An Econophysics Approach to Introduction Uncertainty in Dynamics of Complex Market Structural Models

An Econophysics Approach to Introduction Uncertainty in Dynamics of Complex Market Structural Models

Cem Cagri Donmez (Marmara University, Turkey)
DOI: 10.4018/978-1-5225-3767-0.ch001

Abstract

Econophysicists have begun to make progress in answering significant questions. In particular, these collaborations have the potential to change the paradigm for understanding fluctuations. New theoretical approaches to predict complex markets may be proposed, by the captivating formulation of the stock market concerning statistical correlation to be given, where some simple (non-differential, non-fractal) expressions are also suggested as general stock price formulae in a closed form that can generate a variety of possible price movements in time. A given attribute of mechanics may be submitted as a likely option to cover the price movements regarding traditional concepts where utilising stock mechanics to grow the portfolios in real markets may be realised. The ideas prove useful in risk evaluation, extreme value statistics, critical limit theorems for sums of independent variables with power law distribution, random walks, fractals, and multfractal formalisms, etc.
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Introduction

In this chapter, we will discuss financial data analysis base on Econophysical approach focus on the context as a complex market, and we consider some key features of the relationships that exist between economics and physics. Enable to understand and analyse financial systems (that means Complex Systems such as stock markets, exchange markets...etc.) and applied for Econophysical phenomena. The Complex Markets have become progressively more globalised; many buyers and sellers now increasingly have the entire world economic stage as their planning horizon. International transactions are increasingly important to companies and financial institutions. However, such internationalisation has been forced to cope with increased risk arising from deregulated financial markets. Exchange rates and interest rates are now much more volatile compared to earlier decades; they are now more like traditionally volatile outcomes such as share prices. Financial derivative products are means of avoiding or minimising this risk and uncertainty. In the multivariate systems (Complex Market), fractality is between the stochastic and deterministic approaches or stochastic rather than deterministic. Originates as multiplicative interaction results. The models of Volatility diffusion with multiple stochastic factors can generate the structure of fractality. In a few cases, such as exchange rates, the underlying structural equation will also give increase to fractality. Fractals are the common name for complex geometric shapes that have the property of self-similarity. Fractal principles can be used to develop predicting algorithms. Financial predicting Modelling exhibits high degrees of nonlinear variability and frequently has fractal properties. When the fractal dimension of a base on financial time series is non-integer, this associated with two features: (1) inhomogeneity: extreme fluctuations at irregular intervals, and (2) scaling symmetries: proportionality relationships between variations over different separation distances.

New theoretical approaches to predict complex markets may be proposed, by the captivating formulation of the stock market concerning statistical correlation to be given, where some simple (non-differential, non-fractal) expressions are also suggested as general stock price formula in a closed form that can generate a variety of possible price movements in time. A given attributes of mechanics may be submitted as a likely option to cover the price movements regarding traditional concepts where utilising stock mechanics is to grow the portfolios in real markets may be realised. The ideas that have proved useful in risk evaluation, extreme value statistics, critical limit theorems for sums of independent variables with power law distribution, random walks, fractals and multifractal formalisms, etc. We are discussed in an immediate and direct way so as to provide ready-to-use tools for analysing and representing power law behaviour in natural phenomena.

Contrariwise, fractal markets hypothesis (FMH) (Peters, 1994) has been constructed based on the most general characteristics of the markets. In its core, it builds on a notion altogether omitted in efficient markets hypothesis (EMH) liquidity. In according with FMH, liquidity provides smooth pricing process in the market, making it stable. If liquidity ceases, the market becomes unstable and extreme movements occur. In the literature, FMH usually connected with detection of fractality or multifractality of the price processes of financial assets (Onali et al., 2011). However, it has not put to the test on its predictions about causes and implications of critical events in the financial markets. Currently under development of microscopic models of financial markets is to reproduce the observed statistical features of market movements.

This chapter divided into 11 main sections. The second part Conceptual Literature Review, The next part dedicates to the methodological interaction between The Fields of Physics, Finance and Economy; in the third focuses on the meaning of Econophysics with Perspective of Complex Market Variability; Section four deals with the Existing Financial Variability Structure, particularly regarding the five section the Indeterminateness of Chaotic Structures; following section is Micro Structure of Markets; subsequent to section seven Random Walk Model; afterwards section eight Heterogeneous and Fractal Market Hypotheses; in the ninth focuses on The Relationship Between Chaos & Physics; the conclusion follow.

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