its optimal growth will be determined simultaneously by the elasticity of substitution between capital and the resource input and the dynamic behavior of the marginal product of the resource input, as explained in detail by Dasgupta and Heal [1974]. Finally, when the resource price has an exponential trend, resource extraction will continue both for domestic production and export purposes.