Can nominal exchange rates be characterized by deterministic chaos? To answer this question, a statistical framework utilizing a blockwise bootstrap procedure is used to test for the presence of a positive Lyapunov exponent in an observed stochastic time series (Bask and Gencay, 1998). Daily data for the Swedish Krona against the Deutsche Mark, the ECU, the U.S. Dollar and the Yen exchange rates are examined. In most cases, the null hypothesis that the exchange rate series is not generated by a chaotic dynamical system can be rejected and these results are consistent with the hypothesis that the exchange rate series may be characterized by deterministic chaos.

A Positive Lyapunov Exponent in Swedish Exchange Rates

Scaling laws and generally self-similar structures are now well known facts in financial time series. Furthermore, these signals are characterized by the presence of stochastic behavior allowing their analysis with pure functional methods being incomplete. In the present paper, some existing models are reviewed and modified, based on wavelet theory and self-similarity, to recover multi-scaling cases for approximating financial signals. The resulting models are then tested on some empirical examples and analyzed for error estimates.

A wavelet method coupled with quasi-self-similar stochastic processes for time series approximation

This paper employs GRS test to empirically compare the applicability of five alternatives of asset pricing models for 55 shares listed on the EGX100 for the Egyptian stock market: 1) the CAPM, 2) the Fama-French three factor model, 3) the Cahart four factor model, 4) liquidity-augmented four factor model, 5) and the five factor model (liquidity and momentum-augmented Fama-French three factor model. The sample is split into six portfolios sorted on size and book-to market ratio and 45 shares are excluded due to data unavailability. Our results based on GRS (1989) show evidence that Fama-French model is the best and reject the other models.

/pdf/comparisons-of-asset-pricing-models-in-the-egyptian-stock-2ihwl2ca7n.pdf

Comparisons of Asset Pricing Models in the Egyptian Stock Market

Mainstream of the investors and investment advisory consultants suggest and focus on standard finance models and do not take into account the behavioral and neurological dimensions of finance as neurotransmitters, emotional intelligence and personality of investors. These aspects of individual investors can cause of several mistakes while investing in stock market. The primary data of 455 investors from Pakistan Stock Exchanges is used for analysis. The data analysis performed with the help of Hierarchical Latent Variable Models in PLS-SEM by using the reflective-formative type constructs as guided by Becker et al., (2012). The empirical evidence of the study reveals that personality dimensions especially openness and consciousness as well as emotional intelligence dimensions especially self emotions apraisal and regulation of emotions have significant relations with the behavioral features of investor especially investment horizon, personalization of loss and control level. Similarly, neurotransmitter’s dimensions dopamine and epinephrine have significant relation with investment decisions of indiviual investors. In view of this, emotional intelligence, neurotransmitters and personality collectively have 13.2% impact on ivestor behavior and these dimensions collectively have 4.1% impact on investment decisions of individual investor. The study opens new horizon by providing supplemented inner view of investor’s behavior and their decision’s in the stock market of Pakistan and demands more effort to determine universal latent constructs for combine model of neurofinance and behavioral finance. However, limitation of study is that it does not analyze the current model beyond the current sample size for stock market of other regions of the world.

https://www.econstor.eu/bitstream/10419/188348/1/pjcss427.pdf

Impact of Neurotransmitters, Emotional Intelligence and Personality on Investor's Behavior and Investment Decisions

Complexity analysis of spatial–temporal precipitation system by PCA and SDLE

Purpose – The purpose of this paper is to discuss a multiscale pricing model for the French stock market by combining wavelet analysis and Fama‐French three‐factor model. The objective is to examine the relationship between stock returns and Fama‐French risk factors at different time‐scales.Design/methodology/approach – Exploiting the scale separation property inherent to the maximal overlap discrete wavelet transform, the data set are decomposed into components associated with different time‐scales. This wavelet‐based decomposition scheme allows the three Fama‐French models to be tested over different investments periods.Findings – The obtained results show that the explanatory power of the Fama‐French three‐factor model becomes stronger as the wavelet scale increases. Besides, the relationship between the portfolio returns and the risk factors (i.e. the market, size and value factors) depends significantly upon the considered time‐horizon.Practical implications – The proposed methodology offers investor...

https://cedric.cnam.fr/fichiers/RC1698.pdf

Multiscale Fama‐French model: application to the French market

In this paper, a hybrid scheme for time series prediction is developed based on wavelet decomposition combined with Bayesian Least Squares Support Vector Machine regression. As a filtering step, us...

A wavelet support vector machine coupled method for time series prediction

In this paper, fractional integrating dynamics in the return and the volatility series of stock market indices are investigated. The investigation is conducted using wavelet ordinary least squares, wavelet weighted least squares and the approximate Maximum Likelihood estimator. It is shown that the long memory property in stock returns is approximately associated with emerging markets rather than developed ones while strong evidence of long range dependence is found for all volatility series. The relevance of the wavelet-based estimators, especially, the approximate Maximum Likelihood and the weighted least squares techniques is proved in terms of stability and estimation accuracy.

Wavelet estimators for long memory in stock markets

The Lyapunov exponent is an important indicator of chaotic dynamics. Using wavelet analysis, we define a multiscale representation of this exponent which we demonstrate the scale-wise dependence for functions belonging to Cx0s,s′ spaces. An empirical study involving simulated processes and financial time series corroborates the theoretical findings.

Multiscale Lyapunov exponent for 2-microlocal functions

In this paper, we study the behavior of the correlation dimension estimated using the Grassberger–Procaccia (GP) algorithm [Grassberger & Procaccia, 1983] in the wavelet domain for functions belonging to Holder space. We prove that, as the wavelet scale level tends to infinity, the GP correlation dimension estimate tends to zero. Applying this result to the trajectories of the fractional Brownian motion process and using basic properties of the wavelet transform, we show that, for the fractional Gaussian noise process (fGn), the correlation dimension estimated by the GP procedure converges to the zero value. As the fractional Gaussian noise is a stochastic process with 1/fα spectrum, -1 < α < 1, our results confirm Osborne and Provenzale's assertion that colored random noise leads to the convergence of the GP-based correlation dimension estimator. However, our result holds for a different range of the spectrum exponent values. Moreover, for the fGn class of random processes, we found no correspondence between the value of the scaling exponent H and the value of the correlation dimension estimated by the GP algorithm as the latter is simply zero.

Correlation Dimension of Fractional Gaussian Noise: New Evidence from Wavelets

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