Epaminondas Panas

Are oil markets chaotic? A non-linear dynamic analysis

Abstract The analysis of products’ price behaviour continues to be an important empirical issue. This study contributes to the current literature on price dynamics of products by examining for the presence of chaos and non-linear dynamics in daily oil products for the Rotterdam and Mediterranean petroleum markets. Previous studies using only one invariant, such as the correlation dimension may not effectively determine the chaotic structure of the underlying time series. To obtain better information on the time series structure, a framework is developed, where both invariant and non-invariant quantities were also examined. In this paper various invariants for detecting a chaotic time series were analysed along with the associated Brock’s theorem and Eckman–Ruelle condition, to return series for the prices of oil products. An additional non-invariant quantity, the BDS statistic, was also examined. The correlation dimension, entropies and Lyapunov exponents show strong evidence of chaos in a number of oil products considered.

Estimating fractal dimension using stable distributions and exploring long memory through ARFIMA models in Athens Stock Exchange

It is argued that the study of the correct specification of returns distributions has attractive implications in financial economics. This study estimates Levy-stable (fractal) distributions that can accurately account for skewness, kurtosis, and fat tails. The Levy-stable family distributions are parametrized by the Levy index (α), 0 < (α), ≤ 2, and include the normal distribution as a special case (α = 2). The Levy index, α, is the fractal dimension of the probability space. The unique feature of Levy-stable family distributions is the existence of a relationship between the fractal dimension of the probability space andthe fractal dimensionof the time series. This relationshipis simply expressed in terms of Hurst exponent (H), i.e. α = 1/ H. In addition, Hurst exponent is related to long-memory effects. Thus, estimating the Levy index allows us to determine long-memory effects. The immediate practical implication of the present work is that on the one hand we estimate the shape of returns distributions and on the other hand we investigate the fractal dimensions. Overall, then, the Levy-stable family distributions methodology appears to be useful for analysing the returns distribution, for understanding the fractal dimension of returns and for providing the researcher with direct insights into the long-memory effects of stock returns. A second approach to test the long memory hypothesis is attempted in this paper. This test involves an estimation of the ARFIMA models. A comparative analysis of the two approaches indicates the existence of long-memory in the Athens Stock Exchange. The results of this study are based on a sample of stocks from the Athens Stock Exchange using daily data.

The capital--energy controversy: further results

Abstract The significance of capital-energy (K-E) relationship in manufacturing is important. However, the available econometric evidence is contradictory, in the sense that support can be found for both K-E substitutability and K-E complementarity. Moreover, the empirical studies are based mainly on production and cost functions. Few researchers have examined the K-E relationship by assuming a profit function approach. The objective of this paper is to investigate the K-E controversy by estimating both a cost and a profit function. The results should provide a better understanding of the relationship between model used and estimated K-E elasticities. In addition, the two models are compared on the basis of their predictive performance.

Is there an inflation-productivity trade-off? Some evidence from the manufacturing sector in Greece

This paper examines the effect of inflation on total factor productivity growth (TFP), using time-series data for every two digit Greek manufacturing industries. In order to do the above a translog flexible cost function is estimated and used to decompose TFP growth into scale economies, inflation and technical change. The advantage of estimating a very general and flexible cost function is that it allows us, for the first time, to examine empirically a large number of significant relationships between TFP growth and economies of scale, inflation and technical change. The main conclusion drawn from this analysis is that inflation reduces TFP growth in a way, which is sizeable. Furthermore, using standard causality test the direction of causality between inflation and TFP growth was tested at the manufacturing level.

Generalized beta distributions for describing and analysing intraday stock market data: testing the U-shape pattern

There is an impressive body of empirical evidence which indicates the existence of an intraday U-shaped curve in stock prices. In an effort to shed additional light on the U-shaped curve a new procedure for U-shape testing is introduced. From careful analysis of intraday data it is observed that minimum or maximum stock prices can occur several times during the day. Here, attention is focused on the first time during the day that the maximum or minimum stock price occurred. Because of the importance of the first time during the day that the maximum or minimum stock price occurred, an attempt is made to model these two characteristics with probability distributions. The objective of this study is to use a generalized beta distribution to examine the intradaily behaviour of stocks, using closing stock prices for each one-minute interval, using data from Athens Stock Exchange (ASE). This generalized beta distribution has not been used before to model U-shaped behaviour. The results are consistent with the intraday U-shaped curves, i.e. the time to first maximum (or minimum) stock prices follows a U-shaped pattern. In addition, some potential applications of the generalized beta distribution are discussed and exemplified by analysing the relationship between herd behaviour and U-shaped.

Rolling-sampled parameters of ARCH and Levy-stable models

In this article an asymmetric autoregressive conditional heteroskedasticity (ARCH) model is applied to some well-known financial indices (DAX30, FTSE20, FTSE100 and SP500), using a rolling sample of constant size, in order to investigate whether the values of the estimated parameters of the model change over time. Although, there are changes in the estimated parameters reflecting that structural properties and trading behaviour alter over time, the ARCH model adequately forecasts the one-day-ahead volatility. A simulation study has been carried out to investigate whether the time-variant attitude holds in the case of a generated ARCH data process revealing that either in that case the rolling-sampled parameters are time varying. The rolling analysis is also applied to estimate the parameters of a Levy-stable distribution. The empirical findings support that the stable parameters are also time variant.

Empirical distributions of stock returns: Paris stock market, 1980–2003

The accurate specification of returns distributions has important implications in financial economics. A common practice in financial econometrics is to assume that the logarithms of stock returns are independent and identically distributed and follow a Normal distribution. However, daily stock returns display significant departures from Normality, having fatter tails and more peakedness. This study presents an alternative class of distributions, Levy-stable distributions, which can account for the observed skewness, kurtosis and fat tails, considering a sample of daily returns for nine stocks in Paris Market. Moreover, estimating the Levy-index allows us to determine long-memory behaviour of stock returns. Additionally, this study also tests long-memory hypothesis through an estimation of ARFIMA models. A comparative analysis of both approaches suggests the existence of long-memory in Paris Stock Exchange. The implication of the present work is that Levy-stable distributions are used to better approximate returns distributions and also to explore long-memory effects of stock returns.

A weak form evaluation of the efficiency of the Rotterdam and Italian oil spot markets

Abstract We examine whether the efficient market hypothesis can be applied to the Rotterdam and Italian oil markets, by analysing the log price changes observed in those markets for monthly data during the period January 1980 to December 1987. The objective is to investigate the leptokurticity in observed distributions of low price changes. The stable Paretian distributions have been used to test the leptokurtic distributions.